diff --git a/.gitignore b/.gitignore
index af575bd0c5a43d5b39ef3da13f3dc798ebe1704d..1278fe7eab77d49c0340f2ff492c77c8121364cf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@ node_modules
 dist
 package-lock.json
 src/res/raw
-src/plugin_examples/*/
\ No newline at end of file
+src/plugin_examples/*/
+.vscode
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..a20f2d36324fee46ce3a90f029ba9361839ab550
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,38 @@
+FROM node:8 as builder
+
+ARG BACKEND_URL
+ENV BACKEND_URL=$BACKEND_URL
+
+COPY . /iv
+WORKDIR /iv
+
+ENV VERSION=devNext
+
+RUN npm i
+RUN npm run build-aot
+
+
+# prod container
+FROM node:8-alpine 
+
+ARG PORT
+ENV PORT=$PORT
+ENV NODE_ENV=production
+
+RUN apk --no-cache add ca-certificates
+RUN mkdir /iv-app
+WORKDIR /iv-app
+
+# Copy built interactive viewer
+COPY --from=builder /iv/dist/aot ./public
+
+# Copy the express server
+COPY --from=builder /iv/deploy .
+
+# Copy the resources files needed to respond to queries
+COPY --from=builder /iv/src/res/ext ./res
+RUN npm i
+
+EXPOSE $PORT
+
+ENTRYPOINT [ "node", "server.js" ]
\ No newline at end of file
diff --git a/README.md b/README.md
index 67b1255496cfbfd52a02dfffdf60197045fa09e1..3481157abf73562adf36db5ae3eb836009a7c064 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ Interactive Atlas Viewer is an frontend module wrapping around [nehuba](https://
 A live version of the Interactive Atlas Viewer is available at [https://kg.humanbrainproject.org/viewer/](https://kg.humanbrainproject.org/viewer/). This section is useful for developers who would like to develop this project.
 
 ### General information
-Interactive atlas viewer is built with [Angular (v6.0)](https://angular.io/) and [Bootstrap (v3.3.4)](http://getbootstrap.com/docs/3.3/). Some other notable packages used are: [ng2-charts](https://valor-software.com/ng2-charts/) for charts visualisation, [ngx-bootstrap (v 2.0.5)](https://valor-software.com/ngx-bootstrap/old/2.0.5/) for UI and [ngrx/platform](https://github.com/ngrx/platform) for state management. 
+Interactive atlas viewer is built with [Angular (v6.0)](https://angular.io/), [Bootstrap (v4)](http://getbootstrap.com/), and [fontawesome icons](https://fontawesome.com/). Some other notable packages used are: [ng2-charts](https://valor-software.com/ng2-charts/) for charts visualisation, [ngx-bootstrap](https://valor-software.com/ngx-bootstrap/) for UI and [ngrx/store](https://github.com/ngrx/platform) for state management. 
 
 ### Prerequisites
 
diff --git a/URL_STATE.md b/URL_STATE.md
new file mode 100644
index 0000000000000000000000000000000000000000..556d1ebcdbc387683840151884813301b00ba6a9
--- /dev/null
+++ b/URL_STATE.md
@@ -0,0 +1,119 @@
+
+# URL State  
+
+
+Interactive Atlas Viewer could be run with already selected state. It is possible to create or save application URL so, that it will contain specific data which will usefull to run application with already defined state. In URL, this specific data, is saved as URL query parameters.
+  
+## URL query parameters in Interactive Atlas Viewer  
+  
+  
+URL query parameters are variables which are located after URL and separated from the URL with "?" mark. URL query parameter variable contains variable name and value. Url query parameters are divided by "&" symbol. it is not possible to use whitespaces in the URL query. In Interactive Atlas Viewer, URL query parameters are used to save or create an application state. There are 6 main parameters which are used to save the application state. They are: navigation, niftiLayers, parcellationSelected, pluginState, regionsSelected and templateSelected.  
+  
+Example of already filled URL looks like this:  
+  
+```  
+  
+https://dev-next-interactive-viewer.apps-dev.hbp.eu/?templateSelected=MNI+152+ICBM+2009c+Nonlinear+Asymmetric&parcellationSelected=Fibre+Bundle+Atlas+-+Long+Bundle&navigation=0_0_0_1__0.11092895269393921_0.8756000399589539_-0.44895267486572266_-0.13950254023075104__2500000__1430285_13076858_4495181__204326.41684726204&regionsSelected=31_51_32  
+  
+```  
+
+4 parameters are mentioned inside the example URL:    
+
+- templateSelected  
+- parcellationSelected  
+- navigation  
+- regionsSelected  
+    
+
+### Template Selected - parameter  
+
+Template Selected parameter, is used to or __select the template__ with using URL. From the given example we can see the parameter:  
+  
+```  
+templateSelected=MNI+152+ICBM+2009c+Nonlinear+Asymmetric  
+```  
+  
+In parameter value, whitespaces should be replaced with "+" symbol. So, actually, the selected template name is:  
+  
+```  
+MNI 152 ICBM 2009c Nonlinear Asymmetric  
+```  
+
+To select a template from URL it is not required to have any other parameter. "Parcellation Selected" and "Navigation" parameters will be automatically added by default values. So, before the application automatically adds parameters, the URL will look like this:  
+  
+```  
+https://dev-next-interactive-viewer.apps-dev.hbp.eu/?templateSelected=MNI+152+ICBM+2009c+Nonlinear+Asymmetric  
+```  
+  
+### Parcellation Selected - parameter  
+
+Given parameter is used to select parcellation on the template. As in template selection parameter value, whitespaces are changed with "+" symbol. To use Parcellation parameter, selection template should be in URL parameters too. Navigation parameter will be automatically added by default values. So, before the application automatically adds parameters, the URL will look like this:  
+  
+```  
+https://dev-next-interactive-viewer.apps-dev.hbp.eu/?templateSelected=MNI+152+ICBM+2009c+Nonlinear+Asymmetric&parcellationSelected=Fibre+Bundle+Atlas+-+Long+Bundle  
+```  
+  
+From this example selected parcellation name is  
+  
+
+```  
+Fibre Bundle Atlas - Long Bundle  
+```  
+  
+### Navigation - parameter  
+  
+Navigation parameter is to determine orientation, position and zoom of 2d and 3d parts of the selected template. It does not depend on selected parcellation, but it depends on the selected template.  
+Navigation parameter itself includes 5 fields - orientation, perspectiveOrientation, perspectiveZoom, position and zoom. They are sorted appropriate and divided by "__" symbol.  
+This is the navigation parameters part from the example  
+  
+```  
+navigation=0_0_0_1__0.11092895269393921_0.8756000399589539_-0.44895267486572266_-0.13950254023075104__2500000__1430285_13076858_4495181__204326.41684726204  
+```  
+  
+**orientation** - the field is used to determine an orientation of the 2D part in atlas viewer. The field contains with 4 numbers, divided by "_" symbol. 3 of these numbers should have value "0" and one should have value "1". Value 1 will determine the orientation of the 2D image. In the example orientation is a first element - "0_0_0_1".  
+**perspectiveOrientation** - the field is used to determine an orientation of the 3D part in atlas viewer. The field contains with 4 numbers, divided by "_" symbol. In the example perspective orientation is the second element  
+  
+```  
+0.11092895269393921_0.8756000399589539_-0.44895267486572266_-0.13950254023075104"  
+```  
+  
+**perspectiveZoom** - the field is used to determine zoom in 3D pard in atlas viewer. It is the single number and is the third element - "2500000"  
+**position** - the field is used to determine the position of the dot where the camera is oriented in both 2D and 3D parts of atlas viewer. Field contains with 3 numbers, divided by "_" symbol - "1430285_13076858_4495181".  
+**zoom** - the field is used to determine zoom in 2D pard in atlas viewer. It is the single number and is the last element - "204326.41684726204"  
+    
+
+### Regions Selected - parameter  
+  
+Every region of selected parcellation has its own integer number as id. To select the region from URL, with templateSelected and parcellationSelected parameters, URL should contain the regionSelected parameter. In this parameter. In the given example, we can see the region parameter part.  
+  
+```  
+regionsSelected=31_51_32  
+``` 
+
+in parameter, different regions are divided with "_" symbol. It meant that in the given example, there are 3 selected parameters - 31, 51 and 32.  
+The region belongs to parcellation, so user can not add regionsSelected without templateSelected and parcellationSelected parameters. Navigation parameter will be automatically added by default values.  
+  
+### Nifti Layers - parameter  
+
+Nifti Layers parameter adds to the application URL, when the user selects the nifti layer of selected regions. The value of this parameter is text. Especially, it is a link where the nifti file is located.  
+Example  
+
+```  
+https://dev-next-interactive-viewer.apps-dev.hbp.eu/?niftiLayers=https%3A%2F%2Fneuroglancer.humanbrainproject.org%2Fprecomputed%2FJuBrain%2Fv2.2c%2FPMaps%2FIPL_PF.nii&templateSelected=MNI+Colin+27&parcellationSelected=JuBrain+Cytoarchitectonic+Atlas  
+```  
+
+You see, that value of parameter "niftiLayers" is  
+
+```  
+https%3A%2F%2Fneuroglancer.humanbrainproject.org%2Fprecomputed%2FJuBrain%2Fv2.2c%2FPMaps%2FIPL_PF.nii  
+```  
+
+This is the encoded text for URL where %3A is for ":" and %2F is for "/". So, nifti file's URL is  
+
+```  
+https://Fneuroglancer.humanbrainproject.org/Fprecomputed/JuBrain/v2.2c/PMaps/IPL_PF.nii  
+```  
+  
+## Contributing  
+  
+Feel free to raise an issue in this repo and/or file a PR.
diff --git a/deploy/.gitignore b/deploy/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..20d4182a3bef0d148bce08fdadaf3999dddeeede
--- /dev/null
+++ b/deploy/.gitignore
@@ -0,0 +1,3 @@
+.env
+res/*
+raw
diff --git a/deploy/app.js b/deploy/app.js
new file mode 100644
index 0000000000000000000000000000000000000000..7f77c725f5eaed6f71cc70dbc79466ebf24e61e0
--- /dev/null
+++ b/deploy/app.js
@@ -0,0 +1,67 @@
+const path = require('path')
+const express = require('express')
+const app = express()
+const session = require('express-session')
+const MemoryStore = require('memorystore')(session)
+
+app.disable('x-powered-by')
+
+if (process.env.NODE_ENV !== 'production') {
+  app.use(require('cors')())
+}
+
+/**
+ * load env first, then load other modules
+ */
+
+const configureAuth = require('./auth')
+
+const store = new MemoryStore({
+  checkPeriod: 86400000
+})
+
+const SESSIONSECRET = process.env.SESSIONSECRET || 'this is not really a random session secret'
+
+/**
+ * passport application of oidc requires session
+ */
+app.use(session({
+  secret: SESSIONSECRET,
+  resave: true,
+  saveUninitialized: false,
+  store
+}))
+
+/**
+ * configure Auth
+ * async function, but can start server without
+ */
+configureAuth(app)
+  .then(() => console.log('configure auth properly'))
+  .catch(e => console.error('configure auth failed', e))
+
+const PUBLIC_PATH = process.env.NODE_ENV === 'production'
+  ? path.join(__dirname, 'public')
+  : path.join(__dirname, '..', 'dist', 'aot')
+
+app.use(express.static(PUBLIC_PATH))
+
+app.use((req, res, next) => {
+  res.set('Content-Type', 'application/json')
+  next()
+})
+
+const templateRouter = require('./templates')
+const nehubaConfigRouter = require('./nehubaConfig')
+const datasetRouter = require('./datasets')
+const pluginRouter = require('./plugins')
+
+app.use('/templates', templateRouter)
+app.use('/nehubaConfig', nehubaConfigRouter)
+app.use('/datasets', datasetRouter)
+app.use('/plugins', pluginRouter)
+
+const catchError = require('./catchError')
+app.use(catchError)
+
+module.exports = app
\ No newline at end of file
diff --git a/deploy/auth/hbp-oidc.js b/deploy/auth/hbp-oidc.js
new file mode 100644
index 0000000000000000000000000000000000000000..cbb7ce83f0d34de5814048a3e2986a660640f40c
--- /dev/null
+++ b/deploy/auth/hbp-oidc.js
@@ -0,0 +1,39 @@
+const passport = require('passport')
+const { configureAuth } = require('./oidc')
+
+const HOSTNAME = process.env.HOSTNAME || 'http://localhost:3000'
+const clientId = process.env.HBP_CLIENTID || 'no hbp id'
+const clientSecret = process.env.HBP_CLIENTSECRET || 'no hbp client secret'
+const discoveryUrl = 'https://services.humanbrainproject.eu/oidc'
+const redirectUri = `${HOSTNAME}/hbp-oidc/cb`
+const cb = (tokenset, {sub, given_name, family_name, ...rest}, done) => {
+  return done(null, {
+    id: `hbp-oidc:${sub}`,
+    name: `${given_name} ${family_name}`,
+    type: `hbp-oidc`,
+    tokenset,
+    rest
+  })
+}
+
+module.exports = async (app) => {
+  const { oidcStrategy } = await configureAuth({
+    clientId,
+    clientSecret,
+    discoveryUrl,
+    redirectUri,
+    cb,
+    scope: 'openid offline_access',
+    clientConfig: {
+      redirect_uris: [ redirectUri ],
+      response_types: [ 'code' ]
+    }
+  })
+  
+  passport.use('hbp-oidc', oidcStrategy)
+  app.get('/hbp-oidc/auth', passport.authenticate('hbp-oidc'))
+  app.get('/hbp-oidc/cb', passport.authenticate('hbp-oidc', {
+    successRedirect: '/',
+    failureRedirect: '/'
+  }))
+}
diff --git a/deploy/auth/index.js b/deploy/auth/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..bfd8fab1724d4a1f052734684b2440baa1374e79
--- /dev/null
+++ b/deploy/auth/index.js
@@ -0,0 +1,39 @@
+const hbpOidc = require('./hbp-oidc')
+const passport = require('passport')
+const objStoreDb = new Map()
+
+module.exports = async (app) => {
+  app.use(passport.initialize())
+  app.use(passport.session())
+
+  passport.serializeUser((user, done) => {
+    const { tokenset, rest } = user
+    objStoreDb.set(user.id, user)
+    done(null, user.id)
+  })
+
+  passport.deserializeUser((id, done) => {
+    const user = objStoreDb.get(id)
+    if (user) 
+      return done(null, user)
+    else
+      return done(null, false)
+  })
+
+  await hbpOidc(app)
+
+  app.get('/user', (req, res) => {
+    if (req.user) {
+      return res.status(200).send(JSON.stringify(req.user))
+    } else {
+      return res.sendStatus(401)
+    }
+  })
+
+  app.get('/logout', (req, res) => {
+    if (req.user && req.user.id)
+      objStoreDb.delete(req.user.id)
+    req.logout()
+    res.redirect('/')
+  })
+}
\ No newline at end of file
diff --git a/deploy/auth/oidc.js b/deploy/auth/oidc.js
new file mode 100644
index 0000000000000000000000000000000000000000..da3a0719d3984367fb3057d4aeac424b7b9b03c7
--- /dev/null
+++ b/deploy/auth/oidc.js
@@ -0,0 +1,43 @@
+const { Issuer, Strategy } = require('openid-client')
+
+const defaultCb = (tokenset, {id, ...rest}, done) => {
+  return done(null, {
+    id: id || Date.now(),
+    ...rest
+  })
+}
+
+exports.configureAuth = async ({ discoveryUrl, clientId, clientSecret, redirectUri, clientConfig = {}, cb = defaultCb, scope = 'openid' }) => {
+  if (!discoveryUrl)
+    throw new Error('discoveryUrl must be defined!')
+
+  if (!clientId)
+    throw new Error('clientId must be defined!')
+
+  if (!clientSecret)
+    throw new Error('clientSecret must be defined!')
+
+  if (!redirectUri)
+    throw new Error('redirectUri must be defined!')
+
+  const issuer = await Issuer.discover(discoveryUrl)
+
+  const client = new issuer.Client({
+    client_id: clientId,
+    client_secret: clientSecret,
+    ...clientConfig
+  })
+
+  const oidcStrategy = new Strategy({
+    client,
+    params: {
+      scope
+    },
+  }, cb)
+
+  return {
+    oidcStrategy,
+    issuer,
+    client
+  }
+}
\ No newline at end of file
diff --git a/deploy/auth/util.js b/deploy/auth/util.js
new file mode 100644
index 0000000000000000000000000000000000000000..2f225bc836621bb840f79e1c396da6f39fc69f24
--- /dev/null
+++ b/deploy/auth/util.js
@@ -0,0 +1,66 @@
+const { configureAuth } = require('./oidc')
+const jwtDecode = require('jwt-decode')
+
+const HOSTNAME = process.env.HOSTNAME || 'http://localhost:3000'
+const clientId = process.env.HBP_CLIENTID || 'no hbp id'
+const clientSecret = process.env.HBP_CLIENTSECRET || 'no hbp client secret'
+const discoveryUrl = 'https://services.humanbrainproject.eu/oidc'
+const redirectUri = `${HOSTNAME}/hbp-oidc/cb`
+
+let REFRESH_TOKEN = process.env.REFRESH_TOKEN || null
+const CLIENT_NOT_INIT = `Client is not initialised.`
+const REFRESH_TOKEN_MISSING = `refresh token is missing`
+
+let __client
+let __publicAccessToken
+
+const refreshToken = async () => {
+  if (!__client)
+    throw new Error(CLIENT_NOT_INIT)
+  if (!REFRESH_TOKEN)
+    throw new Error(REFRESH_TOKEN_MISSING)
+  const tokenset = await __client.refresh(REFRESH_TOKEN)
+  const {access_token: accessToken, refresh_token: refreshToken, id_token: idToken} = tokenset
+  if (refreshToken !== REFRESH_TOKEN) {
+    REFRESH_TOKEN = refreshToken
+  }
+  __publicAccessToken = accessToken
+  return true
+}
+
+const getPublicAccessToken = async () => {
+  if (!__client)
+    throw new Error(CLIENT_NOT_INIT)
+  
+  if (!__publicAccessToken) {
+    await refreshToken()
+  }
+
+  const decoded = jwtDecode(__publicAccessToken)
+  const { exp } = decoded
+  if (!exp || isNaN(exp) || (exp * 1000 - Date.now() < 0)) {
+    await refreshToken()
+  }
+  
+  return __publicAccessToken
+}
+
+module.exports = async () => {
+
+  const { client } = await configureAuth({
+    clientId,
+    clientSecret,
+    discoveryUrl,
+    redirectUri,
+    clientConfig: {
+      redirect_uris: [ redirectUri ],
+      response_types: [ 'code' ]
+    }
+  })
+
+  __client = client
+
+  return {
+    getPublicAccessToken: async () => await getPublicAccessToken()
+  }
+}
\ No newline at end of file
diff --git a/deploy/auth/util.spec.js b/deploy/auth/util.spec.js
new file mode 100644
index 0000000000000000000000000000000000000000..7d05de88400a702ebfc7342e233cb4e07980037e
--- /dev/null
+++ b/deploy/auth/util.spec.js
@@ -0,0 +1,34 @@
+const mocha = require('mocha')
+const chai = require('chai')
+const assert = chai.assert
+const chaiAsPromised = require('chai-as-promised')
+chai.use(chaiAsPromised)
+const expect = chai.expect
+const should = chai.should()
+
+describe('mocha.js', () => {
+  it('mocha works properly', () => {
+    assert(true)
+  })
+})
+
+describe('chai-as-promised.js', () => {
+  it('resolving promise is resolved', () => {
+
+    return Promise.resolve(2 + 2).should.eventually.equal(4)
+    
+  })
+  it('rejecting promise is rejected', () => {
+    return Promise.reject('no reason').should.be.rejected
+  })
+})
+
+describe('util.js with env', (done) => {
+
+  it('when client id and client secret and refresh token is set, util should not throw', async () => {
+    
+    const util = require('./util')
+    const { getPublicAccessToken } = await util()
+    return getPublicAccessToken().should.be.fulfilled
+  })
+})
\ No newline at end of file
diff --git a/deploy/auth/util_noenv.spec.js b/deploy/auth/util_noenv.spec.js
new file mode 100644
index 0000000000000000000000000000000000000000..769d926bbfeb06ce7180ca42555fc4b19e572e19
--- /dev/null
+++ b/deploy/auth/util_noenv.spec.js
@@ -0,0 +1,40 @@
+const mocha = require('mocha')
+const chai = require('chai')
+const assert = chai.assert
+const chaiAsPromised = require('chai-as-promised')
+chai.use(chaiAsPromised)
+const expect = chai.expect
+const should = chai.should()
+
+
+describe('mocha.js', () => {
+  it('mocha works properly', () => {
+    assert(true)
+  })
+})
+
+describe('chai-as-promised.js', () => {
+  it('resolving promise is resolved', () => {
+
+    return Promise.resolve(2 + 2).should.eventually.equal(4)
+    
+  })
+  it('rejecting promise is rejected', () => {
+    return Promise.reject('no reason').should.be.rejected
+  })
+})
+
+describe('util.js without env', (done) => {
+
+  const util = require('./util')
+  it('even when no env is set, it should fulfill', () => {
+    const getUtil = util()
+    return getUtil.should.be.fulfilled
+  })
+
+  it('if env is not set, getPublicToken method should fail', async () => {
+    const utilObj = await util()
+    const { getPublicAccessToken } = utilObj
+    return getPublicAccessToken().should.be.rejected
+  })
+})
\ No newline at end of file
diff --git a/deploy/catchError.js b/deploy/catchError.js
new file mode 100644
index 0000000000000000000000000000000000000000..0fdc484348a9e6f8e1f1fc9072eaec5b8de5e1d9
--- /dev/null
+++ b/deploy/catchError.js
@@ -0,0 +1,11 @@
+module.exports = ({code = 500, error = 'an error had occured', trace = 'undefined trace'}, req, res, next) => {
+  /**
+   * probably use more elaborate logging?
+   */
+  console.log('Catching error', {
+    code,
+    error,
+    trace
+  })
+  res.status(code).send()
+}
\ No newline at end of file
diff --git a/deploy/datasets/index.js b/deploy/datasets/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..0eb0b18e955f4d6e77c25061caae26a04bde6a79
--- /dev/null
+++ b/deploy/datasets/index.js
@@ -0,0 +1,98 @@
+const express = require('express')
+const path = require('path')
+const fs = require('fs')
+const datasetsRouter = express.Router()
+const { init, getDatasets, getPreview } = require('./query')
+
+init().catch(e => {
+  console.warn(`dataset init failed`, e)
+})
+
+datasetsRouter.use((req, res, next) => {
+  res.setHeader('Cache-Control', 'no-cache')
+  next()
+})
+
+datasetsRouter.use('/spatialSearch', require('./spatialRouter'))
+
+datasetsRouter.get('/templateName/:templateName', (req, res, next) => {
+  const { templateName } = req.params
+  const { user } = req
+  getDatasets({ templateName, user })
+    .then(ds => {
+      res.status(200).json(ds)
+    })
+    .catch(error => {
+      next({
+        code: 500,
+        error,
+        trace: 'templateName'
+      })
+    })
+})
+
+datasetsRouter.get('/parcellationName/:parcellationName', (req, res, next) => {
+  const { parcellationName } = req.params
+  const { user } = req
+  getDatasets({ parcellationName, user })
+    .then(ds => {
+      res.status(200).json(ds)
+    })
+    .catch(error => {
+      next({
+        code: 500,
+        error,
+        trace: 'parcellationName'
+      })
+    })
+})
+
+datasetsRouter.get('/preview/:datasetName', (req, res, next) => {
+  const { datasetName } = req.params
+  getPreview({ datasetName })
+    .then(preview => {
+      if (preview) {
+        res.status(200).json(preview)
+      } else {
+        next({
+          code: 404,
+          trace: 'preview'
+        })
+      }
+    })
+    .catch(error => {
+      next({
+        code: 500,
+        error,
+        trace: 'preview'
+      })
+    })
+})
+
+const previewFileMap = new Map()
+
+const PUBLIC_PATH = process.env.NODE_ENV === 'production'
+  ? path.join(__dirname, '..', 'public')
+  : path.join(__dirname, '..', '..', 'dist', 'aot')
+
+
+const RECEPTOR_PATH = path.join(PUBLIC_PATH, 'res', 'image')
+fs.readdir(RECEPTOR_PATH, (err, files) => {
+  if (err) {
+    console.log('reading receptor error', err)
+    return
+  }
+  files.forEach(file => previewFileMap.set(`res/image/receptor/${file}`, path.join(RECEPTOR_PATH, file)))
+})
+
+datasetsRouter.get('/previewFile', (req, res) => {
+  const { file } = req.query
+  const filePath = previewFileMap.get(file)
+  if (filePath) {
+    fs.createReadStream(filePath).pipe(res)
+  } else {
+    res.status(404).send()
+  }
+})
+
+module.exports = datasetsRouter
\ No newline at end of file
diff --git a/deploy/datasets/query.js b/deploy/datasets/query.js
new file mode 100644
index 0000000000000000000000000000000000000000..d35fd789deef6c8d57478671fe0cdca069f9e8da
--- /dev/null
+++ b/deploy/datasets/query.js
@@ -0,0 +1,223 @@
+const fs = require('fs')
+const request = require('request')
+const path = require('path')
+const { commonSenseDsFilter } = require('./supplements/commonSense')
+const { getPreviewFile, hasPreview } = require('./supplements/previewFile')
+const { manualFilter: manualFilterDWM, manualMap: manualMapDWM } = require('./supplements/util/mapDwm')
+
+const kgQueryUtil = require('./../auth/util')
+
+let cachedData = null
+let otherQueryResult = null
+const queryUrl = process.env.KG_DATASET_QUERY_URL || `https://kg.humanbrainproject.org/query/minds/core/dataset/v1.0.0/interactiveViewerKgQuery/instances?size=450&vocab=https%3A%2F%2Fschema.hbp.eu%2FmyQuery%2F`
+const timeout = process.env.TIMEOUT || 5000
+const STORAGE_PATH = process.env.STORAGE_PATH || path.join(__dirname, 'data')
+
+let getPublicAccessToken
+
+const fetchDatasetFromKg = async (arg) => {
+
+  const accessToken = arg && arg.user && arg.user.tokenset && arg.user.tokenset.access_token
+  const releasedOnly = !accessToken
+  let publicAccessToken
+  if (!accessToken && getPublicAccessToken) {
+    publicAccessToken = await getPublicAccessToken()
+  }
+  const option = accessToken || publicAccessToken || process.env.ACCESS_TOKEN
+    ? {
+        auth: {
+          'bearer': accessToken || publicAccessToken || process.env.ACCESS_TOKEN
+        }
+      }
+    : {}
+  return await new Promise((resolve, reject) => {
+    request(`${queryUrl}${releasedOnly ? '&databaseScope=RELEASED' : ''}`, option, (err, resp, body) => {
+      if (err)
+        return reject(err)
+      if (resp.statusCode >= 400)
+        return reject(resp.statusCode)
+      const json = JSON.parse(body)
+      return resolve(json)
+    })
+  })
+}
+
+
+const cacheData = ({results, ...rest}) => {
+  cachedData = results
+  otherQueryResult = rest
+  return cachedData
+}
+
+const getPublicDs = () => Promise.race([
+  new Promise((rs, rj) => {
+    setTimeout(() => {
+      if (cachedData) {
+        rs(cachedData)
+      } else {
+        /**
+         * cached data not available, have to wait
+         */
+      }
+    }, timeout)
+  }),
+  fetchDatasetFromKg().then(cacheData)
+])
+
+
+const getDs = ({ user }) => user
+  ? fetchDatasetFromKg({ user }).then(({results}) => results)
+  : getPublicDs()
+
+/**
+ * Needed by filter by parcellation
+ */
+
+const flattenArray = (array) => {
+  return array.concat(
+    ...array.map(item => item.children && item.children instanceof Array
+      ? flattenArray(item.children)
+      : [])
+  )
+}
+
+const readConfigFile = (filename) => new Promise((resolve, reject) => {
+  let filepath
+  if (process.env.NODE_ENV === 'production') {
+    filepath = path.join(__dirname, '..', 'res', filename)
+  } else {
+    filepath = path.join(__dirname, '..', '..', 'src', 'res', 'ext', filename)
+  }
+  fs.readFile(filepath, 'utf-8', (err, data) => {
+    if(err) reject(err)
+    resolve(data)
+  })
+})
+
+let juBrain = null
+let shortBundle = null
+let longBundle = null
+let waxholm = null
+let allen = null
+
+readConfigFile('colin.json')
+  .then(data => JSON.parse(data))
+  .then(json => {
+    juBrain = flattenArray(json.parcellations[0].regions)
+  })
+  .catch(console.error)
+
+readConfigFile('MNI152.json')
+  .then(data => JSON.parse(data))
+  .then(json => {
+    longBundle = flattenArray(json.parcellations[0].regions)
+    shortBundle = flattenArray(json.parcellations[1].regions)
+  })
+  .catch(console.error)
+
+readConfigFile('waxholmRatV2_0.json')
+  .then(data => JSON.parse(data))
+  .then(json => {
+    waxholm = flattenArray(json.parcellations[0].regions)
+  })
+  .catch(console.error)
+
+/**
+ * deprecated
+ */
+const filterByPRs = (prs, atlasPr) => atlasPr
+  ? prs.some(pr => {
+      const regex = new RegExp(pr.name.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&'), 'i')
+      return atlasPr.some(aPr => regex.test(aPr.name))
+    })
+  : false
+
+const manualFilter = require('./supplements/parcellation')
+
+
+const filter = (datasets = [], {templateName, parcellationName}) => datasets
+  .filter(ds => commonSenseDsFilter({ds, templateName, parcellationName }))
+  .filter(ds => {
+    if (/infant/.test(ds.name))
+      return false
+    if (templateName) {
+      return ds.referenceSpaces.some(rs => rs.name === templateName)
+    }
+    if (parcellationName) {
+      if (parcellationName === 'Fibre Bundle Atlas - Long Bundle'){
+        return manualFilterDWM(ds)
+      }
+      return ds.parcellationRegion.length > 0
+        ? filterByPRs(
+            ds.parcellationRegion, 
+            parcellationName === 'JuBrain Cytoarchitectonic Atlas' && juBrain
+              ? juBrain
+              : parcellationName === 'Fibre Bundle Atlas - Short Bundle' && shortBundle
+                ? shortBundle
+                : parcellationName === 'Waxholm Space rat brain atlas v.2.0'
+                  ? waxholm
+                  : null
+          )
+        : false
+    }
+
+    return false
+  })
+  .map(ds => {
+    if (parcellationName && parcellationName === 'Fibre Bundle Atlas - Long Bundle') {
+      return manualMapDWM(ds)
+    }
+    return {
+      ...ds,
+      ...parcellationName && ds.parcellationRegion.length === 0
+        ? { parcellationRegion: [{ name: manualFilter({ parcellationName, dataset: ds }) }] }
+        : {},
+      preview: hasPreview({ datasetName: ds.name })
+    }
+  })
+
+/**
+ * on init, populate the cached data
+ */
+exports.init = async () => {
+  const { getPublicAccessToken: getPublic } = await kgQueryUtil()
+  getPublicAccessToken = getPublic
+  const {results = []} = await fetchDatasetFromKg()
+  cachedData = results
+}
+
+exports.getDatasets = ({ templateName, parcellationName, user }) => getDs({ user })
+    .then(json => filter(json, {templateName, parcellationName}))
+
+exports.getPreview = ({ datasetName }) => getPreviewFile({ datasetName })
+
+/**
+ * TODO
+ * change to real spatial query
+ */
+const cachedMap = new Map()
+const fetchSpatialDataFromKg = async ({ templateName }) => {
+  const cachedResult = cachedMap.get(templateName)
+  if (cachedResult) 
+    return cachedResult
+    
+  try {
+    const filename = path.join(STORAGE_PATH, templateName + '.json')
+    const exists = fs.existsSync(filename)
+
+    if (!exists)
+      return []
+    
+    const data = fs.readFileSync(filename, 'utf-8')
+    const json = JSON.parse(data)
+    cachedMap.set(templateName, json)
+    return json
+  } catch (e) {
+    console.log('datasets#query.js#fetchSpatialDataFromKg', 'read file and parse json failed', e)
+    return []
+  }
+}
+
+exports.getSpatialDatasets = async ({ templateName, queryGeometry, queryArg }) => {
+  return await fetchSpatialDataFromKg({ templateName })
+}
\ No newline at end of file
diff --git a/deploy/datasets/spatialRouter.js b/deploy/datasets/spatialRouter.js
new file mode 100644
index 0000000000000000000000000000000000000000..584eae6db178407be0590eea5a8d5c3a395742e6
--- /dev/null
+++ b/deploy/datasets/spatialRouter.js
@@ -0,0 +1,47 @@
+const router = require('express').Router()
+const { getSpatialDatasets } = require('./query')
+
+const badRequestString = `spatialSearch endpoint uses param as follows:
+
+GET /templateName/<templateName>/<queryGeometry>/<queryArg>
+
+for example;
+
+GET /templateName/Colin/bbox/0_0_0__1_1_1`
+
+router.get('/templateName/:templateName/:queryGeometry/:queryArg', (req, res, next) => {
+  const { templateName, queryGeometry, queryArg } = req.params
+  let errorString = ``
+  if (!templateName)
+    errorString += `templateName is required\n`
+  if (!queryGeometry)
+    errorString += `queryGeometry is required\n`
+  if (!queryArg)
+    errorString += `queryArg is required\n`
+  if (errorString !== ``)
+    return next({
+      code: 400,
+      error: errorString,
+      trace: 'dataset#spatialRouter'
+    })
+  
+  getSpatialDatasets({ templateName, queryGeometry, queryArg })
+    .then(arr => res.status(200).json(arr))
+    .catch(error => {
+      next({
+        code: 500,
+        error,
+        trace: 'dataset#spatialRouter#getSpatialDatasets'
+      })
+    })
+})
+
+router.use((req, res, next) => {
+  next({
+    code: 400,
+    error: badRequestString,
+    trace: 'dataset#spatialRouter#notFound'
+  })
+})
+
+module.exports = router
\ No newline at end of file
diff --git a/deploy/datasets/supplements/.gitignore b/deploy/datasets/supplements/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..16b9d46ca2ab51e9b5f8a9e5ba31f3ef5a906ab6
--- /dev/null
+++ b/deploy/datasets/supplements/.gitignore
@@ -0,0 +1 @@
+raw
diff --git a/deploy/datasets/supplements/commonSense.js b/deploy/datasets/supplements/commonSense.js
new file mode 100644
index 0000000000000000000000000000000000000000..c3c564e88f1c9266ba75890b5fd5ab0e2b0f1c89
--- /dev/null
+++ b/deploy/datasets/supplements/commonSense.js
@@ -0,0 +1,52 @@
+
+const humanTemplateSet = new Set([
+  'Big Brain (Histology)',
+  'MNI Colin 27',
+  'MNI 152 ICBM 2009c Nonlinear Asymmetric'
+])
+
+const humanParcellationSet = new Set([
+  'Grey/White matter',
+  'Area V1',
+  'Area V2',
+  'JuBrain Cytoarchitectonic Atlas',
+  'Fibre Bundle Atlas - Short Bundle',
+  'Fibre Bundle Atlas - Long Bundle'
+])
+
+const ratTemplateSet = new Set([
+  'Waxholm Space rat brain atlas v.2.0'
+])
+
+const ratParcellationSet = new Set([
+  'Waxholm Space rat brain atlas v.2.0'
+])
+
+const mouseTemplateSet = new Set([
+  'Allen adult mouse brain reference atlas V3'
+])
+
+const mouseParcellationSet = new Set([
+  'Allen adult mouse brain reference atlas V3 Brain Atlas'
+])
+
+const dsIsHuman = ({ds}) => ds && ds.species.some(species => /homo\ sapiens/i.test(species))
+const dsIsRat = ({ ds }) => ds && ds.species.some(species => /rattus\ norvegicus/i.test(species))
+const dsIsMouse = ({ ds }) => ds && ds.species.some(species => /mus\ musculus/i.test(species))
+
+const queryIsMouse = ({ templateName, parcellationName }) => 
+  (templateName && mouseTemplateSet.has(templateName))
+  || (parcellationName && mouseParcellationSet.has(parcellationName))
+
+const queryIsRat = ({ templateName, parcellationName }) => 
+  (templateName && ratTemplateSet.has(templateName))
+  || (parcellationName && ratParcellationSet.has(parcellationName))
+
+const queryIsHuman = ({ templateName, parcellationName }) =>
+  (templateName && humanTemplateSet.has(templateName))
+  || (parcellationName && humanParcellationSet.has(parcellationName))
+
+exports.commonSenseDsFilter = ({ ds , templateName, parcellationName}) => 
+  (queryIsHuman({ templateName, parcellationName }) && !dsIsRat({ ds }) && !dsIsMouse({ ds }))
+  || (queryIsMouse({ templateName, parcellationName }) && !dsIsRat({ ds }) && !dsIsHuman({ ds }))
+  || (queryIsRat({ templateName, parcellationName }) && !dsIsMouse({ ds }) && !dsIsHuman({ ds }))
diff --git a/deploy/datasets/supplements/data/allen.json b/deploy/datasets/supplements/data/allen.json
new file mode 100644
index 0000000000000000000000000000000000000000..58ad544920be956ac5dc0ad5c5f880c04eca0451
--- /dev/null
+++ b/deploy/datasets/supplements/data/allen.json
@@ -0,0 +1,453 @@
+[
+  {
+    "Dataset name": "3D imaging of the vascular system of the mouse brain.",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus&p=2#Dataset/632636291a6ff5cb30c55821abaa55c4",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebral cortex",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2F3248-7V",
+    "PLA": "774",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "3D reconstruction of the vascular system of the mouse brain.",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/a3fd14501d8a5c2ffcaee445ac6f8a5e",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebral cortex",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493/3248-7V",
+    "PLA": "745",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Action potential dependent sIPSCs - from juvenile (P21-30) C57Bl6/J male mice",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/679c1e4beb7b5f8d7097f1835480a76f",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Field CA1",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2FH57G-ZK5",
+    "PLA": "15",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Arc expression in resting state",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus&p=2#Dataset/6a5980f16d1b3fd1422db723f9e2c6dc",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 4",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "Landmark-Reg",
+    "DOI": "https://doi.org/10.25493%2FMDRW-SWQ",
+    "PLA": "19",
+    "EMBARGO": "NO",
+    "Comments:": "right hemisphere"
+  },
+  {
+    "Dataset name": "Brain-wide distribution of glutamate type 1 transporter protein (GLT1)",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/63bbb845ac6d2f1839f919c2ef0455bc",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FY147-2CE",
+    "PLA": "167",
+    "EMBARGO": "NO",
+    "Comments:": "also in WHS v2"
+  },
+  {
+    "Dataset name": "Brainwide distribution and variance of amyloid-beta deposits in tg-ArcSwe mice",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/7e972a9c1c1fc2bb493d09512f6550d5",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FG6CQ-D4D",
+    "PLA": "138",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Ca2+/calmodulin-dependent protein kinase II tetracycline-transactivator expression: coronal sections (case 317.8)",
+    "Dataset URL": "https://www.humanbrainproject.eu/en/explore-the-brain/search/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/e15e4048955ed04112d2652d5a8ce587",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FW97C-01R",
+    "PLA": "case 317.8",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Cell density and distribution in the somatosensory cortex of the mouse brain",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/4e3d82a38ec816d970dff814c9fd2689",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Primary somatosensory area, lower limb; Primary motor area",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2F4HFC-6ZX",
+    "PLA": "",
+    "EMBARGO": "YES",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Comparative overview of brain-wide tetracycline-transactivator expression",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus&p=2#Dataset/48cffa543d49f446f0187902734dd631",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 4",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FARKS-R7H",
+    "PLA": "N/A",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Cortical recordings of the Fmr1KO mouse model of Fragile X syndrome during slow wave activity",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/9f11bd320876be5d037164ce696a384f",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Prelimbic area; Primary motor area; Primary somatosensory area; Anteromedial visual area",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2F5VDM-SHH",
+    "PLA": "",
+    "EMBARGO": "YES",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Fluorescence cortical recording of mouse activity after stroke",
+    "Dataset URL": "https://www.humanbrainproject.eu/en/explore-the-brain/search/?facet_type[0]=Dataset#Dataset/4730b8c4ae603587a04b4c4362869f91",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 4",
+    "Semantic link(s)": "Anterior area; Anterior cingulate area, dorsal part; Anterolateral visual area; Posteromedial visual area; Primary motor area; Primary somatosensory area, barrel field; Primary somatosensory area, lower limb; Primary somatosensory area, nose; Primary somatosensory area, trunk; Primary somatosensory area, unassigned; Primary somatosensory area, upper limb; Primary visual area; Retrosplenial area, dorsal part; Retrosplenial area, lateral agranular part; Retrosplenial area, ventral part; Rostrolateral area; Secondary motor area",
+    "Coordinate-based": "Point coordinates",
+    "DOI": "https://doi.org/10.25493%2FZ9J0-ZZQ",
+    "PLA": "552",
+    "EMBARGO": "YES",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Hippocampal image volume derived from Thy1-GFP-M transgenic mouse",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/8fb1a664ca3390bae960cc1aa11f5827",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Hippocampal region",
+    "Coordinate-based": "Landmark-Reg",
+    "DOI": "https://doi.org/10.25493%2FPMDH-FW1",
+    "PLA": "20",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Neuropsin tetracycline-transactivator expression: coronal sections (case 1952)",
+    "Dataset URL": "https://www.humanbrainproject.eu/en/explore-the-brain/search/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/e0f992755e391b1dbdaf5b9669ffb49a",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 3",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2F5H13-1Q0",
+    "PLA": "case 1952",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Neuropsin tetracycline-transactivator expression: coronal sections (case 2849)",
+    "Dataset URL": "https://www.humanbrainproject.eu/en/explore-the-brain/search/?facet_type[0]=Dataset#Dataset/cf9866eac157682b8320037236f54aa4",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 4",
+    "Semantic link(s)": "Cerebrum",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FWB6K-V72",
+    "PLA": "case 2849",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Neuropsin tetracycline-transactivator expression: horizontal sections (case 2877)",
+    "Dataset URL": "https://www.humanbrainproject.eu/en/explore-the-brain/search/?facet_type[0]=Dataset#Dataset/d56b1fe14bb84987a3a2340e21652b2d",
+    "Reference atlas": "Allen Mouse Common Coordinate Framework, version 5",
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diff --git a/deploy/datasets/supplements/data/pmapDWMPreview.json b/deploy/datasets/supplements/data/pmapDWMPreview.json
new file mode 100644
index 0000000000000000000000000000000000000000..447b0ac57c1350d9e61702196febc20b592186d0
--- /dev/null
+++ b/deploy/datasets/supplements/data/pmapDWMPreview.json
@@ -0,0 +1 @@
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NeuroImage, 61(4),1083-99."}],"associatedRegion":"Cingulum_Temporal - Right","associatedParcellation":"Fibre Bundle Atlas - Long Bundle","datasetInfo":"MNIColin27_PMap.json"},"url":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/DWM_atlas/right-hemisphere/Probability_Maps/Cingulum_Temporal_Right.nii"}]],["Probabilistic map of the right corticospinal tract (atlas of deep white matter fibre bundles, version 2018)",[{"filename":"Probabilistic Map for CorticoSpinalTract - Right","name":"Probabilistic Map for CorticoSpinalTract - Right","mimetype":"application/nifti","properties":{"name":null,"description":"","publications":[{"doi":"https://10.1016/j.neuroimage.2012.02.071","citation":"Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99."}],"associatedRegion":"CorticoSpinalTract - Right","associatedParcellation":"Fibre Bundle Atlas - Long Bundle","datasetInfo":"MNIColin27_PMap.json"},"url":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/DWM_atlas/right-hemisphere/Probability_Maps/CorticoSpinalTract_Right.nii"}]],["Probabilistic map of the right fornix (atlas of deep white matter fibre bundles, version 2018)",[{"filename":"Probabilistic Map for Fornix - Right","name":"Probabilistic Map for Fornix - Right","mimetype":"application/nifti","properties":{"name":null,"description":"","publications":[{"doi":"https://10.1016/j.neuroimage.2012.02.071","citation":"Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. 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NeuroImage, 61(4),1083-99."}],"associatedRegion":"InferiorFrontoOccipital - Right","associatedParcellation":"Fibre Bundle Atlas - Long Bundle","datasetInfo":"MNIColin27_PMap.json"},"url":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/DWM_atlas/right-hemisphere/Probability_Maps/InferiorFrontoOccipital_Right.nii"}]],["Probabilistic map of the right inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018)",[{"filename":"Probabilistic Map for InferiorLongitudinal - Right","name":"Probabilistic Map for InferiorLongitudinal - Right","mimetype":"application/nifti","properties":{"name":null,"description":"","publications":[{"doi":"https://10.1016/j.neuroimage.2012.02.071","citation":"Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99."}],"associatedRegion":"InferiorLongitudinal - Right","associatedParcellation":"Fibre Bundle Atlas - Long Bundle","datasetInfo":"MNIColin27_PMap.json"},"url":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/DWM_atlas/right-hemisphere/Probability_Maps/InferiorLongitudinal_Right.nii"}]],["Probabilistic map of the right uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018)",[{"filename":"Probabilistic Map for Uncinate - Right","name":"Probabilistic Map for Uncinate - Right","mimetype":"application/nifti","properties":{"name":null,"description":"","publications":[{"doi":"https://10.1016/j.neuroimage.2012.02.071","citation":"Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. 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\ No newline at end of file
diff --git a/deploy/datasets/supplements/data/pmapJubrainPreview.json b/deploy/datasets/supplements/data/pmapJubrainPreview.json
new file mode 100644
index 0000000000000000000000000000000000000000..f336296ba90d483dc36cbf402b3e38c90dbdd5f9
--- /dev/null
+++ b/deploy/datasets/supplements/data/pmapJubrainPreview.json
@@ -0,0 +1 @@
+[["Probabilistic cytoarchitectonic map of Area FG3 (FusG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area FG3 (FusG)","name":"Cytoarchitectonic Probabilistic Map for Area FG3 (FusG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_FG3.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc4lp (LOC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc4lp (LOC)","name":"Cytoarchitectonic Probabilistic Map for Area hOc4lp (LOC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc4lp.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 5L (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 5L (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 5L 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18)","name":"Cytoarchitectonic Probabilistic Map for Area hOc2 (V2, 18)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc2.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc4la (LOC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc4la (LOC)","name":"Cytoarchitectonic Probabilistic Map for Area hOc4la (LOC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc4la.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Id1 (Insula)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Id1 (Insula)","name":"Cytoarchitectonic Probabilistic Map for Area Id1 (Insula)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Insula_Id1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc1 (V1, 17, CalcS)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc1 (V1, 17, CalcS)","name":"Cytoarchitectonic Probabilistic Map for Area hOc1 (V1, 17, CalcS)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Fastigial Nucleus (Cerebellum)",[{"filename":"Cytoarchitectonic Probabilistic Map for Fastigial Nucleus (Cerebellum)","name":"Cytoarchitectonic Probabilistic Map for Fastigial Nucleus (Cerebellum)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Nfast.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 5M (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 5M (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 5M (SPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/SPL_5M.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 7PC (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 7PC (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 7PC (SPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/SPL_7PC.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area s24 (sACC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area s24 (sACC)","name":"Cytoarchitectonic Probabilistic Map for Area s24 (sACC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cingulum_s24.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 1 (PostCG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 1 (PostCG)","name":"Cytoarchitectonic Probabilistic Map for Area 1 (PostCG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/PSC_1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Fp1 (Fpole)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Fp1 (Fpole)","name":"Cytoarchitectonic Probabilistic Map for Area Fp1 (Fpole)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/FrontalPole_Fp1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Fo3 (OFC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Fo3 (OFC)","name":"Cytoarchitectonic Probabilistic Map for Area Fo3 (OFC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/OFC_Fo3.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Entorhinal Cortex",[{"filename":"Cytoarchitectonic Probabilistic Map for Entorhinal Cortex","name":"Cytoarchitectonic Probabilistic Map for Entorhinal Cortex","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_EC.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 45 (IFG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 45 (IFG)","name":"Cytoarchitectonic Probabilistic Map for Area 45 (IFG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Broca_45.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Fp2 (Fpole)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Fp2 (Fpole)","name":"Cytoarchitectonic Probabilistic Map for Area Fp2 (Fpole)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/FrontalPole_Fp2.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 3b (PostCG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 3b (PostCG)","name":"Cytoarchitectonic Probabilistic Map for Area 3b (PostCG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/PSC_3b.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of DG (Hippocampus)",[{"filename":"Cytoarchitectonic Probabilistic Map for DG (Hippocampus)","name":"Cytoarchitectonic Probabilistic Map for DG (Hippocampus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_DG.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hIP3 (IPS)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hIP3 (IPS)","name":"Cytoarchitectonic Probabilistic Map for Area hIP3 (IPS)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/AIPS_IP3.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hIP1 (IPS)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hIP1 (IPS)","name":"Cytoarchitectonic Probabilistic Map for Area hIP1 (IPS)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/AIPS_IP1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 5Ci (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 5Ci (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 5Ci (SPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/SPL_5Ci.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of HATA (Hippocampus)",[{"filename":"Cytoarchitectonic Probabilistic Map for HATA (Hippocampus)","name":"Cytoarchitectonic Probabilistic Map for HATA (Hippocampus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_HATA.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PFt (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PFt (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PFt (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PFt.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc4d (Cuneus)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc4d (Cuneus)","name":"Cytoarchitectonic Probabilistic Map for Area hOc4d (Cuneus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc4d.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of CA1 (Hippocampus)",[{"filename":"Cytoarchitectonic Probabilistic Map for CA1 (Hippocampus)","name":"Cytoarchitectonic Probabilistic Map for CA1 (Hippocampus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_CA1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area FG4 (FusG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area FG4 (FusG)","name":"Cytoarchitectonic Probabilistic Map for Area FG4 (FusG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_FG4.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of LB (Amygdala)",[{"filename":"Cytoarchitectonic Probabilistic Map for LB (Amygdala)","name":"Cytoarchitectonic Probabilistic Map for LB (Amygdala)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Amygdala_LB.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of CM (Amygdala)",[{"filename":"Cytoarchitectonic Probabilistic Map for CM (Amygdala)","name":"Cytoarchitectonic Probabilistic Map for CM (Amygdala)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Amygdala_CM.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area s32 (sACC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area s32 (sACC)","name":"Cytoarchitectonic Probabilistic Map for Area s32 (sACC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cingulum_s32.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PFcm (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PFcm (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PFcm (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PFcm.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 25 (sACC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 25 (sACC)","name":"Cytoarchitectonic Probabilistic Map for Area 25 (sACC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cingulum_25.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 7M (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 7M (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 7M (SPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/SPL_7M.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Ig1 (Insula)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Ig1 (Insula)","name":"Cytoarchitectonic Probabilistic Map for Area Ig1 (Insula)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Insula_Ig1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 44 (IFG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 44 (IFG)","name":"Cytoarchitectonic Probabilistic Map for Area 44 (IFG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Broca_44.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Subc (Hippocampus)",[{"filename":"Cytoarchitectonic Probabilistic Map for Subc (Hippocampus)","name":"Cytoarchitectonic Probabilistic Map for Subc (Hippocampus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_Subc.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area TE 1.1 (HESCHL)","name":"Cytoarchitectonic Probabilistic Map for Area TE 1.1 (HESCHL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Auditory_Te11.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PF (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PF (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PF (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PF.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Ch 1-3 (Basal Forebrain)",[{"filename":"Cytoarchitectonic Probabilistic Map for Ch 1-3 (Basal Forebrain)","name":"Cytoarchitectonic Probabilistic Map for Ch 1-3 (Basal Forebrain)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Bforebrain_123.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PGp (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PGp (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PGp (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PGp.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area OP4 (POperc)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area OP4 (POperc)","name":"Cytoarchitectonic Probabilistic Map for Area OP4 (POperc)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Operculum_OP4.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Ch 4 (Basal Forebrain)",[{"filename":"Cytoarchitectonic Probabilistic Map for Ch 4 (Basal Forebrain)","name":"Cytoarchitectonic Probabilistic Map for Ch 4 (Basal Forebrain)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Bforebrain_4.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc3d (Cuneus)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc3d (Cuneus)","name":"Cytoarchitectonic Probabilistic Map for Area hOc3d (Cuneus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc3d.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc5 (LOC)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc5 (LOC)","name":"Cytoarchitectonic Probabilistic Map for Area hOc5 (LOC)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc5.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Dorsal Dentate Nucleus (Cerebellum)",[{"filename":"Cytoarchitectonic Probabilistic Map for Dorsal Dentate Nucleus (Cerebellum)","name":"Cytoarchitectonic Probabilistic Map for Dorsal Dentate Nucleus (Cerebellum)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Ndentd.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area OP2 (POperc)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area OP2 (POperc)","name":"Cytoarchitectonic Probabilistic Map for Area OP2 (POperc)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Operculum_OP2.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PFm (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PFm (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PFm (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PFm.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area TE 1.0 (HESCHL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area TE 1.0 (HESCHL)","name":"Cytoarchitectonic Probabilistic Map for Area TE 1.0 (HESCHL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Auditory_Te10.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of CA2 (Hippocampus)",[{"filename":"Cytoarchitectonic Probabilistic Map for CA2 (Hippocampus)","name":"Cytoarchitectonic Probabilistic Map for CA2 (Hippocampus)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Hippocampus_CA2.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 4a (PreCG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 4a (PreCG)","name":"Cytoarchitectonic Probabilistic Map for Area 4a (PreCG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Motor_4a.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area PGa (IPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area PGa (IPL)","name":"Cytoarchitectonic Probabilistic Map for Area PGa (IPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/IPL_PGa.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area hOc4v (LingG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area hOc4v (LingG)","name":"Cytoarchitectonic Probabilistic Map for Area hOc4v (LingG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Visual_hOc4v.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area OP3 (POperc)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area OP3 (POperc)","name":"Cytoarchitectonic Probabilistic Map for Area OP3 (POperc)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Operculum_OP3.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of SF (Amygdala)",[{"filename":"Cytoarchitectonic Probabilistic Map for SF (Amygdala)","name":"Cytoarchitectonic Probabilistic Map for SF (Amygdala)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Amygdala_SF.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 3a (PostCG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 3a (PostCG)","name":"Cytoarchitectonic Probabilistic Map for Area 3a (PostCG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/PSC_3a.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area Ig2 (Insula)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area Ig2 (Insula)","name":"Cytoarchitectonic Probabilistic Map for Area Ig2 (Insula)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Insula_Ig2.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area OP1 (POperc)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area OP1 (POperc)","name":"Cytoarchitectonic Probabilistic Map for Area OP1 (POperc)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Operculum_OP1.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 4p (PreCG)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 4p (PreCG)","name":"Cytoarchitectonic Probabilistic Map for Area 4p (PreCG)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Motor_4p.nii","properties":{}}]],["Probabilistic cytoarchitectonic map of Area 7A (SPL)",[{"filename":"Cytoarchitectonic Probabilistic Map for Area 7A (SPL)","name":"Cytoarchitectonic Probabilistic Map for Area 7A (SPL)","mimetype":"application/nifti","url":"https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/SPL_7A.nii","properties":{}}]]]
\ No newline at end of file
diff --git a/deploy/datasets/supplements/data/receptorPreview.json b/deploy/datasets/supplements/data/receptorPreview.json
new file mode 100644
index 0000000000000000000000000000000000000000..535ad50bf3bed721ddb1ae3eba56469784b7e66a
--- /dev/null
+++ b/deploy/datasets/supplements/data/receptorPreview.json
@@ -0,0 +1 @@
+[["Density measurements of different receptors for Area 4p",[{"name":"Receptor density fingerprint of Area 4p","filename":"fingerprint","mimetype":"application/json","properties":{"description":"**Multireceptor fingerprint for area 4p.** This polar plot shows the mean receptor densities in fmol/mg protein (solid shape) and standard deviation (dashed line) of 16 receptor binding sites in the area 4p. The data is based on the left and right hemisphere of one female subject (brain id: hg0201, sample ids: ID07 and ID08, age: 77, cause of death: lung edema) and the right hemispheres of two male subjects (brain id: hg0500, sample ids: ID09, age: 72, cause of death: cardiac arrest | brain id: hg0100, sample ids: ID12, age: 77, cause of death: coronary heart disease).","publications":[]},"data":{"chartType":"radar","labels":["AMPA (Glu)","kainate (Glu)","NMDA (Glu)","mGluR2/3 (Glu)","GABAᴀ(GABA)","GABAᴃ(GABA)","GABAᴀ(BZ)","M₁(ACh)","M₂(ACh)","M₃(ACh)","α₄β₂(ACh)","α₁(NA)","α₂(NA)","5-HT₁ᴀ(5-HT)","5-HT₂(5-HT)","D₁(DA)"],"datasets":[{"label":"mean","data":["286","286","739","NA","999","1842","1337","245","141","400","69","193","111","162","234","49"]},{"label":"mean_sd","data":["40","35","1","NA","108","10","65","23","27","44","13","7","15","31","2","5"]}],"chartOptions":{"scale":{"ticks":{"showLabelBackdrop":false}},"legend":{"display":true},"title":{"text":"Receptor density fingerprint of Area 4p (fmol/mg protein)","display":true},"elements":{"point":{"radius":0,"hoverRadius":8,"hitRadius":4}}},"colors":[{"borderColor":"rgba(255,255,255,1)","backgroundColor":"rgba(139,71,137,0.2)"},{"borderColor":"rgba(255,255,255,1)"}]}},{"name":"Sample autoradiograph of GABAᴀ(BZ) in Area 4p","filename":"GABAᴀ(BZ)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2F4p_bm_BZ.jpg"},{"name":"Sample autoradiograph of α₁(NA) in Area 4p","filename":"α₁(NA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2F4p_bm_alpha1.jpg"},{"name":"Sample autoradiograph of NMDA (Glu) in Area 4p","filename":"NMDA 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FG1","filename":"GABAᴀ(BZ)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_BZ.jpg"},{"name":"Sample autoradiograph of α₁(NA) in Area FG1","filename":"α₁(NA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_alpha1.jpg"},{"name":"Sample autoradiograph of NMDA (Glu) in Area FG1","filename":"NMDA (Glu)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_NMDA.jpg"},{"name":"Sample autoradiograph of GABAᴀ(GABA) in Area FG1","filename":"GABAᴀ(GABA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_GABAA.jpg"},{"name":"Sample autoradiograph of α₂(NA) in Area FG1","filename":"α₂(NA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_alpha2.jpg"},{"name":"Sample autoradiograph of α₄β₂(ACh) in Area FG1","filename":"α₄β₂(ACh)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_alpha4beta2.jpg"},{"name":"Sample autoradiograph of M₁(ACh) in Area FG1","filename":"M₁(ACh)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_M1.jpg"},{"name":"Sample autoradiograph of 5-HT₁ᴀ(5-HT) in Area FG1","filename":"5-HT₁ᴀ(5-HT)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_5-HT1A.jpg"},{"name":"Sample autoradiograph of kainate (Glu) in Area FG1","filename":"kainate (Glu)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_kainate.jpg"},{"name":"Sample autoradiograph of M₂(ACh) in Area FG1","filename":"M₂(ACh)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_M2.jpg"},{"name":"Sample autoradiograph of AMPA (Glu) in Area FG1","filename":"AMPA (Glu)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_AMPA.jpg"},{"name":"Sample autoradiograph of D₁(DA) in Area FG1","filename":"D₁(DA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_D1.jpg"},{"name":"Sample autoradiograph of M₃(ACh) in Area FG1","filename":"M₃(ACh)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_M3.jpg"},{"name":"Sample autoradiograph of 5-HT₂(5-HT) in Area FG1","filename":"5-HT₂(5-HT)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_5-HT2.jpg"},{"name":"Sample autoradiograph of GABAᴃ(GABA) in Area FG1","filename":"GABAᴃ(GABA)/autoradiography","mimetype":"image/jpeg","properties":{"description":null,"publications":[]},"data":null,"url":"datasets/previewFile?file=res%2Fimage%2Freceptor%2FFG1_bm_GABAB.jpg"}]],["Density measurements of different receptors for Area PGa",[{"name":"Receptor density fingerprint of Area PGa","filename":"fingerprint","mimetype":"application/json","properties":{"description":null,"publications":[]},"data":{"chartType":"radar","labels":["AMPA (Glu)","kainate (Glu)","NMDA (Glu)","mGluR2/3 (Glu)","GABAᴀ(GABA)","GABAᴃ(GABA)","GABAᴀ(BZ)","M₁(ACh)","M₂(ACh)","M₃(ACh)","α₄β₂(ACh)","α₁(NA)","α₂(NA)","5-HT₁ᴀ(5-HT)","5-HT₂(5-HT)","D₁(DA)"],"datasets":[{"label":"mean","data":["463","635","1259","3957","1998","2742","2442","479","172","827","70","361","698","303","441","136"]},{"label":"mean_sd","data":["187","405","278","1655","577","291","876","192","77","348","34","169","mg/mol","123","238","55"]}],"chartOptions":{"scale":{"ticks":{"showLabelBackdrop":false}},"legend":{"display":true},"title":{"text":"Receptor density fingerprint of Area PGa (fmol/mg protein)","display":true},"elements":{"point":{"radius":0,"hoverRadius":8,"hitRadius":4}}},"colors":[{"borderColor":"rgba(255,255,255,1)","backgroundColor":"rgba(5,198,198,0.2)"},{"borderColor":"rgba(255,255,255,1)"}]}}]]]
\ No newline at end of file
diff --git a/deploy/datasets/supplements/data/waxholm.json b/deploy/datasets/supplements/data/waxholm.json
new file mode 100644
index 0000000000000000000000000000000000000000..db52cc9345128274a7cd4a56bf2d49e7417dfaf1
--- /dev/null
+++ b/deploy/datasets/supplements/data/waxholm.json
@@ -0,0 +1,112 @@
+[
+  {
+    "Dataset name": "3D high resolution SRXTM image data of cortical vasculature of rat brain.",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/943484f6f124311f2a60861e29f49b3e",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Neocortex",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2FHCPQ-MY8",
+    "PLA": "18",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Atlas of normal rat brain cyto- and myeloarchitecture",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/6ce1f96ae210b2335b75a793367e3865",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Whole Brain (v2.0)",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FC63A-FEY",
+    "PLA": "165",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Brain-wide distribution of glutamate type 1 transporter protein (GLT1)",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Mus%20musculus#Dataset/63bbb845ac6d2f1839f919c2ef0455bc",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Whole Brain (v2.0)",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FY147-2CE",
+    "PLA": "167",
+    "EMBARGO": "NO",
+    "Comments:": "Also in CCF v3"
+  },
+  {
+    "Dataset name": "Brain-wide distribution of vesicular glutamate transporter type 3 (VGLUT3)",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/ca2de49ed9e6660242b937c8e9f2e4eb",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Whole Brain (v2.0)",
+    "Coordinate-based": "QuickNII",
+    "DOI": "https://doi.org/10.25493%2FGP15-1MS",
+    "PLA": "168",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Graphical representation of rat cortical vasculature reconstructed from high resolution 3D SRXTM data.",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/fc6865e8670b65aebec05b73f9e795c7",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Neocortex",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2FK243-13K",
+    "PLA": "17",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Large scale multi-channel EEG in rats",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/c5aab03945700ffb1a2fd2fd83819340",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Neocortex",
+    "Coordinate-based": "Point coordinates",
+    "DOI": "https://doi.org/10.25493%2F4SPM-V00",
+    "PLA": "",
+    "EMBARGO": "YES",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Loose-cell attached patch-clamp recordings from cerebellar granule cells",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/430c8508746cbd9f8f5c91b66ae97316",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Cerebellum",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2F6R48-E3V",
+    "PLA": "810",
+    "EMBARGO": "NO",
+    "Comments:": ""
+  },
+  {
+    "Dataset name": "Recordings of cerebellar neuronal firing induced by currents steps",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/5e245cf67a42a14a56bc43913f7bf28a",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Cerebellum",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2F4AF6-WSD",
+    "PLA": "810",
+    "EMBARGO": "NO",
+    "Comments:": "Also in CCF v3"
+  },
+  {
+    "Dataset name": "Recordings of excitatory postsynaptic currents from cerebellar neurons",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/a1ba394b5f353a67f9cec3549891d77b",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Cerebellum",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2FF2VK-MB4",
+    "PLA": "810",
+    "EMBARGO": "NO",
+    "Comments:": "Also in CCF v3"
+  },
+  {
+    "Dataset name": "Recordings of excitatory postsynaptic potentials from cerebellar neurons",
+    "Dataset URL": "https://kg.humanbrainproject.org/webapp/?facet_type[0]=Dataset&facet_Dataset_subjects.children.species[0]=Rattus%20norvegicus#Dataset/2ea6f32e8dfd644d426b93aac0ae6eec",
+    "Reference atlas": "Waxholm Space atlas of the Sprague Dawley rat brain, version 2",
+    "Semantic link(s)": "Cerebellum",
+    "Coordinate-based": "",
+    "DOI": "https://doi.org/10.25493%2FG07Q-K87",
+    "PLA": "810",
+    "EMBARGO": "NO",
+    "Comments:": "Also in CCF v3"
+  }
+]
\ No newline at end of file
diff --git a/deploy/datasets/supplements/parcellation.js b/deploy/datasets/supplements/parcellation.js
new file mode 100644
index 0000000000000000000000000000000000000000..20527e571c065ddcf70d9a6a7ba9162e95af79b2
--- /dev/null
+++ b/deploy/datasets/supplements/parcellation.js
@@ -0,0 +1,27 @@
+const fs = require('fs')
+const path = require('path')
+let waxholm = new Map(),
+  allen = new Map()
+
+fs.readFile(path.join(__dirname, 'data', 'allen.json'), 'utf-8', (err, data) => {
+  if (err)
+    throw err
+  const json = JSON.parse(data)
+  allen = new Map(json.map(item => [item['Dataset name'], item["Semantic link(s)"]]))
+})
+
+
+fs.readFile(path.join(__dirname, 'data', 'waxholm.json'), 'utf-8', (err, data) => {
+  if (err)
+    throw err
+  const json = JSON.parse(data)
+  waxholm = new Map(json.map(item => [item['Dataset name'], item["Semantic link(s)"]]))
+})
+
+module.exports = ({ parcellationName, dataset }) => {
+  return parcellationName === 'Allen adult mouse brain reference atlas V3 Brain Atlas'
+    ? allen.get(dataset.name)
+    : parcellationName === 'Whole Brain (v2.0)'
+      ? waxholm.get(dataset.name)
+      : false
+}
\ No newline at end of file
diff --git a/deploy/datasets/supplements/previewFile.js b/deploy/datasets/supplements/previewFile.js
new file mode 100644
index 0000000000000000000000000000000000000000..dcdf4db88c076e45b4f11cf10c82eba348b82238
--- /dev/null
+++ b/deploy/datasets/supplements/previewFile.js
@@ -0,0 +1,50 @@
+const fs = require('fs')
+const path = require('path')
+
+const DISABLE_RECEPTOR_PREVIEW = process.env.DISABLE_RECEPTOR_PREVIEW
+const DISABLE_JUBRAIN_PMAP = process.env.DISABLE_JUBRAIN_PMAP
+const DISABLE_DWM_PMAP = process.env.DISABLE_DWM_PMAP
+const HOSTNAME = process.env.HOSTNAME || 'http://localhost:3000'
+
+let previewMap = new Map(),
+  previewMapKeySet = new Set()
+
+const readFile = (filename) => new Promise((resolve) => {
+  fs.readFile(path.join(__dirname, 'data', filename), 'utf-8', (err, data) => {
+    if (err){
+      console.log('read file error', err)
+      return resolve([])
+    }
+    resolve(JSON.parse(data))
+  })
+})
+
+Promise.all([
+  DISABLE_RECEPTOR_PREVIEW ? Promise.resolve([]) : readFile('receptorPreview.json'),
+  DISABLE_JUBRAIN_PMAP ? Promise.resolve([]) : readFile('pmapJubrainPreview.json'),
+  DISABLE_DWM_PMAP ? Promise.resolve([]) : readFile('pmapDWMPreview.json')
+])
+  .then(arrOfA => arrOfA.reduce((acc, item) => acc.concat(item), []))
+  .then(iterable => {
+    previewMap = new Map(iterable)
+    previewMapKeySet = new Set(previewMap.keys())
+  })
+  .catch(e => {
+    console.error('preview file error', e)
+  })
+
+exports.getPreviewFile = ({ datasetName }) => Promise.resolve(
+  previewMap.get(datasetName)
+    .map(file => {
+      return {
+        ...file,
+        ...(file.url && !/^http/.test(file.url)
+          ? {
+            url: `${HOSTNAME}/${file.url}`
+          }
+          : {})
+      }
+    })
+)
+exports.getAllPreviewDSNames = () => Array.from(previewMap.keys())
+exports.hasPreview = ({ datasetName }) => previewMapKeySet.has(datasetName)
\ No newline at end of file
diff --git a/deploy/datasets/supplements/util/mapDwm.js b/deploy/datasets/supplements/util/mapDwm.js
new file mode 100644
index 0000000000000000000000000000000000000000..b9cda2f1143970a66397c5572c7fbf991ddb96bd
--- /dev/null
+++ b/deploy/datasets/supplements/util/mapDwm.js
@@ -0,0 +1,69 @@
+exports.manualFilter = (dataset) => dataset 
+  && dataset.project
+  && dataset.project instanceof Array
+  && dataset.project.some(p => p === 'Atlas of deep white matter fibre bundles, version 2018')
+
+const dict = [ [ 'Probabilistic map of the anterior segment of the left arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate_Anterior - Left' ],
+[ 'Probabilistic map of the direct segment of the left arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate - Left' ],
+[ 'Probabilistic map of the posterior segment of the left arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate_Posterior - Left' ],
+[ 'Probabilistic map of the left long cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Long - Left' ],
+[ 'Probabilistic map of the left short cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Short - Left' ],
+[ 'Probabilistic map of the left temporal cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Temporal - Left' ],
+[ 'Probabilistic map of the left corticospinal tract (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/CorticoSpinalTract - Left' ],
+[ 'Probabilistic map of the left fornix (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Fornix - Left' ],
+[ 'Probabilistic map of the left inferior fronto-occipital fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/InferiorFrontoOccipital - Left' ],
+[ 'Probabilistic map of the left inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/InferiorLongitudinal - Left' ],
+[ 'Probabilistic map of the left uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Uncinate - Left' ],
+[ 'Probabilistic map of the anterior segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate_Anterior - Right' ],
+[ 'Probabilistic map of the posterior segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate_Posterior - Right' ],
+[ 'Probabilistic map of the posterior segment of the right arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Arcuate - Right' ],
+[ 'Probabilistic map of the right long cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Long - Right' ],
+[ 'Probabilistic map of the right short cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Short - Right' ],
+[ 'Probabilistic map of the right temporal cingulate fibres (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Cingulum_Temporal - Right' ],
+[ 'Probabilistic map of the right corticospinal tract (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/CorticoSpinalTract - Right' ],
+[ 'Probabilistic map of the right fornix (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Fornix - Right' ],
+[ 'Probabilistic map of the right inferior fronto-occipital fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/InferiorFrontoOccipital - Right' ],
+[ 'Probabilistic map of the right inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/InferiorLongitudinal - Right' ],
+[ 'Probabilistic map of the right uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018)',
+'Fibre Bundle Atlas - Long Bundle/Uncinate - Right' ] ]
+
+const dsNameToIdMap = new Map(dict)
+const getIdFromDataset = (dataset) => {
+  const id = dsNameToIdMap.get(dataset.name)
+  if (id) return id
+  return null
+}
+
+exports.manualMap = (dataset) => {
+  return {
+    preview: !process.env.DISABLE_DWM_PMAP,
+    ...dataset,
+    parcellationRegion: dataset.parcellationRegion.map(r => {
+      return {
+        ...r,
+        id: getIdFromDataset(dataset)
+      }
+    })
+  }
+}
\ No newline at end of file
diff --git a/deploy/nehubaConfig/index.js b/deploy/nehubaConfig/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..a5072c82865d230eb8d5320c028d5f9505ec1e38
--- /dev/null
+++ b/deploy/nehubaConfig/index.js
@@ -0,0 +1,19 @@
+const express = require('express')
+const path = require('path')
+const fs = require('fs')
+const { getTemplateNehubaConfig } = require('./query')
+
+const nehubaConfigRouter = express.Router()
+
+nehubaConfigRouter.get('/:configId', (req, res, next) => {
+  const { configId } = req.params
+  getTemplateNehubaConfig(configId)
+    .then(data => res.status(200).send(data))
+    .catch(error => next({
+      code: 500,
+      error,
+      trace: 'nehubaConfigRouter#getTemplateNehubaConfig'
+    }))
+})
+
+module.exports = nehubaConfigRouter
\ No newline at end of file
diff --git a/deploy/nehubaConfig/query.js b/deploy/nehubaConfig/query.js
new file mode 100644
index 0000000000000000000000000000000000000000..972c3d59633cd3748d3678a636f8bcecdf7ed0d9
--- /dev/null
+++ b/deploy/nehubaConfig/query.js
@@ -0,0 +1,15 @@
+const fs = require('fs')
+const path = require('path')
+
+exports.getTemplateNehubaConfig = (configId) => new Promise((resolve, reject) => {
+  let filepath
+  if (process.env.NODE_ENV === 'production') {
+    filepath = path.join(__dirname, '..', 'res', `${configId}.json`)
+  } else {
+    filepath = path.join(__dirname, '..', '..', 'src', 'res', 'ext', `${configId}.json`)
+  }
+  fs.readFile(filepath, 'utf-8', (err, data) => {
+    if (err) return reject(err)
+    resolve(data)
+  })
+})
\ No newline at end of file
diff --git a/deploy/package.json b/deploy/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..e76b3d9bbe32d2aae33a3d7ae39d7ae3740f50e1
--- /dev/null
+++ b/deploy/package.json
@@ -0,0 +1,31 @@
+{
+  "name": "deploy",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "start": "node server.js",
+    "test": "npm run testEnv && npm run testNoEnv",
+    "testEnv": "node -r dotenv/config ./node_modules/.bin/mocha ./test/mocha.test.js",
+    "testNoEnv": "node ./node_modules/.bin/mocha ./test/mocha.test.noenv.js"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "express": "^4.16.4",
+    "express-session": "^1.15.6",
+    "jwt-decode": "^2.2.0",
+    "memorystore": "^1.6.1",
+    "openid-client": "^2.4.5",
+    "passport": "^0.4.0",
+    "request": "^2.88.0"
+  },
+  "devDependencies": {
+    "chai": "^4.2.0",
+    "chai-as-promised": "^7.1.1",
+    "cors": "^2.8.5",
+    "dotenv": "^6.2.0",
+    "mocha": "^6.1.4"
+  }
+}
diff --git a/deploy/plugins/index.js b/deploy/plugins/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..9f15b4687a85be1f07b76284694132fd0fb799eb
--- /dev/null
+++ b/deploy/plugins/index.js
@@ -0,0 +1,19 @@
+/**
+ * TODO
+ * how to discover plugins?
+ */
+
+const express = require('express')
+const router = express.Router()
+const PLUGIN_URLS = process.env.PLUGIN_URLS && JSON.stringify(process.env.PLUGIN_URLS.split(';'))
+
+router.get('', (_req, res) => {
+
+  if (PLUGIN_URLS) {
+    return res.status(200).send(PLUGIN_URLS)
+  } else {
+    return res.status(200).send('[]')
+  }
+})
+
+module.exports = router
\ No newline at end of file
diff --git a/deploy/server.js b/deploy/server.js
new file mode 100644
index 0000000000000000000000000000000000000000..85596178cccb7433a77195c447e3efa665cd2583
--- /dev/null
+++ b/deploy/server.js
@@ -0,0 +1,11 @@
+if (process.env.NODE_ENV !== 'production') {
+  require('dotenv').config()
+  process.on('unhandledRejection', (err, p) => {
+    console.log({err, p})
+  })
+}
+
+const app = require('./app')
+const PORT = process.env.PORT || 3000
+
+app.listen(PORT, () => console.log(`listening on port ${PORT}`))
\ No newline at end of file
diff --git a/deploy/templates/index.js b/deploy/templates/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..1ed84e544f0754e84ee8f48855d7b30975361de4
--- /dev/null
+++ b/deploy/templates/index.js
@@ -0,0 +1,43 @@
+const router = require('express').Router()
+const query = require('./query')
+const path = require('path')
+/**
+ * root path fetches all templates
+ */
+router.get('/', (req, res, next) => {
+  const baseUrl = req.baseUrl
+  query.getAllTemplates()
+    .then(templates => {
+      const templatesRes = templates.map(v => path.join(baseUrl.slice(1), v))
+      res.status(200).send(JSON.stringify(templatesRes))
+    })
+    .catch(error => next({
+      code: 500,
+      error,
+      trace: 'getTemplate'
+    }))
+})
+
+router.get('/:template', (req, res, next) => {
+  const { template } = req.params
+  query.getAllTemplates()
+    .then(templates => {
+      if (templates.indexOf(template) < 0) 
+        return next({
+          code : 404,
+          error: 'template not in the list supported'
+        })
+      return query.getTemplate(template)
+    })
+    .then(data => {
+      if (data)
+        res.status(200).send(data)
+    })
+    .catch(error => next({
+      code: 500,
+      error,
+      trace: 'getTemplate/template'
+    }))
+})
+
+module.exports = router
\ No newline at end of file
diff --git a/deploy/templates/query.js b/deploy/templates/query.js
new file mode 100644
index 0000000000000000000000000000000000000000..1ecebe476dce91c21819342ad6031f46643b1db8
--- /dev/null
+++ b/deploy/templates/query.js
@@ -0,0 +1,31 @@
+const fs = require('fs')
+const path = require('path')
+
+exports.getAllTemplates = () => new Promise((resolve, reject) => {
+  
+  /**
+   * TODO temporary. Need to query KG or something else for this data in the future
+   */
+  const templates = [
+    // 'infant',
+    'bigbrain',
+    'colin',
+    'MNI152',
+    'waxholmRatV2_0',
+    'allenMouse'
+  ]
+  resolve(templates)
+})
+
+exports.getTemplate = (template) => new Promise((resolve, reject) => {
+  let filepath
+  if (process.env.NODE_ENV === 'production') {
+    filepath = path.join(__dirname, '..', 'res', `${template}.json`)
+  } else {
+    filepath = path.join(__dirname, '..', '..', 'src', 'res', 'ext', `${template}.json`)
+  }
+  fs.readFile(filepath, 'utf-8', (err, data) => {
+    if (err) reject(err)
+    resolve(data)
+  })
+})
\ No newline at end of file
diff --git a/deploy/test/mocha.test.js b/deploy/test/mocha.test.js
new file mode 100644
index 0000000000000000000000000000000000000000..8f55b54de1f2566b8d2348adef651303b0ef1fca
--- /dev/null
+++ b/deploy/test/mocha.test.js
@@ -0,0 +1 @@
+require('../auth/util.spec')
\ No newline at end of file
diff --git a/deploy/test/mocha.test.noenv.js b/deploy/test/mocha.test.noenv.js
new file mode 100644
index 0000000000000000000000000000000000000000..36a918fcdf251fd5bcf380d8859f9c711925496b
--- /dev/null
+++ b/deploy/test/mocha.test.noenv.js
@@ -0,0 +1 @@
+require("../auth/util_noenv.spec")
\ No newline at end of file
diff --git a/e2e/src/iv.e2e-spec.js b/e2e/src/iv.e2e-spec.js
new file mode 100644
index 0000000000000000000000000000000000000000..34fdb7c386b2c97e8f1858bd2c7356810bf0d820
--- /dev/null
+++ b/e2e/src/iv.e2e-spec.js
@@ -0,0 +1,98 @@
+const url = 'http://localhost:8081/'
+
+const noErrorLog = require('./noErrorLog')
+const { getSelectedTemplate, getSelectedParcellation } = require('./ivApi')
+const { getSearchParam, wait } = require('./util')
+
+describe('protractor works', () => {
+  it('protractor works', () => {
+    expect(true).toEqual(true)
+  })
+})
+
+describe('Home screen', () => {
+  beforeEach(() => {
+    browser.waitForAngularEnabled(false)
+    browser.get(url)
+  })
+
+  it('get title works', () => {
+    browser.getTitle()
+      .then(title => {
+        expect(title).toEqual('Interactive Atlas Viewer')
+      })
+      .catch(error => {
+        console.error('error', error)
+      })
+
+    browser.executeScript('window.interactiveViewer')
+      .then(result => expect(result).toBeDefined())
+    browser.executeScript('window.viewer')
+      .then(result => expect(result).toBeNull())
+
+    noErrorLog(browser)
+  })
+})
+
+describe('Query param: ', () => {
+
+  it('correctly defined templateSelected and selectedParcellation works', async () => {
+
+    const searchParam = '?templateSelected=MNI+Colin+27&parcellationSelected=JuBrain+Cytoarchitectonic+Atlas'
+    browser.get(url + searchParam)
+    browser.executeScript('window.interactiveViewer').then(result => expect(result).toBeDefined())
+    browser.executeScript('window.viewer').then(result => expect(result).toBeDefined())
+    
+    await wait(browser)
+
+    getSelectedTemplate(browser)
+      .then(template => {
+        expect(template.name).toBe('MNI Colin 27')
+      })
+
+    getSelectedParcellation(browser)
+      .then(parcellation => {
+        expect(parcellation.name).toBe('JuBrain Cytoarchitectonic Atlas')
+      })
+
+    noErrorLog(browser)
+  })
+
+  it('correctly defined templateSelected but incorrectly defined selectedParcellation work', async () => {
+    const searchParam = '?templateSelected=MNI+Colin+27&parcellationSelected=NoName'
+    browser.get(url + searchParam)
+
+    await wait(browser)
+
+    getSelectedTemplate(browser)
+      .then(template => {
+        expect(template.name).toBe('MNI Colin 27')
+      })
+      .catch(fail)
+
+    Promise.all([
+      getSelectedTemplate(browser),
+      getSelectedParcellation(browser)  
+    ])
+      .then(([template, parcellation]) => {
+        expect(parcellation.name).toBe(template.parcellations[0].name)
+      })
+      .catch(fail)
+      
+    noErrorLog(browser)
+  })
+
+  it('incorrectly defined templateSelected should clear searchparam', async () => {
+    const searchParam = '?templateSelected=NoName2&parcellationSelected=NoName'
+    browser.get(url + searchParam)
+
+    await wait(browser)
+
+    getSearchParam(browser)
+      .then(searchParam => {
+        const templateSelected = searchParam.get('templateSelected')
+        expect(templateSelected).toBeNull()
+      })
+      .catch(fail)
+  })
+})
\ No newline at end of file
diff --git a/e2e/src/ivApi.js b/e2e/src/ivApi.js
new file mode 100644
index 0000000000000000000000000000000000000000..825bee506911fff68b5dfd80924b73ed3f83df2f
--- /dev/null
+++ b/e2e/src/ivApi.js
@@ -0,0 +1,11 @@
+exports.getSelectedTemplate = (browser) => new Promise((resolve, reject) => {
+  browser.executeAsyncScript('let sub = window.interactiveViewer.metadata.selectedTemplateBSubject.subscribe(obj => arguments[arguments.length - 1](obj));sub.unsubscribe()')
+    .then(resolve)
+    .catch(reject)
+})
+
+exports.getSelectedParcellation = (browser) => new Promise((resolve, reject) => {
+  browser.executeAsyncScript('let sub = window.interactiveViewer.metadata.selectedParcellationBSubject.subscribe(obj => arguments[arguments.length - 1](obj));sub.unsubscribe()')
+    .then(resolve)
+    .catch(reject)
+})
\ No newline at end of file
diff --git a/e2e/src/niftilayer.e2e-spec.js b/e2e/src/niftilayer.e2e-spec.js
deleted file mode 100644
index 0fab4ed54b424adc59a5b1830f564500a186ca84..0000000000000000000000000000000000000000
--- a/e2e/src/niftilayer.e2e-spec.js
+++ /dev/null
@@ -1,25 +0,0 @@
-const url = 'http://localhost:8080/'
-
-describe('protractor works', () => {
-  it('getting title works', () => {
-    browser.get(url)
-    browser.getTitle().then(function(title){
-      expect(title).toBe('Interactive Atlas Viewer')
-      browser.executeScript('window.interactiveViewer').then(result => expect(result).toBeDefined())
-      browser.executeScript('window.viewer').then(result => expect(result).toBeNull())
-    })
-  })
-})
-
-describe('URL works', () => {
-
-  it('templateSelected existing template works', () => {
-
-    const searchParam = '?templateSelected=MNI+Colin+27'
-    browser.get(url + searchParam)
-    browser.executeScript('window.interactiveViewer').then(result => expect(result).toBeDefined())
-    browser.executeScript('window.viewer').then(result => expect(result).toBeDefined())
-    browser.executeAsyncScript('window.interactiveViewer.metadata.selectedTemplateBSubject.subscribe(obj => arguments[arguments.length - 1](obj))')
-      .then(obj => expect(obj.name).toBe('MNI Colin 27'))
-  })
-})
\ No newline at end of file
diff --git a/e2e/src/noErrorLog.js b/e2e/src/noErrorLog.js
new file mode 100644
index 0000000000000000000000000000000000000000..d7bc1ef7dc223d3f66aece5994623fbaea239823
--- /dev/null
+++ b/e2e/src/noErrorLog.js
@@ -0,0 +1,14 @@
+module.exports = (browser) => {
+  if (!browser) {
+    fail('browser not defined')
+    throw new Error('browser not defined')
+  }
+  browser.manage().logs().get('browser')
+    .then(browserLogs => {
+      const errorLogs = browserLogs
+        .filter(log => !(/favicon/.test(log.message)))
+        .filter(log => log.level.value > 900)
+      expect(errorLogs.length).toEqual(0)
+    })
+    .catch(fail)
+}
\ No newline at end of file
diff --git a/e2e/src/util.js b/e2e/src/util.js
new file mode 100644
index 0000000000000000000000000000000000000000..030f2ccad62151ce4392c95704b211d57fb7af43
--- /dev/null
+++ b/e2e/src/util.js
@@ -0,0 +1,20 @@
+const {URLSearchParams} = require('url')
+
+exports.getSearchParam = (browser) => {
+  const script = `
+  const search = window.location.search;
+  return search
+  `
+  return browser.executeScript(script)
+    .then(search => new URLSearchParams(search))
+}
+
+exports.wait = (browser) => new Promise(resolve => {
+
+  /**
+   * TODO figure out how to wait until the select parcellation is populated
+   */
+    
+  browser.sleep(1000)
+    .then(resolve)
+})
\ No newline at end of file
diff --git a/package.json b/package.json
index 1ed24602b17406726b4cf9a24183da38b1d1b097..b49118e6391468c7484a242ccf1757fdb1c90abd 100644
--- a/package.json
+++ b/package.json
@@ -16,56 +16,70 @@
     "dev-server-aot": "PRODUCTION=true GIT_HASH=`git log --pretty=format:'%h' --invert-grep --grep=^.ignore -1` webpack-dev-server --config webpack.aot.js",
     "dev-server-all-interfaces": "webpack-dev-server --config webpack.dev.js --mode development --hot --host 0.0.0.0",
     "serve-plugins": "node src/plugin_examples/server.js",
-    "test": "karma start spec/karma.conf.js"
+    "test": "karma start spec/karma.conf.js",
+    "e2e": "protractor e2e/protractor.conf"
   },
   "keywords": [],
   "author": "",
   "license": "ISC",
   "devDependencies": {
-    "@angular/animations": "^6.0.7",
-    "@angular/common": "^6.0.7",
-    "@angular/compiler": "^6.0.7",
-    "@angular/compiler-cli": "^6.0.3",
-    "@angular/core": "^6.0.3",
-    "@angular/elements": "^6.0.7",
-    "@angular/forms": "^6.0.7",
-    "@angular/http": "^6.0.7",
-    "@angular/platform-browser": "^6.0.7",
-    "@angular/platform-browser-dynamic": "^6.0.7",
+    "@angular/animations": "^7.2.15",
+    "@angular/common": "^7.2.15",
+    "@angular/compiler": "^7.2.15",
+    "@angular/compiler-cli": "^7.2.15",
+    "@angular/core": "^7.2.15",
+    "@angular/elements": "^7.2.15",
+    "@angular/forms": "^7.2.15",
+    "@angular/http": "^7.2.15",
+    "@angular/language-service": "^7.2.15",
+    "@angular/platform-browser": "^7.2.15",
+    "@angular/platform-browser-dynamic": "^7.2.15",
     "@ngrx/store": "^6.0.1",
     "@ngtools/webpack": "^6.0.5",
     "@types/chart.js": "^2.7.20",
-    "@types/jasmine": "^2.8.8",
+    "@types/jasmine": "^3.3.12",
+    "@types/jasminewd2": "^2.0.6",
+    "@types/node": "^12.0.0",
     "@types/webpack-env": "^1.13.6",
     "angular2-template-loader": "^0.6.2",
     "chart.js": "^2.7.2",
-    "core-js": "^2.5.6",
+    "codelyzer": "^5.0.1",
+    "core-js": "^3.0.1",
     "file-loader": "^1.1.11",
     "html-webpack-plugin": "^3.2.0",
     "jasmine": "^3.1.0",
-    "jasmine-core": "^3.1.0",
-    "karma": "^2.0.5",
+    "jasmine-core": "^3.4.0",
+    "jasmine-spec-reporter": "^4.2.1",
+    "karma": "^4.1.0",
     "karma-chrome-launcher": "^2.2.0",
-    "karma-jasmine": "^1.1.2",
+    "karma-cli": "^2.0.0",
+    "karma-jasmine": "^2.0.1",
     "karma-typescript": "^3.0.13",
     "karma-webpack": "^3.0.0",
     "lodash.merge": "^4.6.1",
     "ng2-charts": "^1.6.0",
     "ngx-bootstrap": "3.0.1",
-    "protractor": "^5.4.0",
+    "protractor": "^5.4.2",
     "raw-loader": "^0.5.1",
     "reflect-metadata": "^0.1.12",
-    "rxjs": "6.2.2",
+    "rxjs": "6.5.1",
     "showdown": "^1.8.6",
     "ts-loader": "^4.3.0",
-    "typescript": "^2.7.2",
+    "ts-node": "^8.1.0",
+    "tslint": "^5.16.0",
+    "typescript": "3.2",
     "uglifyjs-webpack-plugin": "^1.2.5",
     "webpack": "^4.25.0",
-    "webpack-cli": "^3.1.2",
+    "webpack-cli": "^3.3.2",
     "webpack-closure-compiler": "^2.1.6",
     "webpack-dev-server": "^3.1.4",
-    "webpack-merge": "^4.1.2",
-    "zone.js": "^0.8.26"
+    "webpack-merge": "^4.1.2"
   },
-  "dependencies": {}
+  "dependencies": {
+    "@angular/cdk": "^7.3.7",
+    "@angular/material": "^7.3.7",
+    "@angular/router": "^7.2.15",
+    "hammerjs": "^2.0.8",
+    "zone.js": "^0.9.1"
+  }
 }
diff --git a/src/atlasViewer/atlasViewer.apiService.service.ts b/src/atlasViewer/atlasViewer.apiService.service.ts
index c040cde554a6852933ee323bace0d2e409d649ad..bdaf63c10763e0cf58075995cb2f570262a40552 100644
--- a/src/atlasViewer/atlasViewer.apiService.service.ts
+++ b/src/atlasViewer/atlasViewer.apiService.service.ts
@@ -1,12 +1,13 @@
 import { Injectable } from "@angular/core";
 import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, safeFilter, getLabelIndexMap, isDefined } from "../services/stateStore.service";
+import { ViewerStateInterface, safeFilter, getLabelIndexMap, isDefined } from "src/services/stateStore.service";
 import { Observable } from "rxjs";
 import { map, distinctUntilChanged, filter } from "rxjs/operators";
 import { BsModalService } from "ngx-bootstrap/modal";
 import { ModalUnit } from "./modalUnit/modalUnit.component";
 import { ModalHandler } from "../util/pluginHandlerClasses/modalHandler";
 import { ToastHandler } from "../util/pluginHandlerClasses/toastHandler";
+import { PluginManifest } from "./atlasViewer.pluginService.service";
 
 declare var window
 
@@ -107,6 +108,13 @@ export class AtlasViewerAPIServices{
         /* to be overwritten by atlasViewer.component.ts */
         getToastHandler : () => {
           throw new Error('getToast Handler not overwritten by atlasViewer.component.ts')
+        },
+
+        /**
+         * to be overwritten by atlas
+         */
+        launchNewWidget: (manifest) => {
+          return Promise.reject('Needs to be overwritted')
         }
       },
       pluginControl : {
@@ -119,6 +127,11 @@ export class AtlasViewerAPIServices{
     }
     window['interactiveViewer'] = this.interactiveViewer
     this.init()
+
+    /**
+     * TODO debugger debug
+     */
+    window.uiHandle = this.interactiveViewer.uiHandle
   }
 
   private init(){
@@ -164,8 +177,9 @@ export interface InteractiveViewerInterface{
   }
 
   uiHandle : {
-    getModalHandler : () => ModalHandler
-    getToastHandler : () => ToastHandler
+    getModalHandler: () => ModalHandler
+    getToastHandler: () => ToastHandler
+    launchNewWidget: (manifest:PluginManifest) => Promise<any>
   }
 
   pluginControl : {
diff --git a/src/atlasViewer/atlasViewer.component.ts b/src/atlasViewer/atlasViewer.component.ts
index ecc667d7a16f1cd02af990f4547ba9b074e89d24..c6f4628f9c5976923731a17d8dff513d62c4f19f 100644
--- a/src/atlasViewer/atlasViewer.component.ts
+++ b/src/atlasViewer/atlasViewer.component.ts
@@ -1,25 +1,24 @@
-import { Component, HostBinding, ViewChild, ViewContainerRef, ComponentFactoryResolver, ComponentFactory, OnDestroy, ElementRef, ComponentRef, AfterViewInit, OnInit, HostListener, Renderer2, TemplateRef } from "@angular/core";
-import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, isDefined, FETCHED_SPATIAL_DATA, UPDATE_SPATIAL_DATA, TOGGLE_SIDE_PANEL, safeFilter } from "../services/stateStore.service";
-import { Observable, Subscription, combineLatest } from "rxjs";
-import { map, filter, distinctUntilChanged, delay, concatMap } from "rxjs/operators";
+import { Component, HostBinding, ViewChild, ViewContainerRef, OnDestroy, OnInit, TemplateRef, AfterViewInit } from "@angular/core";
+import { Store, select, ActionsSubject } from "@ngrx/store";
+import { ViewerStateInterface, isDefined, FETCHED_SPATIAL_DATA, UPDATE_SPATIAL_DATA, TOGGLE_SIDE_PANEL, safeFilter, UIStateInterface, OPEN_SIDE_PANEL, CLOSE_SIDE_PANEL } from "../services/stateStore.service";
+import { Observable, Subscription, combineLatest, interval, merge, of } from "rxjs";
+import { map, filter, distinctUntilChanged, delay, concatMap, debounceTime, withLatestFrom } from "rxjs/operators";
 import { AtlasViewerDataService } from "./atlasViewer.dataService.service";
 import { WidgetServices } from "./widgetUnit/widgetService.service";
 import { LayoutMainSide } from "../layouts/mainside/mainside.component";
-import { Chart } from 'chart.js'
-import { AtlasViewerConstantsServices, SUPPORT_LIBRARY_MAP } from "./atlasViewer.constantService.service";
+import { AtlasViewerConstantsServices, UNSUPPORTED_PREVIEW, UNSUPPORTED_INTERVAL } from "./atlasViewer.constantService.service";
 import { BsModalService } from "ngx-bootstrap/modal";
 import { ModalUnit } from "./modalUnit/modalUnit.component";
 import { AtlasViewerURLService } from "./atlasViewer.urlService.service";
-import { ToastComponent } from "../components/toast/toast.component";
 import { AtlasViewerAPIServices } from "./atlasViewer.apiService.service";
-import { PluginServices } from "./atlasViewer.pluginService.service";
 
 import '../res/css/extra_styles.css'
 import { NehubaContainer } from "../ui/nehubaContainer/nehubaContainer.component";
-import { ToastHandler } from "../util/pluginHandlerClasses/toastHandler";
-import { colorAnimation } from "./atlasViewer.animation";
-import { ToastService, defaultToastConfig } from "../services/toastService.service";
+import { colorAnimation } from "./atlasViewer.animation"
+import { FixedMouseContextualContainerDirective } from "src/util/directives/FixedMouseContextualContainerDirective.directive";
+import { DatabrowserService } from "src/ui/databrowserModule/databrowser.service";
+import { AGREE_COOKIE, AGREE_KG_TOS, SHOW_KG_TOS } from "src/services/state/uiState.store";
+import { TabsetComponent } from "ngx-bootstrap/tabs";
 
 @Component({
   selector: 'atlas-viewer',
@@ -34,31 +33,42 @@ import { ToastService, defaultToastConfig } from "../services/toastService.servi
 
 export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
 
-  @ViewChild('dockedContainer', { read: ViewContainerRef }) dockedContainer: ViewContainerRef
-  @ViewChild('floatingContainer', { read: ViewContainerRef }) floatingContainer: ViewContainerRef
-  @ViewChild('databrowser', { read: ElementRef }) databrowser: ElementRef
-  @ViewChild('toastContainer', { read: ViewContainerRef }) toastContainer: ViewContainerRef
   @ViewChild('floatingMouseContextualContainer', { read: ViewContainerRef }) floatingMouseContextualContainer: ViewContainerRef
-  @ViewChild('pluginFactory', { read: ViewContainerRef }) pluginViewContainerRef: ViewContainerRef
+  @ViewChild('helpComponent', {read: TemplateRef}) helpComponent : TemplateRef<any>
+  @ViewChild('signinModalComponent', {read: TemplateRef}) signinModalComponent : TemplateRef<any>
+  @ViewChild('cookieAgreementComponent', {read: TemplateRef}) cookieAgreementComponent : TemplateRef<any>
+  @ViewChild('kgToS', {read: TemplateRef}) kgTosComponent: TemplateRef<any>
   @ViewChild(LayoutMainSide) layoutMainSide: LayoutMainSide
 
   @ViewChild(NehubaContainer) nehubaContainer: NehubaContainer
 
+  @ViewChild(FixedMouseContextualContainerDirective) rClContextualMenu: FixedMouseContextualContainerDirective
+
+  @ViewChild('mobileMenuTabs') mobileMenuTabs: TabsetComponent
+
+  /**
+   * required for styling of all child components
+   */
   @HostBinding('attr.darktheme')
   darktheme: boolean = false
 
-  meetsRequirement: boolean = true
+  @HostBinding('attr.ismobile')
+  get isMobile(){
+    return this.constantsService.mobile
+  }
 
-  toastComponentFactory: ComponentFactory<ToastComponent>
-  private dedicatedViewComponentRef: ComponentRef<ToastComponent>
+  meetsRequirement: boolean = true
 
   public sidePanelView$: Observable<string|null>
   private newViewer$: Observable<any>
 
+  public selectedRegions$: Observable<any[]>
   public selectedPOI$ : Observable<any[]>
+  private showHelp$: Observable<any>
 
   public dedicatedView$: Observable<string | null>
-  public onhoverSegment$: Observable<string>
+  public onhoverSegments$: Observable<string[]>
+  public onhoverSegmentsForFixed$: Observable<string[]>
   public onhoverLandmark$ : Observable<string | null>
   private subscriptions: Subscription[] = []
 
@@ -70,25 +80,26 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
   public ngLayers : NgLayerInterface[]
   private disposeHandler : any
 
+  public unsupportedPreviewIdx: number = 0
+  public unsupportedPreviews: any[] = UNSUPPORTED_PREVIEW
+
+  public sidePanelOpen$: Observable<boolean>
+
   get toggleMessage(){
     return this.constantsService.toggleMessage
   }
 
   constructor(
-    private toastService:ToastService,
-    private pluginService: PluginServices,
-    private rd2: Renderer2,
     private store: Store<ViewerStateInterface>,
     public dataService: AtlasViewerDataService,
-    private cfr: ComponentFactoryResolver,
     private widgetServices: WidgetServices,
     private constantsService: AtlasViewerConstantsServices,
     public urlService: AtlasViewerURLService,
     public apiService: AtlasViewerAPIServices,
-    private modalService: BsModalService
+    private modalService: BsModalService,
+    private databrowserService: DatabrowserService,
+    private dispatcher$: ActionsSubject
   ) {
-    this.toastComponentFactory = this.cfr.resolveComponentFactory(ToastComponent)
-
     this.ngLayerNames$ = this.store.pipe(
       select('viewerState'),
       filter(state => isDefined(state) && isDefined(state.templateSelected)),
@@ -103,13 +114,25 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
       map(state => state.focusedSidePanel)
     )
 
+    this.sidePanelOpen$ = this.store.pipe(
+      select('uiState'),  
+      filter(state => isDefined(state)),
+      map(state => state.sidePanelOpen)
+    )
+
+    this.showHelp$ = this.constantsService.showHelpSubject$.pipe(
+      debounceTime(170)
+    )
+
+    this.selectedRegions$ = this.store.pipe(
+      select('viewerState'),
+      filter(state=>isDefined(state)&&isDefined(state.regionsSelected)),
+      map(state=>state.regionsSelected),
+      distinctUntilChanged()
+    )
+
     this.selectedPOI$ = combineLatest(
-      this.store.pipe(
-        select('viewerState'),
-        filter(state=>isDefined(state)&&isDefined(state.regionsSelected)),
-        map(state=>state.regionsSelected),
-        distinctUntilChanged()
-      ),
+      this.selectedRegions$,
       this.store.pipe(
         select('viewerState'),
         filter(state => isDefined(state) && isDefined(state.landmarksSelected)),
@@ -156,144 +179,83 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
     )
 
     // TODO temporary hack. even though the front octant is hidden, it seems if a mesh is present, hover will select the said mesh
-    this.onhoverSegment$ = combineLatest(
+    this.onhoverSegments$ = combineLatest(
       this.store.pipe(
         select('uiState'),
+        select('mouseOverSegments'),
+        filter(v => !!v),
+        distinctUntilChanged((o, n) => o.length === n.length && n.every(segment => o.find(oSegment => oSegment.layer.name === segment.layer.name && oSegment.segment === segment.segment) ) )
         /* cannot filter by state, as the template expects a default value, or it will throw ExpressionChangedAfterItHasBeenCheckedError */
-        map(state => isDefined(state) ?
-          state.mouseOverSegment ?
-            state.mouseOverSegment.constructor === Number ?
-              state.mouseOverSegment.toString() :
-              state.mouseOverSegment.name :
-            null :
-          null),
-        distinctUntilChanged()
+
       ),
       this.onhoverLandmark$
     ).pipe(
-      map(([segment, onhoverLandmark]) => onhoverLandmark ? null : segment )
+      map(([segments, onhoverLandmark]) => onhoverLandmark ? null : segments ),
+      map(segments => !segments || segments.length === 0
+          ? null
+          : segments.map(s => s.segment) )
     )
 
-  }
+    this.selectedParcellation$ = this.store.pipe(
+      select('viewerState'),
+      safeFilter('parcellationSelected'),
+      map(state=>state.parcellationSelected),
+      distinctUntilChanged(),
+    )
 
-  ngOnInit() {
+    this.subscriptions.push(
+      this.selectedParcellation$.subscribe(parcellation => this.selectedParcellation = parcellation)
+    )
 
-    this.toastService.showToast = (message, config) => {
-      const _config = Object.assign({}, defaultToastConfig, config
-        ? config
-        : {})
-      const toastComponent = this.toastContainer.createComponent(this.toastComponentFactory)
-      if(typeof message === 'string')
-        toastComponent.instance.message = message
-      if(message instanceof TemplateRef){
-        toastComponent.instance.messageContainer.createEmbeddedView(message as TemplateRef<any>)
-      }
-         
-      toastComponent.instance.dismissable = _config.dismissable
-      toastComponent.instance.timeout = _config.timeout
-
-      let subscription
-
-      const dismissToast = () => {
-        if(subscription) subscription.unsubscribe()
-        toastComponent.destroy()
-      }
-
-      subscription = toastComponent.instance.dismissed.subscribe(dismissToast)
-      return dismissToast
-    }
+    this.subscriptions.push(
+      this.newViewer$.subscribe(template => this.selectedTemplate = template)
+    )
+  }
 
-    this.apiService.interactiveViewer.uiHandle.getToastHandler = () => {
-      const handler = new ToastHandler()
-      let toastComponent:ComponentRef<ToastComponent>
-      handler.show = () => {
-        toastComponent = this.toastContainer.createComponent(this.toastComponentFactory)
+  private selectedParcellation$: Observable<any>
+  private selectedParcellation: any
 
-        toastComponent.instance.dismissable = handler.dismissable
-        toastComponent.instance.message = handler.message
-        toastComponent.instance.timeout = handler.timeout
+  ngOnInit() {
+    this.meetsRequirement = this.meetsRequirements()
 
-        const _subscription = toastComponent.instance.dismissed.subscribe(userInitiated => {
-          _subscription.unsubscribe()
-          handler.hide()
+    if (!this.meetsRequirement) {
+      merge(
+        of(-1),
+        interval(UNSUPPORTED_INTERVAL)
+      ).pipe(
+        map(v => {
+          let idx = v
+          while (idx < 0) {
+            idx = v + this.unsupportedPreviews.length
+          }
+          return idx % this.unsupportedPreviews.length
         })
-      }
-
-      handler.hide = () => {
-        if(toastComponent){
-          toastComponent.destroy()
-          toastComponent = null
-        }
-      }
-
-      return handler
+      ).subscribe(val => {
+        this.unsupportedPreviewIdx = val
+      })
     }
 
-    this.apiService.interactiveViewer.pluginControl.loadExternalLibraries = (libraries: string[]) => new Promise((resolve, reject) => {
-      const srcHTMLElement = libraries.map(libraryName => ({
-        name: libraryName,
-        srcEl: SUPPORT_LIBRARY_MAP.get(libraryName)
-      }))
-
-      const rejected = srcHTMLElement.filter(scriptObj => scriptObj.srcEl === null)
-      if (rejected.length > 0)
-        return reject(`Some library names cannot be recognised. No libraries were loaded: ${rejected.map(srcObj => srcObj.name).join(', ')}`)
-
-      Promise.all(srcHTMLElement.map(scriptObj => new Promise((rs, rj) => {
-        if('customElements' in window && scriptObj.name === 'webcomponentsLite'){
-          return rs()
-        }
-        const existingEntry = this.apiService.loadedLibraries.get(scriptObj.name)
-        if (existingEntry) {
-          this.apiService.loadedLibraries.set(scriptObj.name, { counter: existingEntry.counter + 1, src: existingEntry.src })
-          rs()
-        } else {
-          const srcEl = scriptObj.srcEl
-          srcEl.onload = () => rs()
-          srcEl.onerror = (e: any) => rj(e)
-          this.rd2.appendChild(document.head, srcEl)
-          this.apiService.loadedLibraries.set(scriptObj.name, { counter: 1, src: srcEl })
-        }
-      })))
-        .then(() => resolve())
-        .catch(e => (console.warn(e), reject(e)))
-    })
-
-    this.apiService.interactiveViewer.pluginControl.unloadExternalLibraries = (libraries: string[]) =>
-      libraries
-        .filter((stringname) => SUPPORT_LIBRARY_MAP.get(stringname) !== null)
-        .forEach(libname => {
-          const ledger = this.apiService.loadedLibraries.get(libname!)
-          if (!ledger) {
-            console.warn('unload external libraries error. cannot find ledger entry...', libname, this.apiService.loadedLibraries)
-            return
-          }
-          if (ledger.src === null) {
-            console.log('webcomponents is native supported. no library needs to be unloaded')
-            return
-          }
-
-          if (ledger.counter - 1 == 0) {
-            this.rd2.removeChild(document.head, ledger.src)
-            this.apiService.loadedLibraries.delete(libname!)
-          } else {
-            this.apiService.loadedLibraries.set(libname!, { counter: ledger.counter - 1, src: ledger.src })
+    this.subscriptions.push(
+      this.showHelp$.subscribe(() => 
+        this.modalService.show(ModalUnit, {
+          initialState: {
+            title: this.constantsService.showHelpTitle,
+            template: this.helpComponent
           }
         })
-
-    this.meetsRequirement = this.meetsRequirements()
+      )
+    )
 
     this.subscriptions.push(
-      this.dedicatedView$.subscribe(string => {
-        if (string === null) {
-          if (this.dedicatedViewComponentRef)
-            this.dedicatedViewComponentRef.destroy()
-          return
-        }
-        this.dedicatedViewComponentRef = this.toastContainer.createComponent(this.toastComponentFactory)
-        this.dedicatedViewComponentRef.instance.message = `hello`
-        this.dedicatedViewComponentRef.instance.dismissable = true
-        this.dedicatedViewComponentRef.instance.timeout = 1000
+      this.constantsService.showSigninSubject$.pipe(
+        debounceTime(160)
+      ).subscribe(user => {
+        this.modalService.show(ModalUnit, {
+          initialState: {
+            title: user ? 'Logout' : `Login`,
+            template: this.signinModalComponent
+          }
+        })
       })
     )
 
@@ -313,6 +275,8 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
           template.useTheme === 'dark' :
           false
 
+        this.constantsService.darktheme = this.darktheme
+        
         /* new viewer should reset the spatial data search */
         this.store.dispatch({
           type : FETCHED_SPATIAL_DATA,
@@ -332,92 +296,72 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
         filter(() => typeof this.layoutMainSide !== 'undefined')
       ).subscribe(v => this.layoutMainSide.showSide =  isDefined(v))
     )
+  }
 
+  ngAfterViewInit() {
+    
     /**
-     * Because there is no easy way to display standard deviation natively, use a plugin 
-     * */
-    Chart.pluginService.register({
-
-      /* patching background color fill, so saved images do not look completely white */
-      beforeDraw: (chart) => {
-        const ctx = chart.ctx as CanvasRenderingContext2D;
-        ctx.fillStyle = this.darktheme ?
-          `rgba(50,50,50,0.8)` :
-          `rgba(255,255,255,0.8)`
-
-        if (chart.canvas) ctx.fillRect(0, 0, chart.canvas.width, chart.canvas.height)
-
-      },
-
-      /* patching standard deviation for polar (potentially also line/bar etc) graph */
-      afterInit: (chart) => {
-        if (chart.config.options && chart.config.options.tooltips) {
-
-          chart.config.options.tooltips.callbacks = {
-            label: function (tooltipItem, data) {
-              let sdValue
-              if (data.datasets && typeof tooltipItem.datasetIndex != 'undefined' && data.datasets[tooltipItem.datasetIndex].label) {
-                const sdLabel = data.datasets[tooltipItem.datasetIndex].label + '_sd'
-                const sd = data.datasets.find(dataset => typeof dataset.label != 'undefined' && dataset.label == sdLabel)
-                if (sd && sd.data && typeof tooltipItem.index != 'undefined' && typeof tooltipItem.yLabel != 'undefined') sdValue = Number(sd.data[tooltipItem.index]) - Number(tooltipItem.yLabel)
-              }
-              return `${tooltipItem.yLabel} ${sdValue ? '(' + sdValue + ')' : ''}`
-            }
-          }
+     * Show Cookie disclaimer if not yet agreed
+     */
+    /**
+     * TODO avoid creating new views in lifecycle hooks in general
+     */
+    this.store.pipe(
+      select('uiState'),
+      select('agreedCookies'),
+      filter(agreed => !agreed),
+      delay(0)
+    ).subscribe(() => {
+      this.modalService.show(ModalUnit, {
+        initialState: {
+          title: 'Cookie Disclaimer',
+          template: this.cookieAgreementComponent
         }
-        if (chart.data.datasets) {
-          chart.data.datasets = chart.data.datasets
-            .map(dataset => {
-              if (dataset.label && /\_sd$/.test(dataset.label)) {
-                const originalDS = chart.data.datasets!.find(baseDS => typeof baseDS.label !== 'undefined' && (baseDS.label == dataset.label!.replace(/_sd$/, '')))
-                if (originalDS) {
-                  return Object.assign({}, dataset, {
-                    data: (originalDS.data as number[]).map((datapoint, idx) => (Number(datapoint) + Number((dataset.data as number[])[idx]))),
-                    ... this.constantsService.chartSdStyle
-                  })
-                } else {
-                  return dataset
-                }
-              } else if (dataset.label) {
-                const sdDS = chart.data.datasets!.find(sdDS => typeof sdDS.label !== 'undefined' && (sdDS.label == dataset.label + '_sd'))
-                if (sdDS) {
-                  return Object.assign({}, dataset, {
-                    ...this.constantsService.chartBaseStyle
-                  })
-                } else {
-                  return dataset
-                }
-              } else {
-                return dataset
-              }
-            })
+      }) 
+    })
+
+    this.dispatcher$.pipe(
+      filter(({type}) => type === SHOW_KG_TOS),
+      withLatestFrom(this.store.pipe(
+        select('uiState'),
+        select('agreedKgTos')
+      )),
+      map(([_, agreed]) => agreed),
+      filter(flag => !flag),
+      delay(0)
+    ).subscribe(val => {
+      this.modalService.show(ModalUnit, {
+        initialState: {
+          title: 'Knowldge Graph ToS',
+          template: this.kgTosComponent
         }
-      }
+      })
     })
+
+    this.onhoverSegmentsForFixed$ = this.rClContextualMenu.onShow.pipe(
+      withLatestFrom(this.onhoverSegments$),
+      map(([_flag, onhoverSegments]) => onhoverSegments || [])
+    )
   }
 
+  /**
+   * For completeness sake. Root element should never be destroyed. 
+   */
   ngOnDestroy() {
     this.subscriptions.forEach(s => s.unsubscribe())
   }
 
-  ngAfterViewInit() {
-    this.widgetServices.floatingContainer = this.floatingContainer
-    this.widgetServices.dockedContainer = this.dockedContainer
-
-    this.pluginService.pluginViewContainerRef = this.pluginViewContainerRef
-    this.pluginService.appendSrc = (src: HTMLElement) => this.rd2.appendChild(document.head, src)
-  }
-
   /**
    * perhaps move this to constructor?
    */
   meetsRequirements() {
 
+    const testFirefox = /Firefox\/([0-9]*?)\.[0-9]*?/.exec(navigator.userAgent)
+    if (testFirefox && Number(testFirefox[1]) >= 67)
+      return false
+
     const canvas = document.createElement('canvas')
     const gl = canvas.getContext('webgl2') as WebGLRenderingContext
-    const message: any = {
-      Error: this.constantsService.minReqModalHeader
-    }
 
     if (!gl) {
       return false
@@ -451,12 +395,35 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
       }) as NgLayerInterface)
   }
 
+  kgTosClickedOk(){
+    this.modalService.hide(1)
+    this.store.dispatch({
+      type: AGREE_KG_TOS
+    })
+  }
+
+  cookieClickedOk(){
+    this.modalService.hide(1)
+    this.store.dispatch({
+      type: AGREE_COOKIE
+    })
+  }
+
   panelAnimationEnd(){
 
     if( this.nehubaContainer && this.nehubaContainer.nehubaViewer && this.nehubaContainer.nehubaViewer.nehubaViewer )
       this.nehubaContainer.nehubaViewer.nehubaViewer.redraw()
   }
 
+  nehubaClickHandler(event:MouseEvent){
+    if (!this.rClContextualMenu) return
+    this.rClContextualMenu.mousePos = [
+      event.clientX,
+      event.clientY
+    ]
+    this.rClContextualMenu.show()
+  }
+
   toggleSidePanel(panelName:string){
     this.store.dispatch({
       type : TOGGLE_SIDE_PANEL,
@@ -464,22 +431,35 @@ export class AtlasViewer implements OnDestroy, OnInit, AfterViewInit {
     })
   }
 
-  mousePos: [number, number] = [0, 0]
-
-  @HostListener('mousemove', ['$event'])
-  mousemove(event: MouseEvent) {
-    this.mousePos = [event.clientX, event.clientY]
+  private selectedTemplate: any
+  searchRegion(regions:any[]){
+    this.rClContextualMenu.hide()
+    this.databrowserService.queryData({ regions, parcellation: this.selectedParcellation, template: this.selectedTemplate })
+    if (this.isMobile) {
+      this.store.dispatch({
+        type : OPEN_SIDE_PANEL
+      })
+      this.mobileMenuTabs.tabs[1].active = true
+    }
   }
 
   @HostBinding('attr.version')
   public _version : string = VERSION
 
-  get floatingMouseContextualContainerTransform() {
-    return `translate(${this.mousePos[0]}px,${this.mousePos[1]}px)`
-  }
-
-  get isMobile(){
-    return this.constantsService.mobile
+  changeMenuState({open, close}:{open?:boolean, close?:boolean} = {}) {
+    if (open) {
+      return this.store.dispatch({
+        type: OPEN_SIDE_PANEL
+      })
+    }
+    if (close) {
+      return this.store.dispatch({
+        type: CLOSE_SIDE_PANEL
+      })
+    }
+    this.store.dispatch({
+      type: TOGGLE_SIDE_PANEL
+    })
   }
 }
 
diff --git a/src/atlasViewer/atlasViewer.constantService.service.ts b/src/atlasViewer/atlasViewer.constantService.service.ts
index 19e5dd73901b6094e12f5f7ba2f500ca98e0f797..c1b7b63e9bb2230032c5e6c73e6bb2037d90a31a 100644
--- a/src/atlasViewer/atlasViewer.constantService.service.ts
+++ b/src/atlasViewer/atlasViewer.constantService.service.ts
@@ -1,7 +1,11 @@
 import { Injectable } from "@angular/core";
 import { Store } from "@ngrx/store";
 import { ViewerStateInterface, Property, FETCHED_METADATA } from "../services/stateStore.service";
+import { Subject } from "rxjs";
+import { ACTION_TYPES, ViewerConfiguration } from 'src/services/state/viewerConfig.store'
 
+export const CM_THRESHOLD = `0.05`
+export const CM_MATLAB_JET = `float r;if( x < 0.7 ){r = 4.0 * x - 1.5;} else {r = -4.0 * x + 4.5;}float g;if (x < 0.5) {g = 4.0 * x - 0.5;} else {g = -4.0 * x + 3.5;}float b;if (x < 0.3) {b = 4.0 * x + 0.5;} else {b = -4.0 * x + 2.5;}float a = 1.0;`
 
 @Injectable({
   providedIn : 'root'
@@ -9,13 +13,16 @@ import { ViewerStateInterface, Property, FETCHED_METADATA } from "../services/st
 
 export class AtlasViewerConstantsServices{
 
+  public darktheme: boolean = false
   public mobile: boolean
   public loadExportNehubaPromise : Promise<boolean>
 
+  public getActiveColorMapFragmentMain = ():string=>`void main(){float x = toNormalized(getDataValue());${CM_MATLAB_JET}if(x>${CM_THRESHOLD}){emitRGB(vec3(r,g,b));}else{emitTransparent();}}`
+
   public ngLandmarkLayerName = 'spatial landmark layer'
   public ngUserLandmarkLayerName = 'user landmark layer'
 
-  public citationToastDuration = 7000
+  public citationToastDuration = 7e3
 
   /**
    * optimized for nehubaConfig.layout.useNehubaPerspective.fixedZoomPerspectiveSlices
@@ -25,7 +32,10 @@ export class AtlasViewerConstantsServices{
    */
   public nehubaLandmarkConstant = 1e-8
 
-  private TIMEOUT = 4000
+  /**
+   * Timeout can be longer, since configs are lazy loaded.
+   */
+  private TIMEOUT = 16000
 
   /**
    * raceFetch 
@@ -44,14 +54,30 @@ export class AtlasViewerConstantsServices{
       prevLandmarks.length === newLandmarks.length
   }
 
+  public backendUrl = BACKEND_URL
+
   /* to be provided by KG in future */
-  public templateUrls = [
+  public templateUrlsPr : Promise<string[]> = new Promise((resolve, reject) => {
+    fetch(`${this.backendUrl}templates`)
+      .then(res => res.json())
+      .then(arr => {
+        this.templateUrls = arr
+        return arr
+      })
+      .then(resolve)
+      .catch(reject)
+  })
+
+  public templateUrls = Array(100)
+
+  private _templateUrls = [
     // 'res/json/infant.json',
     'res/json/bigbrain.json',
     'res/json/colin.json',
     'res/json/MNI152.json',
     'res/json/waxholmRatV2_0.json',
-    'res/json/allenMouse.json'
+    'res/json/allenMouse.json',
+    // 'res/json/test.json'
   ]
 
   /* to be provided by KG in future */
@@ -69,7 +95,7 @@ export class AtlasViewerConstantsServices{
       ]
     ],
     [
-      'Allen Mouse Brain Atlas',
+      'Allen adult mouse brain reference atlas V3 Brain Atlas',
       [
         'res/json/allenAggregated.json'
       ]
@@ -107,6 +133,13 @@ export class AtlasViewerConstantsServices{
     pointHitRadius : 0,
   }
 
+  public minReqExplaner = `
+  - Interactive atlas viewer requires **webgl2.0**, and the \`EXT_color_buffer_float\` extension enabled.
+  - You can check browsers' support of webgl2.0 by visiting <https://caniuse.com/#feat=webgl2>
+  - Unfortunately, Safari and iOS devices currently do not support **webgl2.0**: <https://webkit.org/status/#specification-webgl-2>
+  - Mozilla Firefox v67 introduced [a conflict with neuroglancer](https://github.com/google/neuroglancer/issues/138). Until this is fixed, it is advised to use chrome for the interactive atlas viewer.
+  `
+
   public minReqMD = `
 # Hmm... it seems like we hit a snag
 It seems your browser has trouble loading interactive atlas viewer. 
@@ -130,7 +163,81 @@ Interactive atlas viewer requires **webgl2.0**, and the \`EXT_color_buffer_float
   /**
    * message when user on hover a segment or landmark
    */
-  public toggleMessage: string = 'double click to toggle select'
+  public toggleMessage: string = 'double click to toggle select, right click to search'
+
+  /**
+   * observable for showing login modal
+   */
+  public showSigninSubject$: Subject<any> = new Subject()
+
+  /**
+   * Observable for showing config modal
+   */
+  public showConfigTitle: String = 'Settings'
+  /**
+   * Observable for showing help modal
+   */
+  public showHelpSubject$: Subject<null> = new Subject()
+  public showHelpTitle: String = 'About'
+
+  private showHelpGeneralMobile = [
+    ['hold 🌏 + ↕', 'change oblique slice mode'],
+    ['hold 🌏 + ↔', 'oblique slice']
+  ]
+  private showHelpGeneralDesktop = [
+    ['num keys [0-9]', 'toggle layer visibility [0-9]'],
+    ['h', 'show help'],
+    ['?', 'show help'],
+    ['o', 'toggle perspective/orthographic']
+  ] 
+  get showHelpGeneralMap() {
+    return this.mobile
+      ? this.showHelpGeneralMobile
+      : this.showHelpGeneralDesktop
+  }
+
+  private showHelpSliceViewMobile = [
+    ['drag', 'pan']
+  ]
+  private showHelpSliceViewDesktop = [
+    ['drag', 'pan'],
+    ['shift + drag', 'oblique slice']
+  ]
+  get showHelpSliceViewMap() {
+    return this.mobile
+      ? this.showHelpSliceViewMobile
+      : this.showHelpSliceViewDesktop
+  }
+
+  private showHelpPerspectiveMobile = [
+    ['drag', 'change perspective view']
+  ]
+  
+  private showHelpPerspectiveDesktop = [
+    ['drag', 'change perspective view']
+  ]
+  get showHelpPerspectiveViewMap() {
+    return this.mobile
+      ? this.showHelpPerspectiveMobile
+      : this.showHelpPerspectiveDesktop
+  }
+
+  get showHelpSupportText() {
+    return `Did you encounter an issue? 
+      Send us an email: <a target = "_blank" href = "mailto:${this.supportEmailAddress}">${this.supportEmailAddress}</a>, 
+      raise/track issues at github repo: <a target = "_blank" href = "${this.repoUrl}">${this.repoUrl}</a>`
+  }
+
+  incorrectParcellationNameSearchParam(title) {
+    return `The selected parcellation - ${title} - is not available. The the first parcellation of the template is selected instead.`
+  }
+
+  incorrectTemplateNameSearchParam(title) {
+    return `The selected template - ${title} - is not available.`
+  }
+
+  private supportEmailAddress = `x.gui@fz-juelich.de`
+  private repoUrl = `https://github.com/HumanBrainProject/interactive-viewer`
 
   constructor(private store : Store<ViewerStateInterface>){
 
@@ -141,6 +248,21 @@ Interactive atlas viewer requires **webgl2.0**, and the \`EXT_color_buffer_float
     /* https://stackoverflow.com/a/25394023/6059235 */
     this.mobile = /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini|Mobile|mobile|CriOS/i.test(ua)
 
+    /**
+     * set gpu limit if user is on mobile
+     */
+    if (this.mobile) {
+      this.store.dispatch({
+        type: ACTION_TYPES.UPDATE_CONFIG,
+        config: {
+          gpuLimit: 2e8
+        } as Partial<ViewerConfiguration>
+      })  
+    }
+
+    /**
+     * TODO deprecate
+     */
     const meta = 'res/json/allAggregatedData.json'
   
     fetch(meta)
@@ -172,6 +294,19 @@ const parseURLToElement = (url:string):HTMLElement=>{
   return el
 }
 
+export const UNSUPPORTED_PREVIEW = [{
+  text: 'Preview of Colin 27 and JuBrain Cytoarchitectonic',
+  previewSrc: './res/image/1.png'
+},{
+  text: 'Preview of Big Brain 2015 Release',
+  previewSrc: './res/image/2.png'
+},{
+  text: 'Preview of Waxholm Rat V2.0',
+  previewSrc: './res/image/3.png'
+}]
+
+export const UNSUPPORTED_INTERVAL = 7000
+
 export const SUPPORT_LIBRARY_MAP : Map<string,HTMLElement> = new Map([
   ['jquery@3',parseURLToElement('http://code.jquery.com/jquery-3.3.1.min.js')],
   ['jquery@2',parseURLToElement('http://code.jquery.com/jquery-2.2.4.min.js')],
@@ -179,5 +314,6 @@ export const SUPPORT_LIBRARY_MAP : Map<string,HTMLElement> = new Map([
   ['react@16',parseURLToElement('https://unpkg.com/react@16/umd/react.development.js')],
   ['reactdom@16',parseURLToElement('https://unpkg.com/react-dom@16/umd/react-dom.development.js')],
   ['vue@2.5.16',parseURLToElement('https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js')],
-  ['preact@8.4.2',parseURLToElement('https://cdn.jsdelivr.net/npm/preact@8.4.2/dist/preact.min.js')]
-])
+  ['preact@8.4.2',parseURLToElement('https://cdn.jsdelivr.net/npm/preact@8.4.2/dist/preact.min.js')],
+  ['d3@5.7.0',parseURLToElement('https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min.js')]
+])
\ No newline at end of file
diff --git a/src/atlasViewer/atlasViewer.dataService.service.ts b/src/atlasViewer/atlasViewer.dataService.service.ts
index 57cb81d22566e2aa7a184760af5aa2f0aced9f3e..b974d8536374bc1d38c467e935d5543ee2691352 100644
--- a/src/atlasViewer/atlasViewer.dataService.service.ts
+++ b/src/atlasViewer/atlasViewer.dataService.service.ts
@@ -1,10 +1,12 @@
-import { Injectable, OnDestroy, OnInit } from "@angular/core";
-import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, FETCHED_TEMPLATE, DataEntry, FETCHED_DATAENTRIES, safeFilter, FETCHED_SPATIAL_DATA, UPDATE_SPATIAL_DATA } from "../services/stateStore.service";
-import { map, distinctUntilChanged } from "rxjs/operators";
+import { Injectable, OnDestroy } from "@angular/core";
+import { Store } from "@ngrx/store";
+import { ViewerStateInterface, FETCHED_TEMPLATE, FETCHED_SPATIAL_DATA, UPDATE_SPATIAL_DATA } from "../services/stateStore.service";
 import { Subscription } from "rxjs";
 import { AtlasViewerConstantsServices } from "./atlasViewer.constantService.service";
-import { PluginManifest } from "./atlasViewer.pluginService.service";
+
+/**
+ * TODO move constructor into else where and deprecate ASAP
+ */
 
 @Injectable({
   providedIn : 'root'
@@ -12,57 +14,46 @@ import { PluginManifest } from "./atlasViewer.pluginService.service";
 export class AtlasViewerDataService implements OnDestroy{
   
   private subscriptions : Subscription[] = []
-
-  public promiseFetchedPluginManifests : Promise<PluginManifest[]> = new Promise((resolve,reject)=>{
-    Promise.all([
-      PLUGINDEV
-        ? fetch(PLUGINDEV).then(res => res.json())
-        : Promise.resolve([]),
-      Promise.all(
-        BUNDLEDPLUGINS
-          .filter(v => typeof v === 'string')
-          .map(v => fetch(`res/plugin_examples/${v}/manifest.json`).then(res => res.json()))
-      )
-        .then(arr => arr.reduce((acc,curr) => acc.concat(curr) ,[]))
-    ])
-      .then(arr => resolve( [ ... arr[0], ... arr[1] ] ))
-      .catch(reject)
-  })
   
   constructor(
     private store : Store<ViewerStateInterface>,
     private constantService : AtlasViewerConstantsServices
   ){
-    this.constantService.templateUrls.map(url => 
-      this.constantService.raceFetch(url)
-        .then(res => res.json())
-        .then(json => new Promise((resolve, reject) => {
-          if(json.nehubaConfig)
-            resolve(json)
-          else if(json.nehubaConfigURL)
-            this.constantService.raceFetch(json.nehubaConfigURL)
-              .then(res => res.json())
-              .then(json2 => resolve(
-                Object.assign({}, json, { nehubaConfig: json2 })
-              ))
-              .catch(reject)
-          else
-            reject('neither nehubaConfig nor nehubaConfigURL defined')
-        }))
-        .then(json => this.store.dispatch({
-          type: FETCHED_TEMPLATE,
-          fetchedTemplate: json
-        }))
-        .catch(e => {
-          console.warn('fetching template url failed', e)
-          this.store.dispatch({
-            type: FETCHED_TEMPLATE,
-            fetchedTemplate: null
-          })
-        })
-      )
+    this.constantService.templateUrlsPr
+      .then(urls => 
+        urls.map(url => 
+          this.constantService.raceFetch(`${this.constantService.backendUrl}${url}`)
+            .then(res => res.json())
+            .then(json => new Promise((resolve, reject) => {
+              if(json.nehubaConfig)
+                resolve(json)
+              else if(json.nehubaConfigURL)
+                this.constantService.raceFetch(`${this.constantService.backendUrl}${json.nehubaConfigURL}`)
+                  .then(res => res.json())
+                  .then(json2 => resolve({
+                      ...json,
+                      nehubaConfig: json2
+                    }))
+                  .catch(reject)
+              else
+                reject('neither nehubaConfig nor nehubaConfigURL defined')
+            }))
+            .then(json => this.store.dispatch({
+              type: FETCHED_TEMPLATE,
+              fetchedTemplate: json
+            }))
+            .catch(e => {
+              console.warn('fetching template url failed', e)
+              this.store.dispatch({
+                type: FETCHED_TEMPLATE,
+                fetchedTemplate: null
+              })
+            })
+        ))
+  }
 
-    this.init()
+  public searchDataset(){
+    
   }
 
   /* all units in mm */
@@ -86,7 +77,7 @@ export class AtlasViewerDataService implements OnDestroy{
     /* TODO future for template space? */
     const filterTemplateSpace = templateSpace == 'MNI Colin 27' ? 
       'datapath:metadata/sEEG-sample.json' :
-        templateSpace == 'Waxholm Rat V2.0' ?
+        templateSpace == 'Waxholm Space rat brain atlas v.2.0' ?
         'datapath:metadata/OSLO_sp_data_rev.json' :
           null
     
@@ -98,9 +89,15 @@ export class AtlasViewerDataService implements OnDestroy{
         .then(arr => {
           this.store.dispatch({
             type : FETCHED_SPATIAL_DATA,
-            fetchedDataEntries : arr.reduce((acc,curr) => acc.concat(curr.map(obj => Object.assign({}, obj, {
-              properties : {}
-            }))), [])
+            fetchedDataEntries: arr
+              .reduce((acc, curr) => acc.concat(curr), [])
+              .map((obj, idx) => {
+                return {
+                  ...obj,
+                  name: `Spatial landmark #${idx}`,
+                  properties: {}
+                }
+              })
           })
           this.store.dispatch({
             type : UPDATE_SPATIAL_DATA,
@@ -108,9 +105,9 @@ export class AtlasViewerDataService implements OnDestroy{
           })
         })
         .catch(console.error)
-    }else if (templateSpace === 'Allen Mouse'){
+    }else if (templateSpace === 'Allen adult mouse brain reference atlas V3'){
       return Promise.all([
-        'res/json/allen3DVolumeAggregated.json',
+        // 'res/json/allen3DVolumeAggregated.json',
         'res/json/allenTestPlane.json',
         'res/json/allen3DReconAggregated.json'
       ].map(url => fetch(url).then(res => res.json())))
@@ -126,9 +123,9 @@ export class AtlasViewerDataService implements OnDestroy{
           })
         })
         .catch(console.error)
-    }else if (templateSpace === 'Waxholm Rat V2.0'){
+    }else if (templateSpace === 'Waxholm Space rat brain atlas v.2.0'){
       return Promise.all([
-        fetch('res/json/waxholmPlaneAggregatedData.json').then(res => res.json()),
+        // fetch('res/json/waxholmPlaneAggregatedData.json').then(res => res.json()),
         fetch('res/json/camillaWaxholmPointsAggregatedData.json').then(res => res.json())
       ])
         .then(arr => arr.reduce((acc,curr) => acc.concat(curr) ,[]))
@@ -176,35 +173,6 @@ export class AtlasViewerDataService implements OnDestroy{
 
   }
 
-  init(){
-
-    const dispatchData = (arr:DataEntry[][]) =>{
-      this.store.dispatch({
-        type : FETCHED_DATAENTRIES,
-        fetchedDataEntries : arr.reduce((acc,curr)=>acc.concat(curr),[])
-      })
-    }
-
-    const fetchData = (parcellationName : string) => {
-      const urlArrays = this.constantService.mapParcellationNameToFetchUrl.get(parcellationName)
-      urlArrays ?
-        Promise.all(urlArrays.map(url=>fetch(url).then(res=>res.json())))
-          .then(arr=>dispatchData(arr))
-          .catch(console.warn) :
-        dispatchData([])
-    }
-      
-    this.subscriptions.push(
-      this.store.pipe(
-        select('viewerState'),
-        safeFilter('parcellationSelected'),
-        map(({parcellationSelected})=>(parcellationSelected.name)),
-        distinctUntilChanged()
-      )
-        .subscribe(fetchData)
-    )
-  }
-
   ngOnDestroy(){
     this.subscriptions.forEach(s=>s.unsubscribe())
   }
diff --git a/src/atlasViewer/atlasViewer.pluginService.service.ts b/src/atlasViewer/atlasViewer.pluginService.service.ts
index 363a86130659ad28d00283202d6de76b914ac03b..7adc864521c39d581871bd6be3b1a8172e065ed4 100644
--- a/src/atlasViewer/atlasViewer.pluginService.service.ts
+++ b/src/atlasViewer/atlasViewer.pluginService.service.ts
@@ -1,6 +1,6 @@
 import { Injectable, ViewContainerRef, ComponentFactoryResolver, ComponentFactory } from "@angular/core";
-import { AtlasViewerDataService } from "./atlasViewer.dataService.service";
-import { isDefined, PluginInitManifestInterface, SET_INIT_PLUGIN } from "../services/stateStore.service";
+import { PluginInitManifestInterface, ACTION_TYPES } from "src/services/state/pluginState.store";
+import { isDefined } from 'src/services/stateStore.service'
 import { AtlasViewerAPIServices } from "./atlasViewer.apiService.service";
 import { PluginUnit } from "./pluginUnit/pluginUnit.component";
 import { WidgetServices } from "./widgetUnit/widgetService.service";
@@ -9,6 +9,8 @@ import '../res/css/plugin_styles.css'
 import { interval } from "rxjs";
 import { take, takeUntil } from "rxjs/operators";
 import { Store } from "@ngrx/store";
+import { WidgetUnit } from "./widgetUnit/widgetUnit.component";
+import { AtlasViewerConstantsServices } from "./atlasViewer.constantService.service";
 
 @Injectable({
   providedIn : 'root'
@@ -19,24 +21,74 @@ export class PluginServices{
   public fetchedPluginManifests : PluginManifest[] = []
   public pluginViewContainerRef : ViewContainerRef 
   public appendSrc : (script:HTMLElement)=>void
+  public removeSrc: (script:HTMLElement) => void
   private pluginUnitFactory : ComponentFactory<PluginUnit>
 
   constructor(
     private apiService : AtlasViewerAPIServices,
-    private atlasDataService : AtlasViewerDataService,
+    private constantService : AtlasViewerConstantsServices,
     private widgetService : WidgetServices,
     private cfr : ComponentFactoryResolver,
     private store : Store<PluginInitManifestInterface>
   ){
 
     this.pluginUnitFactory = this.cfr.resolveComponentFactory( PluginUnit )
-
-    this.atlasDataService.promiseFetchedPluginManifests
+    this.apiService.interactiveViewer.uiHandle.launchNewWidget = this.launchNewWidget.bind(this) 
+    
+    /**
+     * TODO convert to rxjs streams, instead of Promise.all
+     */
+    const promiseFetchedPluginManifests : Promise<PluginManifest[]> = new Promise((resolve, reject) => {
+      Promise.all([
+        /**
+         * PLUGINDEV should return an array of 
+         */
+        PLUGINDEV
+          ? fetch(PLUGINDEV).then(res => res.json())
+          : Promise.resolve([]),
+        new Promise(resolve => {
+          fetch(`${this.constantService.backendUrl}plugins`)
+            .then(res => res.json())
+            .then(arr => Promise.all(
+              arr.map(url => new Promise(rs => 
+                /**
+                 * instead of failing all promises when fetching manifests, only fail those that fails to fetch
+                 */
+                fetch(url).then(res => res.json()).then(rs).catch(e => (console.log('fetching manifest error', e), rs(null))))
+              )
+            ))
+            .then(manifests => resolve(
+              manifests.filter(m => !!m)
+            ))
+            .catch(e => {
+              resolve([])
+            })
+        }),
+        Promise.all(
+          BUNDLEDPLUGINS
+            .filter(v => typeof v === 'string')
+            .map(v => fetch(`res/plugin_examples/${v}/manifest.json`).then(res => res.json()))
+        )
+          .then(arr => arr.reduce((acc,curr) => acc.concat(curr) ,[]))
+      ])
+        .then(arr => resolve( [].concat(arr[0]).concat(arr[1]) ))
+        .catch(reject)
+    })
+    
+    promiseFetchedPluginManifests
       .then(arr=>
         this.fetchedPluginManifests = arr)
       .catch(console.error)
   }
 
+  launchNewWidget = (manifest) => this.launchPlugin(manifest)
+    .then(handler => {
+      this.orphanPlugins.add(manifest)
+      handler.onShutdown(() => {
+        this.orphanPlugins.delete(manifest)
+      })
+    })
+
   readyPlugin(plugin:PluginManifest):Promise<any>{
     return Promise.all([
         isDefined(plugin.template) ?
@@ -52,18 +104,28 @@ export class PluginServices{
             fetch(plugin.scriptURL)
               .then(res=>res.text())
               .then(script=>plugin.script = script) :
-            Promise.reject('both template and templateURL are not defined') 
+            Promise.reject('both script and scriptURL are not defined') 
       ])
   }
 
+  public launchedPlugins: Set<string> = new Set()
+  private mapPluginNameToWidgetUnit: Map<string, WidgetUnit> = new Map()
+
+  pluginMinimised(pluginManifest:PluginManifest){
+    return this.widgetService.minimisedWindow.has( this.mapPluginNameToWidgetUnit.get(pluginManifest.name) )
+  }
+
+  public orphanPlugins: Set<PluginManifest> = new Set()
   launchPlugin(plugin:PluginManifest){
     if(this.apiService.interactiveViewer.pluginControl[plugin.name])
     {
       console.warn('plugin already launched. blinking for 10s.')
       this.apiService.interactiveViewer.pluginControl[plugin.name].blink(10)
-      return
+      const wu = this.mapPluginNameToWidgetUnit.get(plugin.name)
+      this.widgetService.minimisedWindow.delete(wu)
+      return Promise.reject('plugin already launched')
     }
-    this.readyPlugin(plugin)
+    return this.readyPlugin(plugin)
       .then(()=>{
         const pluginUnit = this.pluginViewContainerRef.createComponent( this.pluginUnitFactory )
         /* TODO in v0.2, I used:
@@ -76,8 +138,13 @@ export class PluginServices{
 
         */
 
-        /* initialising the plugin handler first, and in the future, perhaps populate the initState object/initStateUrl object */
         const handler = new PluginHandler()
+        this.apiService.interactiveViewer.pluginControl[plugin.name] = handler
+
+        /**
+         * define the handler properties prior to appending plugin script
+         * so that plugin script can access properties w/o timeout
+         */
         handler.initState = plugin.initState
           ? plugin.initState
           : null
@@ -87,18 +154,31 @@ export class PluginServices{
           : null
 
         handler.setInitManifestUrl = (url) => this.store.dispatch({
-          type : SET_INIT_PLUGIN,
+          type : ACTION_TYPES.SET_INIT_PLUGIN,
           manifest : {
             name : plugin.name,
             initManifestUrl : url
           }
         })
 
-        this.apiService.interactiveViewer.pluginControl[plugin.name] = handler
+        const shutdownCB = [
+          () => {
+            this.launchedPlugins.delete(plugin.name)
+          }
+        ]
+
+        handler.onShutdown = (cb) => {
+          if(typeof cb !== 'function'){
+            console.warn('onShutdown requires the argument to be a function') 
+            return
+          }
+          shutdownCB.push(cb)
+        }
 
         const script = document.createElement('script')
         script.innerHTML = plugin.script
         this.appendSrc(script)
+        handler.onShutdown(() => this.removeSrc(script))
 
         const template = document.createElement('div')
         template.insertAdjacentHTML('afterbegin',plugin.template)
@@ -107,19 +187,14 @@ export class PluginServices{
         const widgetCompRef = this.widgetService.addNewWidget(pluginUnit,{
           state : 'floating',
           exitable : true,
-          title : plugin.name
+          persistency: plugin.persistency,
+          title : plugin.displayName || plugin.name
         })
 
-        const unsubscribeOnPluginDestroy = []
-        const shutdownCB = []
+        this.launchedPlugins.add(plugin.name)
+        this.mapPluginNameToWidgetUnit.set(plugin.name, widgetCompRef.instance)
 
-        handler.onShutdown = (cb)=>{
-          if(typeof cb !== 'function'){
-            console.warn('onShutdown requires the argument to be a function') 
-            return
-          }
-          shutdownCB.push(cb)
-        }
+        const unsubscribeOnPluginDestroy = []
 
         handler.blink = (sec?:number)=>{
           if(typeof sec !== 'number')
@@ -146,6 +221,7 @@ export class PluginServices{
         handler.onShutdown(()=>{
           unsubscribeOnPluginDestroy.forEach(s=>s.unsubscribe())
           delete this.apiService.interactiveViewer.pluginControl[plugin.name]
+          this.mapPluginNameToWidgetUnit.delete(plugin.name)
         })
         
         pluginUnit.onDestroy(()=>{
@@ -153,15 +229,16 @@ export class PluginServices{
             shutdownCB.pop()()
           }
         })
+
+        return handler
       })
-      .catch(console.error)
   }
 }
 
 export class PluginHandler{
-  onShutdown : (callback:()=>void)=>void
-  blink : (sec?:number)=>void
-  shutdown : ()=>void
+  onShutdown : (callback:()=>void)=>void = (_) => {}
+  blink : (sec?:number)=>void = (_) => {}
+  shutdown : ()=>void = () => {}
 
   initState? : any
   initStateUrl? : string
@@ -171,10 +248,12 @@ export class PluginHandler{
 
 export interface PluginManifest{
   name? : string
+  displayName? : string
   templateURL? : string
   template? : string
   scriptURL? : string
   script? : string 
   initState? : any
   initStateUrl? : string
+  persistency? : boolean
 }
\ No newline at end of file
diff --git a/src/atlasViewer/atlasViewer.style.css b/src/atlasViewer/atlasViewer.style.css
index 2c8d8178570e50956588776d8ac5e2d7ffbd16f0..f0f1b2e0550826934afbf5e032c96fb8c706714c 100644
--- a/src/atlasViewer/atlasViewer.style.css
+++ b/src/atlasViewer/atlasViewer.style.css
@@ -1,3 +1,5 @@
+/* @import '~@angular/material/prebuilt-themes/indigo-pink.css'; */
+
 :host, :host > .atlas-container
 {
   display:block;
@@ -10,18 +12,6 @@
   background-color:black;
 }
 
-layout-mainside
-{
-  width:100%;
-  height:100%;
-}
-
-div[maincontent]
-{
-  position:relative;
-  height:100%;
-}
-
 ui-nehuba-container
 {
   position:absolute;
@@ -31,26 +21,20 @@ ui-nehuba-container
   height:100%;
 }
 
+/* zindex of banner wrapper needs to be lower than zindex of floating layer */
 div[bannerWrapper]
 {
-  z-index:5;
+  z-index:12;
   position:absolute;
-  top : 1em;
-  left: 1em;
 
   width:100%;
   display:flex;
   height:0px;
-}
-
 
-div[sidecontent]
-{
-  height:100%;
-  overflow-y:auto;
-  overflow-x:hidden;
+  justify-content: space-between;
 }
 
+
 layout-floating-container
 {
   width:100%;
@@ -58,6 +42,16 @@ layout-floating-container
   overflow:hidden;
 }
 
+[iconWrapper]
+{
+  margin-left: 1em;
+}
+
+[signinWrapper]
+{
+  margin: 0.8em 0.4em;
+}
+
 [toastContainer]
 {
   pointer-events: none;
@@ -74,11 +68,6 @@ layout-floating-container
   flex : 0 0 auto;
 }
 
-[dedicatedviewertext]
-{
-  vertical-align: middle;
-}
-
 div[floatingMouseContextualContainer]
 {
   position : absolute;
@@ -104,144 +93,88 @@ div[contextualBlock]
   color : rgba(250,250,250,0.8);
 }
 
-:host-context([darktheme="false"]) div[resizeSliver] > span.tabContainer
-{
-  background-color:rgba(235,235,235,0.8);
-}
 
-:host-context([darktheme="true"]) div[resizeSliver] > span.tabContainer
+[dockedContainer]
 {
-  background-color:rgba(50,50,50,0.8);
-  color:rgba(245,245,245,0.8);
+  margin-top:0.2em;
 }
 
-.pointer-events
+[mobileMenu]
 {
-  pointer-events: all;
+  min-height:50em;
+  margin-top: 1em;
 }
 
-.no-pointer-events
+[mobileMenu] > *
 {
-  pointer-events: none;
+  position: relative;
 }
 
-span.tabContainer > *:not(.pointer-events),
-span.tabContainer > *:not(.pointer-events):before
+[mobileMenu] > atlas-banner
 {
-  pointer-events: none;
+  z-index: 9999;
 }
 
-[resizeSliver]
+[mobileMenu] > template-parcellation-citation-container
 {
-  display:flex;
-  flex-direction: column;
-  align-items: flex-end;
+  z-index: 9;
 }
 
-[resizeSliver] span.badge:hover
+[mobileTemplateCitation]
 {
-  cursor:default;
+  padding: 1.5em;
+  display:block;
 }
 
-[heading]
+[fixedMouseContextualContainerDirective]
 {
-  padding: 0.5em 0.7em;
-  font-size: 1.5em;
-  background-color:rgba(128, 128, 128, 0.2);
-  white-space: nowrap;
+  width: 15rem;
 }
 
-span.tabContainer
+[fixedMouseContextualContainerDirective] div[body]
 {
-  display:inline-block;
-  box-sizing: border-box;
-  white-space: nowrap;
-  padding:1em 0.5em;
-  margin-top:1em;
-  position: relative;
-  opacity : 0.5;
-  transition: all 200ms;
-}
-span.tabContainer:hover
-{
-  opacity: 0.7;
+  overflow: hidden;
 }
 
-span.tabContainer > span.highlightContainer
+div[minReq]
 {
-  position : absolute;
-  left: 0;
-  top: 0;
   width: 100%;
   height: 100%;
-  display: inline-block;
-  background-color : #dbb556;
-  opacity : 0
 }
 
-span.tabContainer > span.badge
+div[imageContainer]
 {
-  margin-left:0.5em;
-  position:relative;
-  z-index:1;
-  background-color:rgba(255,0,0,1.0);
+  flex-grow: 1;
+  background: no-repeat center / contain;
 }
 
-span.tabContainer.active-tab
+div.displayCard
 {
-  opacity : 1.0;
-  position:relative;
-}
-span.tabContainer.active-tab:before
-{
-  content: ' ';
-  position: absolute;
-  left:0;
-  top:0;
-  width:100%;
-  height:100%;
-  pointer-events: none;
+  opacity: 0.8;
 }
 
-:host-context([darktheme="true"]) span.tabContainer.active-tab:before
-{
-  background-color:rgba(20,20,20,0.8);
+.mobileNavigationPanel { 
+  background-color: white;
+  width: 80%;
 }
 
-[dockedContainer]
-{
-  margin-top:0.2em;
-}
-
-[mobileMenu]
-{
-  min-height:50em;
-  margin-top: 1em;
-}
-
-[mobileMenu] > *
-{
-  position: relative;
-}
-
-[mobileMenu] > atlas-banner
-{
-  z-index: 9999;
+.menuButtonMobile {
+  position: absolute;
+  top: 0px;
+  right: 0px;
+  z-index: 9;
 }
 
-[mobileMenu] > template-parcellation-citation-container
-{
-  z-index: 9;
+.logoContainerMobile {
+  width: 100%; 
+  display:flex; 
+  justify-content: center;
 }
 
-[mobileTemplateCitation]
-{
-  padding: 1.5em;
-  display:block;
+mat-sidenav {
+  box-shadow: 0 3px 6px rgba(0,0,0,0.16), 0 3px 6px rgba(0,0,0,0.23);
 }
 
-markdown-dom[minReqMd]
-{
-  margin: 1em;
-  display:block;
+.mpobileMenuTabs {
+  margin: 40px 0 0 5px;
 }
\ No newline at end of file
diff --git a/src/atlasViewer/atlasViewer.template.html b/src/atlasViewer/atlasViewer.template.html
index 497a1bd9d402fcd4b5722259f13c5c668a5fce12..c0921108ed4514c07ebe51405e9a523171e39886 100644
--- a/src/atlasViewer/atlasViewer.template.html
+++ b/src/atlasViewer/atlasViewer.template.html
@@ -1,179 +1,252 @@
-<div *ngIf = "meetsRequirement" class = "atlas-container">
+<ng-container *ngIf="meetsRequirement; else doesNotMeetReqTemplate">
 
-  <layout-mainside
-    [showSide] = "false"
-    [animationFlag] = "false"
-    (panelAnimationEnd) = "panelAnimationEnd($event)">
-    <div maincontent>
-      <ui-nehuba-container>
-      </ui-nehuba-container>
-
-      <div bannerWrapper>
-
-        <logo-container>
-        </logo-container>
+<!-- if not mobile -->
+  <ng-template [ngIf]="!isMobile" [ngIfElse]="mobileTemplate">
+    <ng-container *ngTemplateOutlet="viewerBody">
+    </ng-container>
+  </ng-template>
   
-        <atlas-banner *ngIf = "!isMobile">
-        </atlas-banner>
-      </div>
-
-    </div>
-    <div [ngSwitch] = "sidePanelView$ | async" sidecontent>
-      <div *ngSwitchCase = "'menuBrowser'">
-        <panel-component>
-          <div heading>
-            Menu
-          </div>
-          <div mobileMenu body>
-            <atlas-banner>
-
-            </atlas-banner>
-            <template-parcellation-citation-container mobileTemplateCitation>
-
-            </template-parcellation-citation-container>
-          </div>
-        </panel-component>
-      </div>
+  <!-- if mobile -->
+  <ng-template #mobileTemplate>
+    <mat-sidenav-container
+      (backdropClick)="changeMenuState({close: true})"
+      class="w-100 h-100">
+      <mat-sidenav
+        #sidenav
+        [fixedInViewport]="true"
+        [disableClose]="true"
+        [opened]="sidePanelOpen$ | async" 
+        class="mobileNavigationPanel p-2"
+        [ngStyle]="{'background-color': darktheme? '#404040' : '#F2F2F2'}" >
+  
+        <!-- 'absorbs' the auto-focus behavior -->
+        <button mat-button style="height: 0; position: absolute;"></button>
+        <logo-container class="logoContainerMobile"></logo-container>
+  
+        <tabset #mobileMenuTabs [justified]="true" class="mpobileMenuTabs" >
+          <tab heading="Template">
+            <signin-banner [darktheme] = "darktheme" signinWrapper></signin-banner>
+          </tab>
+          <tab heading="Containers">
+            <menu-icons iconWrapper hidden #MenuIcons>
+            </menu-icons>
+            <panel-component>
+              <div class="m-2" heading>
+                Layer Browser
+              </div>
+              <div class="m-2" body>
+                <layer-browser></layer-browser>
+              </div>
+            </panel-component>
+            <div dockedContainerDirective>
+            </div>
+          </tab>
+        </tabset>
+  
+      </mat-sidenav>
       
-      <!-- Data Browser -->
-      <div *ngSwitchCase = "'dataBrowser'">
-        <panel-component [bodyCollapsable] = "true">
-          <div heading>
-            Data Browser
-          </div>
-          <data-browser body>
-          </data-browser>
-        </panel-component>
-      </div>
-
-      <!-- Layer Browser -->
-      <div *ngSwitchCase = "'ngLayer'">
-        <panel-component [bodyCollapsable] = "true">
-          <div heading>
-            Layer Browser
-          </div>
-          <layer-browser body>
-          </layer-browser>
-        </panel-component>
+      <mat-sidenav-content>
+        <ng-container *ngTemplateOutlet="viewerBody">
+  
+        </ng-container>
+      </mat-sidenav-content>
+  
+    </mat-sidenav-container>
+  
+    <div class="btnWrapper btnWrapper-lg menuButtonMobile m-2" (click)="changeMenuState()">
+      <div
+        class="shadow btn btn-sm btn-secondary rounded-circle">
+        <i class="fas fa-bars"></i>
       </div>
-
-      <!-- Tools Browser-->
-      <div *ngSwitchCase = "'toolsBrowser'">
-        <panel-component [bodyCollapsable] = "true">
-          <div heading>
-            Tools Browser
-          </div>
-          <plugin-banner body>          
-          </plugin-banner>
-        </panel-component>
+    </div>
+  </ng-template>
+</ng-container>
+
+
+<ng-template #helpComponent>
+  <tabset>
+    <tab heading="Help">
+      <help-component>
+      </help-component>
+    </tab>
+    <tab heading="Settings">
+      <div class="mt-2">
+        <config-component>
+        </config-component>
       </div>
-
-      <!-- Default (shown at the beginning + sliver of time during closing of side bar)-->
-      <div *ngSwitchDefault>
-        
+    </tab>
+    <tab heading="Privacy Policy">
+      <div class="mt-2">
+        <cookie-agreement>
+        </cookie-agreement>
       </div>
-      
-      <!-- Docked Container-->
-      <div dockedContainer #dockedContainer>
+    </tab>
+    <tab heading="Terms of Use">
+      <div class="mt-2">
+        <kgtos-component>
+        </kgtos-component>
       </div>
+    </tab>
+  </tabset>
+</ng-template>
+<ng-template #signinModalComponent>
+  <signin-modal>
+    
+  </signin-modal>
+</ng-template>
+
+<ng-template #kgToS>
+    <kgtos-component>
+    </kgtos-component>
+    <div class="modal-footer">
+      <button type="button" class="btn btn-primary" (click)="kgTosClickedOk()">Ok</button>
     </div>
+  </ng-template>
 
-    <div resizeSliver>
-      <span
-        *ngIf = "isMobile"
-        class = "tabContainer"
-        (click) = "toggleSidePanel('menuBrowser')"
-        [ngClass] = "{'active-tab' : (sidePanelView$ | async) === 'menuBrowser'}" >
-        <i class="glyphicon glyphicon-info-sign"></i>
-      </span>
+<ng-template #cookieAgreementComponent>
+  <cookie-agreement>
+  </cookie-agreement>
 
-      <span 
-        container = "body"
-        placement = "left"
-        [tooltip] = "isMobile ? null : !(selectedPOI$ | async) ? '' : (((selectedPOI$ | async).length === 0 ? 'No' : (selectedPOI$ | async).length) + ' selected region' + ((selectedPOI$ | async).length > 1 ? 's' : '')) + ' of interest' "
-        [ngClass] = "{'active-tab' : (sidePanelView$ | async) === 'dataBrowser'}" 
-        (click) = "toggleSidePanel('dataBrowser')" 
-        class = "tabContainer">
-        <span  [@newEvent] = "selectedPOI$ | async" class = "highlightContainer">
-        </span>
-        <i class = "glyphicon glyphicon-search"></i>
-        <span class = "badge" *ngIf = "(selectedPOI$ | async) && (selectedPOI$ | async).length > 0">
-          {{ (selectedPOI$ | async).length }}
-        </span>
-      </span>
+  <div class="modal-footer">
+    <button type="button" class="btn btn-primary" (click)="cookieClickedOk()">Ok</button>
+  </div>
+</ng-template>
 
-      <span 
-        placement = "left"
-        [tooltip] = "isMobile ? null : !ngLayers ? '' : ((ngLayers.length === 0 ? 'No' : ngLayers.length) + ' loaded layer' + (ngLayers.length > 1 ? 's' : ''))"
-        [ngClass] = "{'active-tab' : (sidePanelView$ | async) === 'ngLayer'}" 
-        (click) = "toggleSidePanel('ngLayer')" 
-        class = "tabContainer">
-        <span  [@newEvent] = "ngLayers" class = "highlightContainer">
-        </span>
-        <i class = "glyphicon glyphicon-list"></i>
-        <span class = "badge" *ngIf = "ngLayers && ngLayers.length > 0">      
-          {{ ngLayers.length }}
-        </span>
-      </span>
+<!-- atlas template -->
+<ng-template #viewerBody>
+  <div class="atlas-container" helpdirective>
 
-      <span 
-        placement = "left"
-        (mouseover) = "_toolIconsVisible = true" 
-        (mouseout) = "_toolIconsVisible = false"
-        [tooltip] = "isMobile ? null : _toolIconDockAll ? 'dock all widgets' : _toolIconFloatAll ? 'float all widgets' : 'Tools Browser'"
-        [ngClass] = "{'active-tab' : (sidePanelView$ | async) === 'toolsBrowser'}" 
-        (click) = "toggleSidePanel('toolsBrowser')" 
-        class = "tabContainer">
-        <i class="glyphicon glyphicon-wrench"></i>
-        <span *ngIf = "!isMobile" class ="badge">
-          <span [class.hidden] = "_toolIconsVisible" >
-            ...
-          </span>
-          <span [class.hidden] = "!_toolIconsVisible">
-            <span class = "pointer-events" (click) = "$event.stopPropagation();this.widgetServices.dockAllWidgets()" (mouseover) = "_toolIconDockAll = true" (mouseout) = "_toolIconDockAll = false">
-              <i class = "no-pointer-events glyphicon glyphicon-log-in"></i>
-            </span>
-            <span class = "mute-text">
-              |
-            </span>
-            <span class = "pointer-events" (click) = "$event.stopPropagation();this.widgetServices.floatAllWidgets()" (mouseover) = "_toolIconFloatAll = true" (mouseout) = "_toolIconFloatAll = false">
-              <i class = "no-pointer-events glyphicon glyphicon-new-window"></i>
-            </span>
-          </span>
-        </span>
-      </span>
+    <ui-nehuba-container (contextmenu)="nehubaClickHandler($event)">
+    </ui-nehuba-container>
+  
+    <div *ngIf="!isMobile" bannerWrapper>
+      <menu-icons iconWrapper>
+      </menu-icons>
+      <signin-banner signinWrapper>
+      </signin-banner>
     </div>
-  </layout-mainside>
-
-  <layout-floating-container
-    zIndex = "9"
-    #floatingOverlayContainer>
-    <ng-template #floatingContainer>
-
-    </ng-template>
-    <div [style.transform] = "floatingMouseContextualContainerTransform" floatingMouseContextualContainer>
-      <div 
-        *ngIf = "onhoverLandmark$ | async" 
-        contextualBlock>
-        {{ onhoverLandmark$ | async }} <i><small class = "mute-text">{{ toggleMessage }}</small></i>
+  
+    <layout-floating-container
+      zIndex="13"
+      #floatingOverlayContainer>
+      <div floatingContainerDirective>
+  
+      </div>
+      
+      <!-- TODO move to nehuba overlay container -->
+      <panel-component class="shadow" fixedMouseContextualContainerDirective #rClContextMenu>
+        <div heading>
+          <h5 class="pe-all p-2 m-0">
+            What's here?
+          </h5>
+        </div>
+        <div body>
+
+          <div
+            *ngIf="(onhoverSegmentsForFixed$ | async)?.length > 0 || (selectedRegions$ | async)?.length > 0"
+            class="p-2">
+            Search for data relating to:
+          </div>
+          
+          <div
+            *ngFor="let onhoverSegmentFixed of (onhoverSegmentsForFixed$ | async)"
+            (click)="searchRegion([onhoverSegmentFixed])"
+            class="ws-no-wrap text-left pe-all btn btn-sm btn-secondary btn-block mt-0"
+            data-toggle="tooltip" 
+            data-placement="top" 
+            [title]="onhoverSegmentFixed.name">
+            <small class="text-semi-transparent">(hovering)</small> {{ onhoverSegmentFixed.name }}
+          </div>
+  
+          <div
+            *ngIf="(selectedRegions$ | async)?.length > 0 && (selectedRegions$ | async); let selectedRegions"
+            (click)="searchRegion(selectedRegions)"
+            class="ws-no-wrap text-left pe-all mt-0 btn btn-sm btn-secondary btn-block">
+            <ng-container *ngIf="selectedRegions.length > 1">
+              <small class="text-semi-transparent">(selected)</small> {{ selectedRegions.length }} selected regions
+            </ng-container>
+            <ng-container *ngIf="selectedRegions.length === 1">
+              <small class="text-semi-transparent">(selected)</small> {{ selectedRegions[0].name }}
+            </ng-container>
+          </div>
+          
+          <div
+            class="p-2 text-muted"
+            *ngIf="(onhoverSegmentsForFixed$ | async)?.length === 0 && (selectedRegions$ | async)?.length === 0">
+            Right click on a parcellation region or select parcellation regions to search KG for associated datasets.
+          </div>
+  
+          <ng-template #noRegionSelected>
+            <div
+              (click)="searchRegion()"
+              class="ws-no-wrap text-left pe-all mt-0 btn btn-sm btn-secondary btn-block">
+              No region selected. Search KG for all datasets in this template space.
+            </div>
+          </ng-template>
+  
+        </div>
+      </panel-component>
+      
+      <div floatingMouseContextualContainer floatingMouseContextualContainerDirective>
+        <div 
+          *ngIf="onhoverLandmark$ | async" 
+          contextualBlock>
+          {{ onhoverLandmark$ | async }} <i><small class = "mute-text">{{ toggleMessage }}</small></i>
+        </div>
+        <div 
+          *ngIf="onhoverSegments$ | async; let onhoverSegments"
+          contextualBlock>
+          <div
+            *ngFor="let segment of onhoverSegments"
+            [innerHtml]="segment | transformOnhoverSegment">
+          </div>
+          <i><small class = "mute-text">{{ toggleMessage }}</small></i>
+        </div>
+        <!-- TODO Potentially implementing plugin contextual info -->
       </div>
-      <div 
-        *ngIf = "onhoverSegment$ | async as onhoverSegment " 
-        contextualBlock>
-        {{ onhoverSegment }} <i><small class = "mute-text">{{ toggleMessage }}</small></i>
+  
+      <div toastContainer>
+        <div toastDirective>
+        </div>
       </div>
-      <!-- TODO Potentially implementing plugin contextual info -->
+    </layout-floating-container>
+  
+    <div pluginFactoryDirective>
     </div>
-    <div toastContainer>
-      <ng-template #toastContainer>
-      </ng-template>
+  </div>
+</ng-template>
+
+<!-- does not meet req template -->
+<ng-template #doesNotMeetReqTemplate>
+  <div class="d-flex flex-column" minReq *ngIf="!meetsRequirement">
+    <div class="jumbotron bg-light text-center mb-0">
+      <div>
+        <h1 class="mb-3">
+          <i class="fas fa-exclamation-triangle"></i> Unsupported browser detected
+        </h1>
+        <p>
+          We recommend using the latest version of <a target="_blank" href="https://www.google.com/chrome/">Google Chrome</a>
+          <!-- or <a target="_blank" href="https://www.mozilla.org/firefox/">Mozilla Firefox (&lt; 67)</a> for viewing the interactive viewer. -->
+        </p>
+        <div class="col-6 d-inline-block text-left">
+          <readmore-component
+            [collapsedHeight]="0">
+            <markdown-dom [markdown]="constantsService.minReqExplaner">
+  
+            </markdown-dom>
+          </readmore-component>
+        </div>
+        
+      </div>
     </div>
-  </layout-floating-container>
-
-  <ng-template #pluginFactory>
-  </ng-template>
-
-</div>
-
-<markdown-dom minReqMd *ngIf = "!meetsRequirement" [markdown] = "constantsService.minReqMD" >
-</markdown-dom>
+    <ng-container *ngFor="let preview of unsupportedPreviews; let idx = index">
+      <div [hidden]="idx !== unsupportedPreviewIdx" class="text-center mb-3" imageContainer [style.backgroundImage]="'url(' + preview.previewSrc + ')'" >
+        <div class="mt-2 card d-inline-block displayCard">
+          <div class="card-body">
+            {{ preview.text }}
+          </div>
+        </div>
+      </div>
+    </ng-container>
+  </div>
+</ng-template>
\ No newline at end of file
diff --git a/src/atlasViewer/atlasViewer.urlService.service.ts b/src/atlasViewer/atlasViewer.urlService.service.ts
index 9087930a30aea98f5baf1fc04237c202d8241a73..b548c96f292bfa89fa808beccd7b2c5b16778a2a 100644
--- a/src/atlasViewer/atlasViewer.urlService.service.ts
+++ b/src/atlasViewer/atlasViewer.urlService.service.ts
@@ -1,11 +1,13 @@
 import { Injectable } from "@angular/core";
 import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, isDefined, NEWVIEWER, getLabelIndexMap, SELECT_REGIONS, CHANGE_NAVIGATION, LOAD_DEDICATED_LAYER, ADD_NG_LAYER, PluginInitManifestInterface } from "../services/stateStore.service";
+import { ViewerStateInterface, isDefined, NEWVIEWER, CHANGE_NAVIGATION, ADD_NG_LAYER, generateLabelIndexId } from "../services/stateStore.service";
+import { PluginInitManifestInterface } from 'src/services/state/pluginState.store'
 import { Observable,combineLatest } from "rxjs";
 import { filter, map, scan, distinctUntilChanged, skipWhile, take } from "rxjs/operators";
-import { getActiveColorMapFragmentMain } from "../ui/nehubaContainer/nehubaContainer.component";
 import { PluginServices } from "./atlasViewer.pluginService.service";
 import { AtlasViewerConstantsServices } from "./atlasViewer.constantService.service";
+import { ToastService } from "src/services/toastService.service";
+import { SELECT_REGIONS_WITH_ID } from "src/services/state/viewerState.store";
 
 declare var window
 
@@ -21,7 +23,8 @@ export class AtlasViewerURLService{
   constructor(
     private store : Store<ViewerStateInterface>,
     private pluginService : PluginServices,
-    private constantService:AtlasViewerConstantsServices
+    private constantService:AtlasViewerConstantsServices,
+    private toastService: ToastService
   ){
 
     this.pluginState$ = this.store.pipe(
@@ -49,6 +52,9 @@ export class AtlasViewerURLService{
       scan((acc,val)=>Object.assign({},acc,val),{})
     )
 
+    /**
+     * TODO change additionalNgLayer to id, querying node backend for actual urls
+     */
     this.additionalNgLayers$ = combineLatest(
       this.changeQueryObservable$.pipe(
         map(state => state.templateSelected)
@@ -80,33 +86,77 @@ export class AtlasViewerURLService{
       take(1),
       map(ft => ft.filter(t => t !== null))
     ).subscribe(fetchedTemplates=>{
+
+      /**
+       * TODO
+       * consider what to do when we have ill formed search params
+       * param validation?
+       */
       const searchparams = new URLSearchParams(window.location.search)
  
       /* first, check if any template and parcellations are to be loaded */
       const searchedTemplatename = searchparams.get('templateSelected')
       const searchedParcellationName = searchparams.get('parcellationSelected')
+
+      if (!searchedTemplatename) {
+        const urlString = window.location.href
+        /**
+         * TODO think of better way of doing this
+         */
+        history.replaceState(null, '', urlString.split('?')[0])
+        return
+      }
       
       const templateToLoad = fetchedTemplates.find(template=>template.name === searchedTemplatename)
-      if(!templateToLoad)
+      if (!templateToLoad) {
+        this.toastService.showToast(
+          this.constantService.incorrectTemplateNameSearchParam(searchedTemplatename),
+          {
+            timeout: 5000
+          }
+        )
+        const urlString = window.location.href
+        /**
+         * TODO think of better way of doing this... maybe pushstate?
+         */
+        history.replaceState(null, '', urlString.split('?')[0])
         return
-      const parcellationToLoad = templateToLoad ? 
-        templateToLoad.parcellations.find(parcellation=>parcellation.name === searchedParcellationName) :
-        templateToLoad.parcellations[0]
+      }
+
+      /**
+       * TODO if search param of either template or parcellation is incorrect, wrong things are searched
+       */
+      const parcellationToLoad = templateToLoad.parcellations.find(parcellation=>parcellation.name === searchedParcellationName)
+
+      if (!parcellationToLoad) {
+        this.toastService.showToast(
+          this.constantService.incorrectParcellationNameSearchParam(searchedParcellationName),
+          {
+            timeout: 5000
+          }
+        )
+      }
       
       this.store.dispatch({
         type : NEWVIEWER,
         selectTemplate : templateToLoad,
-        selectParcellation : parcellationToLoad
+        selectParcellation : parcellationToLoad || templateToLoad.parcellations[0]
       })
 
       /* selected regions */
-      const labelIndexMap = getLabelIndexMap(parcellationToLoad.regions)
-      const selectedRegions = searchparams.get('regionsSelected')
-      if(selectedRegions){
-        this.store.dispatch({
-          type : SELECT_REGIONS,
-          selectRegions : selectedRegions.split('_').map(labelIndex=>labelIndexMap.get(Number(labelIndex)))
-        })
+      if (parcellationToLoad && parcellationToLoad.regions) {
+        /**
+         * either or both parcellationToLoad and .regions maybe empty
+         */
+        const selectedRegionsParam = searchparams.get('regionsSelected')
+        if(selectedRegionsParam){
+          const ids = selectedRegionsParam.split('_')
+
+          this.store.dispatch({
+            type : SELECT_REGIONS_WITH_ID,
+            selectRegionIds: ids
+          })
+        }
       }
       
       /* now that the parcellation is loaded, load the navigation state */
@@ -135,7 +185,7 @@ export class AtlasViewerURLService{
             name : layer,
             source : `nifti://${layer}`,
             mixability : 'nonmixable',
-            shader : getActiveColorMapFragmentMain()
+            shader : this.constantService.getActiveColorMapFragmentMain()
           }
         }))
       }
@@ -143,7 +193,7 @@ export class AtlasViewerURLService{
       const pluginStates = searchparams.get('pluginStates')
       if(pluginStates){
         const arrPluginStates = pluginStates.split('__')
-        arrPluginStates.forEach(url => fetch(url).then(res => res.json()).then(json => this.pluginService.launchPlugin(json)).catch(console.error))
+        arrPluginStates.forEach(url => fetch(url).then(res => res.json()).then(json => this.pluginService.launchNewWidget(json)).catch(console.error))
       }
     })
 
@@ -173,7 +223,7 @@ export class AtlasViewerURLService{
                   }
                   break;
                 case 'regionsSelected':
-                  _[key] = state[key].map(region=>region.labelIndex).join('_')
+                  _[key] = state[key].map(({ ngId, labelIndex })=> generateLabelIndexId({ ngId,labelIndex })).join('_')
                   break;
                 case 'templateSelected':
                 case 'parcellationSelected':
@@ -191,13 +241,27 @@ export class AtlasViewerURLService{
       this.pluginState$
     ).pipe(
       /* TODO fix encoding of nifti path. if path has double underscore, this encoding will fail */
-      map(([navigationState, niftiLayers, pluginState]) => Object.assign({}, navigationState, { pluginStates : Array.from(pluginState.initManifests.values()).filter(v => v !== null).length > 0 ? Array.from(pluginState.initManifests.values()).filter(v => v !== null).join('__') : null }, { niftiLayers : niftiLayers.length > 0 ? niftiLayers.map(layer => layer.name).join('__') : null }))
+      map(([navigationState, niftiLayers, pluginState]) => {
+        return {
+          ...navigationState,
+          pluginState: Array.from(pluginState.initManifests.values()).filter(v => v !== null).length > 0 
+            ? Array.from(pluginState.initManifests.values()).filter(v => v !== null).join('__') 
+            : null,
+          niftiLayers : niftiLayers.length > 0
+            ? niftiLayers.map(layer => layer.name).join('__')
+            : null
+        }
+      })
     ).subscribe(cleanedState=>{
       const url = new URL(window.location)
       const search = new URLSearchParams( window.location.search )
-      Object.keys(cleanedState).forEach(key=>{
-        cleanedState[key] ? search.set(key,cleanedState[key]) : search.delete(key)
-      })
+      for (const key in cleanedState) {
+        if (cleanedState[key]) {
+          search.set(key, cleanedState[key])
+        } else {
+          search.delete(key)
+        }
+      }
 
       url.search = search.toString()
       history.replaceState(null, '', url.toString())
diff --git a/src/atlasViewer/modalUnit/modalUnit.component.ts b/src/atlasViewer/modalUnit/modalUnit.component.ts
index a7fd52717d9086a2fd63cb7451ec50d0846fc2cb..ae5b300707a3ed011268e9cc23733776a94ce89c 100644
--- a/src/atlasViewer/modalUnit/modalUnit.component.ts
+++ b/src/atlasViewer/modalUnit/modalUnit.component.ts
@@ -1,4 +1,4 @@
-import { Component, Input, ViewContainerRef } from '@angular/core'
+import { Component, Input, ViewContainerRef, TemplateRef, ViewChild } from '@angular/core'
 
 @Component({
   templateUrl : './modalUnit.template.html',
@@ -10,9 +10,18 @@ import { Component, Input, ViewContainerRef } from '@angular/core'
 export class ModalUnit{
   @Input() title : string
   @Input() body : string = 'Modal Body Text'
+  @Input() template: TemplateRef<any>
   @Input() footer: string 
 
+  @ViewChild('templateContainer', {read:ViewContainerRef}) templateContainer : ViewContainerRef
+
   constructor(public viewContainerRef : ViewContainerRef){
     
   }
+
+  ngAfterViewInit(){
+    if (this.templateContainer) {
+      this.templateContainer.createEmbeddedView(this.template)
+    }
+  }
 }
\ No newline at end of file
diff --git a/src/atlasViewer/modalUnit/modalUnit.template.html b/src/atlasViewer/modalUnit/modalUnit.template.html
index abb9fbb959d040759ca2f92942e951989180bb03..a3aba585c53b1d35e044ec5aa92ece92c94a05a2 100644
--- a/src/atlasViewer/modalUnit/modalUnit.template.html
+++ b/src/atlasViewer/modalUnit/modalUnit.template.html
@@ -4,10 +4,16 @@
   </h4>
 </div>
 
-<div class = "modal-body">
+<div *ngIf = "!template" class = "modal-body">
   {{ body }}
 </div>
 
+<div *ngIf = "template" class="modal-body">
+  <ng-template #templateContainer>
+
+  </ng-template>
+</div>
+
 <div *ngIf = "footer" class = "modal-footer">
   {{ footer }}
 </div>
\ No newline at end of file
diff --git a/src/atlasViewer/onhoverSegment.pipe.ts b/src/atlasViewer/onhoverSegment.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..baff7b9d5d1de04b0d6c5327aabf2a41a532ff4f
--- /dev/null
+++ b/src/atlasViewer/onhoverSegment.pipe.ts
@@ -0,0 +1,25 @@
+import { PipeTransform, Pipe, SecurityContext } from "@angular/core";
+import { DomSanitizer } from "@angular/platform-browser";
+
+@Pipe({
+  name: 'transformOnhoverSegment'
+})
+
+export class TransformOnhoverSegmentPipe implements PipeTransform{
+  constructor(private sanitizer:DomSanitizer){
+
+  }
+
+  private getStatus(text:string) {
+    return ` <span class="text-muted">(${this.sanitizer.sanitize(SecurityContext.HTML, text)})</span>`
+  }
+
+  public transform(segment: any | number){
+    return this.sanitizer.bypassSecurityTrustHtml((
+      (segment.name || segment) +
+      (segment.status
+        ? this.getStatus(segment.status)
+        : '')
+    ))
+  }
+}
\ No newline at end of file
diff --git a/src/atlasViewer/pluginUnit/pluginUnit.component.ts b/src/atlasViewer/pluginUnit/pluginUnit.component.ts
index 8afdef862023810da00a20580c0b7dbb36796f06..5d849eab7fb874a71b9053c8c4ad6e2b4a5a3103 100644
--- a/src/atlasViewer/pluginUnit/pluginUnit.component.ts
+++ b/src/atlasViewer/pluginUnit/pluginUnit.component.ts
@@ -17,7 +17,8 @@ export class PluginUnit implements OnDestroy{
   }
 
   ngOnDestroy(){
-    console.log('plugin being destroyed')
+    if (!PRODUCTION)
+      console.log('plugin being destroyed')
   }
 
 }
\ No newline at end of file
diff --git a/src/atlasViewer/widgetUnit/widgetService.service.ts b/src/atlasViewer/widgetUnit/widgetService.service.ts
index be784e7cb95c13d75f48f6e22e212cdc03e78d46..521d69ac8356087702807ec34ebeec44204f7888 100644
--- a/src/atlasViewer/widgetUnit/widgetService.service.ts
+++ b/src/atlasViewer/widgetUnit/widgetService.service.ts
@@ -20,6 +20,8 @@ export class WidgetServices{
 
   private clickedListener : Subscription[] = []
 
+  public minimisedWindow: Set<WidgetUnit> = new Set()
+
   constructor(
     private cfr:ComponentFactoryResolver,
     private constantServce:AtlasViewerConstantsServices,
@@ -36,6 +38,10 @@ export class WidgetServices{
     this.clickedListener.forEach(s=>s.unsubscribe())
   }
 
+  minimise(wu:WidgetUnit){
+    this.minimisedWindow.add(wu)
+  }
+
   addNewWidget(guestComponentRef:ComponentRef<any>,options?:Partial<WidgetOptionsInterface>):ComponentRef<WidgetUnit>{
     const component = this.widgetUnitFactory.create(this.injector)
     const _option = getOption(options)
@@ -64,6 +70,7 @@ export class WidgetServices{
       _component.instance.exitable = _option.exitable
       _component.instance.title = _option.title
       _component.instance.persistency = _option.persistency
+      _component.instance.titleHTML = _option.titleHTML
 
       /* internal properties, used for changing state */
       _component.instance.guestComponentRef = guestComponentRef
@@ -82,6 +89,7 @@ export class WidgetServices{
       _component.instance.setWidthHeight()
 
       this.widgetComponentRefs.add( _component )
+      _component.onDestroy(() => this.minimisedWindow.delete(_component.instance))
 
       this.clickedListener.push(
         _component.instance.clickedEmitter.subscribe((widgetUnit:WidgetUnit)=>{
@@ -157,15 +165,10 @@ function getOption(option?:Partial<WidgetOptionsInterface>):WidgetOptionsInterfa
     exitable : safeGet(option, 'exitable') !== null
       ? safeGet(option, 'exitable')
       : true,
-    state : safeGet(option, 'state')
-      ? safeGet(option, 'state')
-      : 'floating',
-    title : safeGet(option, 'title')
-      ? safeGet(option, 'title')
-      : 'Untitled',
-    persistency : safeGet(option, 'persistency')
-      ? safeGet(option, 'persistency')
-      : false
+    state : safeGet(option, 'state') || 'floating',
+    title : safeGet(option, 'title') || 'Untitled',
+    persistency : safeGet(option, 'persistency') || false,
+    titleHTML: safeGet(option, 'titleHTML') || null
   }
 }
 
@@ -173,6 +176,7 @@ export interface WidgetOptionsInterface{
   title? : string
   state? : 'docked' | 'floating'
   exitable? : boolean
-  persistency : boolean
+  persistency? : boolean
+  titleHTML? : string
 }
 
diff --git a/src/atlasViewer/widgetUnit/widgetUnit.component.ts b/src/atlasViewer/widgetUnit/widgetUnit.component.ts
index 3be619d899d9293eaff612448acc35bebb394663..fc32210cd0774a8b1b75126c25f9e10b2de9195a 100644
--- a/src/atlasViewer/widgetUnit/widgetUnit.component.ts
+++ b/src/atlasViewer/widgetUnit/widgetUnit.component.ts
@@ -1,5 +1,6 @@
-import { Component, ViewChild, ViewContainerRef,ComponentRef, HostBinding, HostListener, Output, EventEmitter, Input, ElementRef } from "@angular/core";
+import { Component, ViewChild, ViewContainerRef,ComponentRef, HostBinding, HostListener, Output, EventEmitter, Input, ElementRef, OnInit } from "@angular/core";
 import { WidgetServices } from "./widgetService.service";
+import { AtlasViewerConstantsServices } from "../atlasViewer.constantService.service";
 
 
 @Component({
@@ -9,7 +10,7 @@ import { WidgetServices } from "./widgetService.service";
   ]
 })
 
-export class WidgetUnit {
+export class WidgetUnit implements OnInit{
   @ViewChild('container',{read:ViewContainerRef}) container : ViewContainerRef
   @ViewChild('emptyspan',{read:ElementRef}) emtpy : ElementRef
 
@@ -22,6 +23,21 @@ export class WidgetUnit {
   @HostBinding('style.height')
   height : string = this.state === 'docked' ? null : '0px'
 
+  @HostBinding('style.display')
+  get isMinimised(){
+    return this.widgetServices.minimisedWindow.has(this) ? 'none' : null
+  }
+  /**
+   * TODO
+   * upgrade to angular>=7, and use cdk to handle draggable components
+   */
+  get transform(){
+    return this.state === 'floating' ?
+      `translate(${this.position[0]}px, ${this.position[1]}px)` :
+      `translate(0 , 0)`
+  }
+
+  public canBeDocked: boolean = false
   @HostListener('mousedown')
   clicked(){
     this.clickedEmitter.emit(this)
@@ -34,11 +50,26 @@ export class WidgetUnit {
   @Output()
   clickedEmitter : EventEmitter<WidgetUnit> = new EventEmitter()
 
+  @Input()
   public exitable : boolean = true
 
+  @Input()
+  public titleHTML : string = null
+
   public guestComponentRef : ComponentRef<any>
-  public cf : ComponentRef<WidgetUnit>
   public widgetServices:WidgetServices
+  public cf : ComponentRef<WidgetUnit>
+
+  public id: string 
+  constructor(
+    private constantsService: AtlasViewerConstantsServices
+    ){
+    this.id = Date.now().toString()
+  }
+
+  ngOnInit(){
+    this.canBeDocked = typeof this.widgetServices.dockedContainer !== 'undefined'
+  }
 
   /**
    * @param {boolean}
@@ -93,44 +124,8 @@ export class WidgetUnit {
   /* floating widget specific functionalities */
 
   position : [number,number] = [400,100]
-  reposStartViewPos : [number,number] = [0,0]
-  reposStartMousePos : [number,number] = [0,0]
-  repositionFlag : boolean = false
-
-  /* nb FF does not provide event.clientX / event.clientY on drag event. So will have to attach dragover event listener */
-  /* ref: https://bugzilla.mozilla.org/show_bug.cgi?id=505521 */
-  @HostListener('document:dragover',['$event'])
-  mousemove(ev:MouseEvent){
-    if(this.repositionFlag){
-      this.position[0] = this.reposStartViewPos[0] - this.reposStartMousePos[0] + ev.clientX
-      this.position[1] = this.reposStartViewPos[1] - this.reposStartMousePos[1] + ev.clientY
-    }
-  }
-
-  dragend(ev:DragEvent){
-    this.repositionFlag = false
-  }
-
-  dragstart(ev:DragEvent){
-    this.reposStartMousePos[0] = ev.clientX
-    this.reposStartMousePos[1] = ev.clientY
-
-    this.reposStartViewPos[0] = this.position[0]
-    this.reposStartViewPos[1] = this.position[1]
-
-    this.repositionFlag = true
-
-    /* nb FF requires dataTransfer.setData in order to fire the drag or dragover event */
-    ev.dataTransfer.setData('application/node type', '')
 
-    /* nb FF will render any invisible DOM element as a file icon.  */
-    ev.dataTransfer.setDragImage(this.emtpy.nativeElement, 0, 0)
+  get isMobile(){
+    return this.constantsService.mobile
   }
-
-  get transform(){
-    return this.state === 'floating' ?
-      `translate(${this.position[0]}px, ${this.position[1]}px)` :
-      `translate(0 , 0)`
-  }
-
 }
\ No newline at end of file
diff --git a/src/atlasViewer/widgetUnit/widgetUnit.style.css b/src/atlasViewer/widgetUnit/widgetUnit.style.css
index f3f1e776489729696d6204df809f43f953258cbe..10289f924763603c034766cd15e23a819cb0e857 100644
--- a/src/atlasViewer/widgetUnit/widgetUnit.style.css
+++ b/src/atlasViewer/widgetUnit/widgetUnit.style.css
@@ -34,8 +34,6 @@ panel-component[widgetUnitPanel]
 {
   display:block;
   min-width:280px;
-  max-width:300px;
-  overflow-x:hidden;
 }
 
 :host-context([state='floating']) div[widgetUnitHeading]:hover
@@ -46,4 +44,4 @@ panel-component[widgetUnitPanel]
 [emptyspan]
 {
   opacity:0.01;
-}
\ No newline at end of file
+}
diff --git a/src/atlasViewer/widgetUnit/widgetUnit.template.html b/src/atlasViewer/widgetUnit/widgetUnit.template.html
index 9c70830616324ecdf38c10b84761805ff1ebd6d1..a7cfd51ef1f2b839190f4cb060b3c4038ee58270 100644
--- a/src/atlasViewer/widgetUnit/widgetUnit.template.html
+++ b/src/atlasViewer/widgetUnit/widgetUnit.template.html
@@ -2,27 +2,43 @@
   [style.transform] = "transform"
   [containerClass] = "containerClass"
   widgetUnitPanel
-  [bodyCollapsable] = "state === 'docked'">
+  [bodyCollapsable] = "state === 'docked'"
+  [cdkDragDisabled]="state === 'docked'"
+  cdkDrag
+  [ngStyle]="{'max-width': isMobile? '100%' : '300px',
+              'margin-bottom': isMobile? '5px': '0'}">
   <div 
-    draggable = "true"
-    (dragend) = "dragend($event)"
-    (dragstart) = "dragstart($event)"
-    widgetUnitHeading 
-    heading>
+    widgetUnitHeading
+    heading
+    cdkDragHandle>
     <div #emptyspan emptyspan>.</div>
-    <div title>{{ title }}</div>
+    <div title>
+      <div *ngIf="!titleHTML">
+        {{ title }}
+      </div>
+      <div [innerHTML]="titleHTML" *ngIf="titleHTML">
+
+      </div>
+    </div>
     <div icons>
-      <i *ngIf = "state === 'floating'"
+      <i
+        *ngIf="!isMobile"
+        (click)="widgetServices.minimise(this)"
+        class="fas fa-window-minimize"
+        hoverable>
+
+      </i>
+      <i *ngIf = "canBeDocked && state === 'floating' && !isMobile"
         (click) = "dock($event)" 
-        class = "glyphicon glyphicon-log-in" 
+        class = "fas fa-window-minimize" 
         hoverable></i>
-      <i *ngIf = "state === 'docked'"
+      <i *ngIf = "state === 'docked' && !isMobile"
         (click) = "undock($event)" 
-        class = "glyphicon glyphicon-new-window" 
+        class = "fas fa-window-restore" 
         hoverable></i>
       <i *ngIf = "exitable"
         (click) = "exit($event)" 
-        class = "glyphicon glyphicon-remove" 
+        class = "fas fa-times" 
         hoverable></i>
     </div>
   </div>
diff --git a/src/components/components.module.ts b/src/components/components.module.ts
index 7f7f4f19cbede9efcc7f03dc13061878d19086d2..deeed608e8648a00074b0aae7edc17119613e5b0 100644
--- a/src/components/components.module.ts
+++ b/src/components/components.module.ts
@@ -1,12 +1,12 @@
 import { NgModule } from '@angular/core'
 import { FormsModule } from '@angular/forms'
+import { ScrollingModule } from '@angular/cdk/scrolling'
 import { BrowserAnimationsModule } from '@angular/platform-browser/animations'
 
 import {  MarkdownDom } from './markdown/markdown.component';
 
 import { SafeHtmlPipe } from '../util/pipes/safeHtml.pipe'
 import { ReadmoreComponent } from './readmoore/readmore.component';
-import { BrowserModule } from '@angular/platform-browser';
 import { HoverableBlockDirective } from './hoverableBlock.directive';
 import { DropdownComponent } from './dropdown/dropdown.component';
 import { TreeComponent } from './tree/tree.component';
@@ -24,11 +24,15 @@ import { FitlerRowsByVisibilityPipe } from './flatTree/filterRowsByVisibility.pi
 import { AppendSiblingFlagPipe } from './flatTree/appendSiblingFlag.pipe';
 import { ClusteringPipe } from './flatTree/clustering.pipe';
 import { TimerComponent } from './timer/timer.component';
+import { PillComponent } from './pill/pill.component';
+import { CommonModule } from '@angular/common';
+import { RadioList } from './radiolist/radiolist.component';
 
 
 @NgModule({
   imports : [
-    BrowserModule,
+    CommonModule,
+    ScrollingModule,
     FormsModule,
     BrowserAnimationsModule,
   ],
@@ -43,6 +47,8 @@ import { TimerComponent } from './timer/timer.component';
     ToastComponent,
     FlatTreeComponent,
     TimerComponent,
+    PillComponent,
+    RadioList,
 
     /* directive */
     HoverableBlockDirective,
@@ -71,6 +77,8 @@ import { TimerComponent } from './timer/timer.component';
     ToastComponent,
     FlatTreeComponent,
     TimerComponent,
+    PillComponent,
+    RadioList,
 
     SearchResultPaginationPipe,
     TreeSearchPipe,
diff --git a/src/components/dropdown/dropdown.component.ts b/src/components/dropdown/dropdown.component.ts
index 4b7a52da659b52b1d393ff2daaa23aba0f8f7363..139e1add801255f3fb651ceaffb2bbd2bad312d2 100644
--- a/src/components/dropdown/dropdown.component.ts
+++ b/src/components/dropdown/dropdown.component.ts
@@ -7,22 +7,6 @@ import { dropdownAnimation } from "./dropdown.animation";
   styleUrls : [
     `./dropdown.style.css`
   ],
-  styles : [
-    `
-ul > li.selected > span:before
-{
-  content: '\u2022';
-  width : 1em;
-  display:inline-block;
-}
-ul > li:not(.selected) > span:before
-{
-  content: ' ';
-  width : 1em;
-  display:inline-block;
-}  
-    `
-  ],
   animations:[
     dropdownAnimation
   ],
@@ -33,10 +17,14 @@ export class DropdownComponent{
 
   @Input() inputArray : any[] = []
   @Input() selectedItem : any | null = null
+  @Input() checkSelected: (selectedItem:any, item:any) => boolean = (si,i) => si === i
 
   @Input() listDisplay : (obj:any)=>string = (obj)=>obj.name
   @Input() activeDisplay : (obj:any|null)=>string = (obj)=>obj ? obj.name : `Please select an item.`
 
+  @Input() isMobile: boolean
+  @Input() darktheme: boolean
+
   @Output() itemSelected : EventEmitter<any> = new EventEmitter()
 
   @ViewChild('dropdownToggle',{read:ElementRef}) dropdownToggle : ElementRef
@@ -45,10 +33,7 @@ export class DropdownComponent{
 
   @HostListener('document:click',['$event'])
   close(event:MouseEvent){
-    /* FF <62 does not implement event.srcElement so use event.originalTarget to polyfill for FF */
-    const contains = event.srcElement 
-      ? this.dropdownToggle.nativeElement.contains(event.srcElement)
-      : this.dropdownToggle.nativeElement.contains((event as any).originalTarget)
+    const contains = this.dropdownToggle.nativeElement.contains(event.target)
     if(contains)
       this.openState = !this.openState
     else
diff --git a/src/components/dropdown/dropdown.style.css b/src/components/dropdown/dropdown.style.css
index 0d08444ec11328b06b3d0333f1a5d67240161d95..e13bd32eb58f18b15d6de37040e1547df1360f30 100644
--- a/src/components/dropdown/dropdown.style.css
+++ b/src/components/dropdown/dropdown.style.css
@@ -4,51 +4,21 @@
   display:block;
 }
 
-ul.dropdown-menu
-{
-  width:100%;
-  overflow-x:hidden;
-  display:inline-block;
-}
-
-ul.dropdown-menu,span.dropdown-item
-{
-  padding:0px;
-  border-radius:0px;
-  white-space: nowrap;
-  border:none;
-}
-
 span[dropdownToggle]:hover
 {
   cursor:default;
 }
-
-li[role="menuitem"],span[dropdownToggle]
-{
-  padding:0.5em 0.8em;
-  border-radius:0px;
-  cursor:default;
-}
-
-li[role="menuitem"]
-{
-  transition : opacity 0.3s ease,background-color 0.2s ease ;
-  opacity:0.75;
-}
-
-
-
-:host-context([darktheme="false"]) ul.dropdown-menu
+span[dropdownToggle]
 {
-  background-color:rgba(255,255,255,0.8);
+  display:block;
+  text-align:left;
 }
-
-:host-context([darktheme="false"]) li[role="menuitem"]:hover
+span[dropdownToggle]
 {
+  padding:0.5em 0.8em;
+  border-radius:0px;
   cursor:default;
-  background-color:rgba(128,128,128,0.2);
-  opacity:0.85;
+  white-space: nowrap;
 }
 
 :host-context([darktheme="false"]) span[dropdownToggle]
@@ -65,22 +35,6 @@ li[role="menuitem"]
 }
 
 
-:host-context([darktheme="true"]) ul.dropdown-menu
-{
-  background-color:rgba(30,30,30,0.8);
-}
-
-:host-context([darktheme="true"]) li[role="menuitem"]
-{
-  color:rgba(255,255,255,0.8);
-}
-
-:host-context([darktheme="true"]) li[role="menuitem"]:hover
-{
-  cursor:default;
-  background-color:rgba(128,128,128,0.2);
-}
-
 :host-context([darktheme="true"]) span[dropdownToggle]
 {
   border:none;
@@ -94,3 +48,11 @@ li[role="menuitem"]
   box-shadow:rgba(0,0,0,0.1);
   background-color:rgba(30,30,30,0.99);
 }
+
+radio-list
+{
+  width:100%;
+  height:0;
+  overflow-x:hidden;
+  display:block;
+}
diff --git a/src/components/dropdown/dropdown.template.html b/src/components/dropdown/dropdown.template.html
index 97207b21f69bb4e1cd2218eabb10dc207b31e27c..b31ed3fc131507c35127bbb1c5da3e5c52328439 100644
--- a/src/components/dropdown/dropdown.template.html
+++ b/src/components/dropdown/dropdown.template.html
@@ -1,24 +1,30 @@
+<!-- TODO remove dependency on ismobile -->
 <span
-  class = "btn btn-default"
-  [innerHTML] = "activeDisplay(selectedItem)"
-  hoverable
+  class="btn btn-default"
+  [innerHTML]="activeDisplay(selectedItem)"
+  [hoverable]="{translateY:-2}"
   #dropdownToggle
-  dropdownToggle>
+  dropdownToggle
+              
+  [ngStyle]="{'border': isMobile? 'none' : 'none', 'border-color': darktheme? 'white' : 'black', 'border-width': openState? '1px 1px 0 1px' : ''}">
+
 </span>
 
-<ul 
-  class="dropdown-menu"
-  role="menu"
-  [@showState] = "openState ? 'show' : 'hide'">
-  <li 
-    *ngFor="let input of inputArray" 
-    [ngClass]="selectedItem === input ? 'selected' : 'notselected'" 
-    (click)="itemSelected.emit(input); openState = false" 
-    role="menuitem">
-    
-    <span 
-      class="dropdown-item" 
-      [innerHTML] = "listDisplay(input)">
-    </span>
+<!-- needed to ensure dropdown width matches  -->
+<ul class="m-0 h-0 overflow-hidden">
+  <li *ngFor="let item of inputArray">
+    {{ listDisplay(item) }}
   </li>
-</ul>
\ No newline at end of file
+</ul>
+<radio-list
+  [ulClass]="'dropdown-menu shadow'"
+  (itemSelected)="itemSelected.emit($event)"
+  [listDisplay]="listDisplay"
+  [checkSelected]="checkSelected"
+  [selectedItem]="selectedItem"
+  [inputArray]="inputArray"
+  [@showState]="openState ? 'show' : 'hide'"
+  [isMobile]="isMobile"
+  [darktheme]="darktheme">
+
+</radio-list>
diff --git a/src/components/flatTree/flatTree.component.ts b/src/components/flatTree/flatTree.component.ts
index b82ec4f5b1c5ac9d720bf762a74852f7bcdcd2d4..bf1efd663bf2bb3f9af5615fb5284057f341be7c 100644
--- a/src/components/flatTree/flatTree.component.ts
+++ b/src/components/flatTree/flatTree.component.ts
@@ -1,4 +1,4 @@
-import { EventEmitter, Component, Input, Output, ChangeDetectionStrategy, ElementRef, OnDestroy, ChangeDetectorRef, ViewChildren, QueryList, AfterViewChecked, AfterViewInit, OnInit } from "@angular/core";
+import { EventEmitter, Component, Input, Output, ChangeDetectionStrategy } from "@angular/core";
 import { FlattenedTreeInterface } from "./flattener.pipe";
 
 /**
@@ -14,13 +14,15 @@ import { FlattenedTreeInterface } from "./flattener.pipe";
   changeDetection:ChangeDetectionStrategy.OnPush
 })
 
-export class FlatTreeComponent implements AfterViewChecked, AfterViewInit, OnInit, OnDestroy{
+export class FlatTreeComponent{
   @Input() inputItem : any = {
     name : 'Untitled',
     children : []
   }
   @Input() childrenExpanded : boolean = true
 
+  @Input() useDefaultList: boolean = false
+
   @Output() treeNodeClick : EventEmitter<any> = new EventEmitter()
 
   /* highly non-performant. rerenders each time on mouseover or mouseout */
@@ -31,60 +33,7 @@ export class FlatTreeComponent implements AfterViewChecked, AfterViewInit, OnIni
   @Input() findChildren : (item:any)=>any[] = (item)=>item.children ? item.children : [] 
   @Input() searchFilter : (item:any)=>boolean | null = ()=>true
 
-  @Input() flatTreeViewPort : HTMLElement
-
-  @ViewChildren('flatTreeStart',{read : ElementRef}) flatTreeStartCollection : QueryList<ElementRef>
-  @ViewChildren('flatTreeEnd',{read : ElementRef}) flatTreeEndCollection : QueryList<ElementRef>
-
-  intersectionObserver : IntersectionObserver
-
-  constructor(
-    private cdr:ChangeDetectorRef
-  ){
-
-  }
-
-  ngAfterViewChecked(){
-    if(this.intersectionObserver){
-      this.intersectionObserver.disconnect()
-      this.flatTreeStartCollection.forEach(er => this.intersectionObserver.observe(er.nativeElement))
-      this.flatTreeEndCollection.forEach(er => this.intersectionObserver.observe(er.nativeElement))
-    }
-  }
-
-  ngOnInit(){
-    if(this.flatTreeViewPort){
-      this.clusterNumber = 50
-    }
-  }
-
-  ngAfterViewInit(){
-
-    if(this.flatTreeViewPort){
-      this.intersectionObserver = new IntersectionObserver(entries => {
-        const currPos = entries
-          .filter(entry => entry.isIntersecting)
-          .filter(entry => Number(entry.target.getAttribute('clusterindex')) !== NaN )
-          .map(entry => Number(entry.target.getAttribute('clusterindex')))
-          .reduce((acc, clusterindex, i, array) => acc + (clusterindex / array.length), 0)
-        
-        if( currPos - this._currentPos >= 1 ){
-          this._currentPos = Math.round(currPos)
-          this.cdr.markForCheck()
-        }
-      },{
-        root: this.flatTreeViewPort,
-        rootMargin : '0px',
-        threshold : 0.1
-      })
-    }
-  }
-
-  ngOnDestroy(){
-    if(this.intersectionObserver){
-      this.intersectionObserver.disconnect()
-    }
-  }
+  public flattenedItems : any[] = []
 
   getClass(level:number){
     return [...Array(level+1)].map((v,idx) => `render-node-level-${idx}`).join(' ')
@@ -121,10 +70,4 @@ export class FlatTreeComponent implements AfterViewChecked, AfterViewInit, OnIni
       .some(id => this.isCollapsedById(id))
   }
 
-  private _currentPos : number = 0
-  public clusterNumber : number = Number.POSITIVE_INFINITY
-
-  showCluster(index:number){
-    return index <= this._currentPos + 1
-  }
 }
\ No newline at end of file
diff --git a/src/components/flatTree/flatTree.style.css b/src/components/flatTree/flatTree.style.css
index cc5bdf60a95b1ccbf06c3a71bb6fe2ea71d5c2f6..9ee266f33ce80735ba9ebae0689f01c031aaaeaf 100644
--- a/src/components/flatTree/flatTree.style.css
+++ b/src/components/flatTree/flatTree.style.css
@@ -1,37 +1,20 @@
 :host
 {
-  display: block;
+  display: flex;
+  flex-direction: column;
   height:100%;
 }
 
-[clusterContainer]
+cdk-virtual-scroll-viewport,
+.default-container
 {
-  position:relative;
-}
-
-[flatTreeStart]
-{
-  position: absolute;
-  top: 0;
-  height:50%;
-  width:1em;
-  opacity:0;
-  background-color:rgba(200,250,200,0.2);
-}
-
-[flatTreeEnd]
-{
-  position: absolute;
-  top: 0;
-  transform: translateY(100%);
-  width:1em;
-  opacity:0;
-  background-color:rgba(250,200,200,0.2);
-  height:50%;
+  flex: 1 0 auto;
+  overflow-x: hidden;
 }
 
 .render-node-text
 {
+  white-space: nowrap;
   cursor:default;
 }
 
@@ -185,4 +168,14 @@ span[renderText]
 .render-node-level-10 > .padding-block-container
 {
   left: -10em;
-}
\ No newline at end of file
+}
+
+.r-270
+{
+  transform: rotate(270deg);
+}
+
+[renderNode]
+{
+  height: 15px;
+}
diff --git a/src/components/flatTree/flatTree.template.html b/src/components/flatTree/flatTree.template.html
index de7151e12be34258ebcb2064afe5360d54b25ba4..6340f50b7992ab68a9b01213699fb5cd2db666a5 100644
--- a/src/components/flatTree/flatTree.template.html
+++ b/src/components/flatTree/flatTree.template.html
@@ -1,44 +1,79 @@
-<div *ngFor = "let flattenedItems of (inputItem | flattenTreePipe : findChildren | filterRowsByVisbilityPipe : findChildren : searchFilter | appendSiblingFlagPipe | clusteringPipe : clusterNumber ); let index = index" clusterContainer>
+<cdk-virtual-scroll-viewport
+  *ngIf="!useDefaultList"
+  (wheel)="$event.stopPropagation()"
+  itemSize="15">
 
-  <div [attr.clusterindex] = "index" flatTreeStart #flatTreeStart>
+  <div
+    *cdkVirtualFor="let flattenedItem of (inputItem | flattenTreePipe : findChildren | filterRowsByVisbilityPipe : findChildren : searchFilter | appendSiblingFlagPipe )" 
+    [ngClass]="getClass(flattenedItem.flattenedTreeLevel)"
+    class="text-nowrap"
+    [attr.flattenedtreelevel]="flattenedItem.flattenedTreeLevel" 
+    [attr.collapsed]="flattenedItem.collapsed ? flattenedItem.collapsed : false"
+    [attr.lvlId]="flattenedItem.lvlId"
+    [hidden]="collapseRow(flattenedItem) "
+    renderNode>
+
+    <span class="padding-block-container">
+      <span
+        *ngFor="let block of flattenedItem.siblingFlags"
+        [attr.hidemargin]="block"
+        class="padding-block">
+
+      </span>
+    </span>
+    <span
+      *ngIf="findChildren(flattenedItem).length > 0; else noChildren"
+      (click)="$event.stopPropagation(); toggleCollapse(flattenedItem)" >
+      <i [ngClass]="isCollapsed(flattenedItem) ? 'r-270' : ''" class="fas fa-chevron-down"></i>
+    </span>
+    <span
+      (click)="treeNodeClick.emit({event:$event,inputItem:flattenedItem})"
+      class="render-node-text"
+      [innerHtml]="flattenedItem | renderPipe : renderNode ">
+    </span>
   </div>
+</cdk-virtual-scroll-viewport>
+
+<div
+  *ngIf="useDefaultList"
+  class="overflow-auto">
 
-  <div *ngIf = "showCluster(index)">
+  <div class="overflow-hidden default-container">
     <div
-      *ngFor = "let flattenedItem of flattenedItems"
-      [ngClass] = "getClass(flattenedItem.flattenedTreeLevel)"
-      [attr.flattenedtreelevel] = "flattenedItem.flattenedTreeLevel" 
-      [attr.collapsed] = "flattenedItem.collapsed ? flattenedItem.collapsed : false"
-      [attr.lvlId] = "flattenedItem.lvlId"
-      [hidden] = "collapseRow(flattenedItem) "
+      *ngFor="let flattenedItem of (inputItem | flattenTreePipe : findChildren | filterRowsByVisbilityPipe : findChildren : searchFilter | appendSiblingFlagPipe )" 
+      [ngClass]="getClass(flattenedItem.flattenedTreeLevel)"
+      class="text-nowrap"
+      [attr.flattenedtreelevel]="flattenedItem.flattenedTreeLevel" 
+      [attr.collapsed]="flattenedItem.collapsed ? flattenedItem.collapsed : false"
+      [attr.lvlId]="flattenedItem.lvlId"
+      [hidden]="collapseRow(flattenedItem) "
       renderNode>
-    
-      <span class = "padding-block-container">
+  
+      <span class="padding-block-container">
         <span
-          *ngFor = "let block of flattenedItem.siblingFlags"
-          [attr.hidemargin] = "block"
-          class = "padding-block">
-    
+          *ngFor="let block of flattenedItem.siblingFlags"
+          [attr.hidemargin]="block"
+          class="padding-block">
+  
         </span>
       </span>
-      <span 
-        *ngIf = "findChildren(flattenedItem).length > 0; else noChildren"
-        (click) = "$event.stopPropagation(); toggleCollapse(flattenedItem)" >
-        <i [ngClass] = "isCollapsed(flattenedItem) ? 'glyphicon-chevron-right' : 'glyphicon-chevron-down'" class="glyphicon"></i>
+      <span
+        *ngIf="findChildren(flattenedItem).length > 0; else noChildren"
+        (click)="$event.stopPropagation(); toggleCollapse(flattenedItem)" >
+        <i [ngClass]="isCollapsed(flattenedItem) ? 'r-270' : ''" class="fas fa-chevron-down"></i>
       </span>
-      <ng-template #noChildren>
-        <i class="glyphicon glyphicon-none">
-    
-        </i>
-      </ng-template>
       <span
-        (click) = "treeNodeClick.emit({event:$event,inputItem:flattenedItem})"
-        class = "render-node-text"
-        [innerHtml] = "flattenedItem | renderPipe : renderNode ">
+        (click)="treeNodeClick.emit({event:$event,inputItem:flattenedItem})"
+        class="render-node-text"
+        [innerHtml]="flattenedItem | renderPipe : renderNode ">
       </span>
     </div>
   </div>
+</div>
 
-  <div [attr.clusterindex] = "index" flatTreeEnd #flatTreeEnd>
-  </div>
-</div>
\ No newline at end of file
+
+<ng-template #noChildren>
+  <i class="fas fa-none">
+
+  </i>
+</ng-template>
\ No newline at end of file
diff --git a/src/components/hoverableBlock.directive.ts b/src/components/hoverableBlock.directive.ts
index e05dc260d0f2693500c98318b5deb2c7350c56d3..bc0da29e43f6b99d73108d3db6f63633a659e922 100644
--- a/src/components/hoverableBlock.directive.ts
+++ b/src/components/hoverableBlock.directive.ts
@@ -1,4 +1,4 @@
-import { Directive, HostListener, HostBinding } from "@angular/core";
+import { Directive, HostListener, HostBinding, Input } from "@angular/core";
 import { DomSanitizer } from "@angular/platform-browser";
 
 @Directive({
@@ -15,6 +15,26 @@ import { DomSanitizer } from "@angular/platform-browser";
 
 export class HoverableBlockDirective{
 
+  @Input('hoverable')
+  config:any = {
+    disable: false,
+    translateY: -5
+  }
+
+  private _disable = false
+  private _translateY = -5
+
+  ngOnChanges(){
+    this._disable = this.config && !!this.config.disable
+    /**
+     * 0 is evaluated as falsy, but a valid number
+     * conditional tests for whether we need to fall back to default
+     */
+    this._translateY = this.config && this.config.translateY !== 0 && !!Number(this.config.translateY)
+      ? Number(this.config.translateY)
+      : -5
+  }
+
   @HostBinding('style.opacity')
   opacity : number = 0.9
 
@@ -26,13 +46,19 @@ export class HoverableBlockDirective{
 
   @HostListener('mouseenter')
   onMouseenter(){
+    if (this._disable) return
     this.opacity = 1.0
     this.boxShadow = this.sanitizer.bypassSecurityTrustStyle(`0 4px 6px 0 rgba(5,5,5,0.25)`)
-    this.transform = this.sanitizer.bypassSecurityTrustStyle(`translateY(-2%)`)
+    /**
+     * n.b. risk of XSS. But sincle translate Y is passed through Number, and corerced into a number,
+     * and using 5 as a fallback, it should be safe
+     */
+    this.transform = this.sanitizer.bypassSecurityTrustStyle(`translateY(${this._translateY}px)`)
   }
 
   @HostListener('mouseleave')
   onmouseleave(){
+    if (this._disable) return
     this.opacity = 0.9
     this.boxShadow = this.sanitizer.bypassSecurityTrustStyle(`0 4px 6px 0 rgba(5,5,5,0.1)`)
     this.transform = this.sanitizer.bypassSecurityTrustStyle(`translateY(0px)`)
diff --git a/src/components/pagination/pagination.component.ts b/src/components/pagination/pagination.component.ts
index fc962a2ae61f8423b9b898a7ad40bca1e7e5de0d..0e5ff89eefc188bd56b05c768ababcdd89310bb8 100644
--- a/src/components/pagination/pagination.component.ts
+++ b/src/components/pagination/pagination.component.ts
@@ -4,16 +4,18 @@ import { Component, Input, Output, EventEmitter } from '@angular/core'
   selector : 'pagination-component',
   templateUrl : './pagination.template.html',
   styleUrls : [
-    './pagination.style.css'
+    './pagination.style.css',
+    '../../css/darkBtns.css'
   ]
 })
 
-export class PaginationComponent{
+export class PaginationComponent {
   @Input() total : number = 0
   @Input() hitsPerPage : number = 15
   @Input() currentPage : number = 0
 
   @Output() paginationChange : EventEmitter<number> = new EventEmitter()
+  @Output() outOfBound: EventEmitter<number> = new EventEmitter()
 
   goto(pgnum:number){
     const emitValue = pgnum < 0 ? 
diff --git a/src/components/pagination/pagination.style.css b/src/components/pagination/pagination.style.css
index 2b06f6363c0b0d943f788e8be0077da07b8e75a2..ca46999f972a2f39ccd8e170eb6701551ef7a7e5 100644
--- a/src/components/pagination/pagination.style.css
+++ b/src/components/pagination/pagination.style.css
@@ -44,12 +44,6 @@ div.btn:hover:before
   opacity : 0.3;
 }
 
-:host-context([darktheme="true"]) div.btn.btn-default
-{
-  background-color:rgba(128,128,128,0.5);
-  color:rgba(240,240,240,0.9);
-}
-
 div.btn.btn-primary
 {
   transform:translateY(3%);
@@ -70,4 +64,9 @@ div.btn.btn-primary:before
   opacity : 0.1;
 
   pointer-events: none;
+}
+
+.pagination-control
+{
+  font-size:80%;
 }
\ No newline at end of file
diff --git a/src/components/pagination/pagination.template.html b/src/components/pagination/pagination.template.html
index cb53156b2a5e30acdf284bf2a9bb37c90a3f261b..1d3f14ae11e85731797eb47941ced54ef63bfc62 100644
--- a/src/components/pagination/pagination.template.html
+++ b/src/components/pagination/pagination.template.html
@@ -6,14 +6,14 @@
       (click)="gotoFirst()"
       >
 
-      <i class = "glyphicon glyphicon-fast-backward"></i>
+      <i class = "fas fa-fast-backward"></i>
     </div>
 
     <div 
       class = "btn btn-default btn-sm" 
       (click)="goto(currentPage - 1)"
       >
-      <i class = "glyphicon glyphicon-step-backward"></i>
+      <i class = "fas fa-step-backward"></i>
     </div>
 
     <div 
@@ -29,13 +29,13 @@
       class = "btn btn-default btn-sm" 
       (click)="goto(currentPage + 1)"
       >
-      <i class = "glyphicon glyphicon-step-forward"></i>
+      <i class = "fas fa-step-forward"></i>
     </div>
     <div 
       class = "btn btn-default btn-sm" 
       (click)="gotoLast()"
       >
-      <i class = "glyphicon glyphicon-fast-forward"></i>
+      <i class = "fas fa-fast-forward"></i>
     </div>
 
   </div>
diff --git a/src/components/panel/panel.component.ts b/src/components/panel/panel.component.ts
index 04c869d4f252b04370473ead6f9eb8677eb43b41..6b2c5095c1f04c39d1a85123e0f793163290d845 100644
--- a/src/components/panel/panel.component.ts
+++ b/src/components/panel/panel.component.ts
@@ -8,16 +8,13 @@ import { ParseAttributeDirective } from "../parseAttribute.directive";
   styleUrls : [
     `./panel.style.css`
   ],
-  animations : [
-    panelAnimations
-  ],
   host: {
     '[class]': 'getClassNames'
   },
   changeDetection:ChangeDetectionStrategy.OnPush
 })
 
-export class PanelComponent extends ParseAttributeDirective implements AfterContentChecked{
+export class PanelComponent extends ParseAttributeDirective {
 
   @Input() showHeading : boolean = true
   @Input() showBody : boolean = true
@@ -31,9 +28,7 @@ export class PanelComponent extends ParseAttributeDirective implements AfterCont
   @ViewChild('panelBody',{ read : ElementRef }) efPanelBody : ElementRef
   @ViewChild('panelFooter',{ read : ElementRef }) efPanelFooter : ElementRef
 
-  _fullHeight : number = 0
-
-  constructor(private cdr:ChangeDetectorRef){
+  constructor(){
     super()
   }
 
@@ -41,34 +36,11 @@ export class PanelComponent extends ParseAttributeDirective implements AfterCont
     return `panel ${this.containerClass === '' ? 'panel-default' : this.containerClass}`
   }
 
-  ngAfterContentChecked(){
-    /**
-     * TODO check performance implication. 
-     * setting height forces a repaint (?)
-     */
-    this.fullHeight = (this.efPanelBody ? this.efPanelBody.nativeElement.offsetHeight : 0) +
-      (this.efPanelFooter ? this.efPanelFooter.nativeElement.offsetHeight : 0)
-    this.cdr.detectChanges()
-  }
-
-  @HostBinding('class.panel-default')
-
-  set fullHeight(num:number){
-    this._fullHeight = num
-  }
-
-  get fullHeight(){
-    return this._fullHeight
-  }
-
-  toggleCollapseBody(event:Event){
+  toggleCollapseBody(_event:Event){
     if(this.bodyCollapsable){
       this.collapseBody = !this.collapseBody
-
-      // this.fullHeight = (this.efPanelBody ? this.efPanelBody.nativeElement.offsetHeight : 0) +
-      //   (this.efPanelFooter ? this.efPanelFooter.nativeElement.offsetHeight : 0)
+      this.showBody = !this.showBody
+      this.showFooter = !this.showFooter
     }
-    event.stopPropagation()
-    event.preventDefault()
   }
 }
diff --git a/src/components/panel/panel.style.css b/src/components/panel/panel.style.css
index 8cf87fc19f464e119eabba889b2e4b9e70c968f5..6acdc28bdb60de794b7556a987360da98d1be662 100644
--- a/src/components/panel/panel.style.css
+++ b/src/components/panel/panel.style.css
@@ -1,7 +1,7 @@
 :host
 {
   font-size:92%;
-  overflow:hidden;
+  overflow:visible;
   box-shadow: 0px 4px 16px -4px rgba(0,0,0,0.2);
   border-radius : 0;
   border:none;
@@ -11,7 +11,7 @@
 
 [bodyFooterContainer]
 {
-  overflow:hidden;
+  overflow:visible;
 }
 
 :host > .panel-heading
@@ -47,7 +47,13 @@ div.panel-body
   color:rgba(223,240,216  ,0.9);
 }
 
-:host-context([darktheme="true"]) div.panel-body
+div.l-card-body
+{
+  color:rgba(0, 0, 0, 0.9);
+  background-color:rgba(230, 230, 230, 0.8);
+}
+
+:host-context([darktheme="true"]) div.l-card-body
 {
   color:rgba(255,255,255,0.9);
   background-color:rgba(45,45,45,0.8);
diff --git a/src/components/panel/panel.template.html b/src/components/panel/panel.template.html
index 7f7c1af411784a2153456c412dc9f02429288e5e..d2c55e22b6daed1c0b223e86492e6429e34a143d 100644
--- a/src/components/panel/panel.template.html
+++ b/src/components/panel/panel.template.html
@@ -1,35 +1,32 @@
 <div
   *ngIf = "showHeading"
   class = "panel-heading"
-  (click) = "toggleCollapseBody($event)"
   hoverable>
 
-  <ng-content select="[heading]">
-  </ng-content>
-
 </div>
 
-<!-- While wrapping body and footer in a diffrent div adds a lot of overhead, it enables nice transition effects -->
-<div 
-  bodyFooterContainer>
-  <div
-    *ngIf = "showBody"
-    class = "panel-body"
-    [@collapseState] = "{ value : collapseBody ? 'collapsed' : 'visible', params : { fullHeight : fullHeight } }"
-    #panelBody>
-
-    <ng-content select="[body]">
-    </ng-content>
+<div class="l-card">
+  <div class="l-card-body">
+    <div
+      *ngIf="showHeading"
+      class="l-card-title"
+      (click)="toggleCollapseBody($event)"
+      [hoverable]="{disable:!bodyCollapsable}">
+      <ng-content select="[heading]">
+      </ng-content>
+    </div>
+    <!-- While wrapping body and footer in a diffrent div adds a lot of overhead, it enables nice transition effects -->
+    <div 
+      bodyFooterContainer>
+      <div
+        *ngIf="showBody"
+        class="l-card-text"
+        #panelBody>
 
-  </div>
-  <div
-    *ngIf = "showFooter"
-    class = "panel-footer"
-    [@collapseState] = "{ value : collapseBody ? 'collapsed' : 'visible', params : { fullHeight : fullHeight } }"
-    #panelFooter>
-    
-    <ng-content select="[footer]">
-    </ng-content>
+        <ng-content select="[body]">
+        </ng-content>
 
+      </div>
+    </div>
   </div>
 </div>
\ No newline at end of file
diff --git a/src/components/pill/pill.component.ts b/src/components/pill/pill.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4764320b46ec6782e56266e4489bf0d66d51b7b6
--- /dev/null
+++ b/src/components/pill/pill.component.ts
@@ -0,0 +1,27 @@
+import { Component, Input, Output, EventEmitter } from "@angular/core";
+
+@Component({
+  selector: 'pill-component',
+  templateUrl: './pill.template.html',
+  styleUrls: [
+    './pill.style.css'
+  ]
+})
+
+export class PillComponent{
+  @Input() title: string = 'Untitled Pill'
+  @Input() showClose: boolean = true
+  @Output() pillClicked: EventEmitter<boolean> = new EventEmitter()
+  @Output() closeClicked: EventEmitter<boolean> = new EventEmitter()
+
+  @Input() containerStyle: any = {
+    backgroundColor: 'grey'
+  }
+  @Input() closeBtnStyle: any = {
+    backgroundColor: 'lightgrey'
+  }
+
+  close() {
+    this.closeClicked.emit(true)
+  }
+}
\ No newline at end of file
diff --git a/src/components/pill/pill.style.css b/src/components/pill/pill.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e2aaf541a20bb78635edbb6ebf711fbf95a6756a
--- /dev/null
+++ b/src/components/pill/pill.style.css
@@ -0,0 +1,28 @@
+.pill-container
+{
+  padding: 0.2em 1em;
+  margin-left: 0.2em;
+  border-radius: 1em;
+  display: flex;
+  align-items: center;
+}
+
+.pill-title
+{
+  flex: 1 1 0;
+}
+
+.pill-close
+{
+  flex: 0 0 auto;
+  box-sizing: border-box;
+
+  line-height: 1;
+  margin-left: 0.4em;
+  margin-right: -0.6em;
+}
+
+:host
+{
+  display: inline-block;
+}
\ No newline at end of file
diff --git a/src/components/pill/pill.template.html b/src/components/pill/pill.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..9ce7bf503564227d872a657f5677907aca7052c9
--- /dev/null
+++ b/src/components/pill/pill.template.html
@@ -0,0 +1,15 @@
+<div
+  [ngStyle]="containerStyle"
+  class="pill-container">
+  <span
+    class="text-truncate pill-title">
+    {{ title }}
+  </span>
+  <div
+    [ngStyle]="closeBtnStyle"
+    class="pill-close rounded-circle"
+    (click)="close()"
+    *ngIf="showClose">
+    <i class="fas fa-times-circle"></i>
+  </div>
+</div>
\ No newline at end of file
diff --git a/src/components/radiolist/radiolist.component.ts b/src/components/radiolist/radiolist.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..486ee98ecb8d2bf3775d5a1e0e2aef22571c9e68
--- /dev/null
+++ b/src/components/radiolist/radiolist.component.ts
@@ -0,0 +1,49 @@
+import { Component, ChangeDetectionStrategy, Input, Output, EventEmitter } from "@angular/core";
+
+@Component({
+  selector: 'radio-list',
+  templateUrl: './radiolist.template.html',
+  styleUrls: [
+    './radiolist.style.css'
+  ],
+  styles: [
+    `
+    ul > li.selected > span:before
+    {
+      content: '\u2022';
+      width : 1em;
+      display:inline-block;
+    }
+    ul > li:not(.selected) > span:before
+    {
+      content: ' ';
+      width : 1em;
+      display:inline-block;
+    }  
+    `
+  ],
+  changeDetection: ChangeDetectionStrategy.OnPush
+})
+
+export class RadioList{
+  @Input() 
+  listDisplay : (item:any) => string = (obj) => obj.name
+
+  @Output()
+  itemSelected : EventEmitter<any> = new EventEmitter()
+
+  @Input()
+  selectedItem: any | null = null
+
+  @Input()
+  inputArray: any[] = []
+
+  @Input()
+  ulClass: string = ''
+  
+  @Input() checkSelected: (selectedItem:any, item:any) => boolean = (si,i) => si === i
+
+  @Input() isMobile: boolean
+  @Input() darktheme: boolean
+
+}
\ No newline at end of file
diff --git a/src/components/radiolist/radiolist.style.css b/src/components/radiolist/radiolist.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..d264f83b0676a602570939980e60c9abc93c2294
--- /dev/null
+++ b/src/components/radiolist/radiolist.style.css
@@ -0,0 +1,57 @@
+
+:host-context([darktheme="true"]) ul
+{
+  background-color:rgba(30,30,30);
+}
+
+:host-context([darktheme="true"]) li[role="menuitem"]
+{
+  color:rgba(255,255,255,0.8);
+}
+
+:host-context([darktheme="true"]) li[role="menuitem"]:hover
+{
+  cursor:default;
+  background-color:rgba(128,128,128,0.2);
+}
+
+
+:host-context([darktheme="false"]) ul
+{
+  background-color:rgba(255,255,255);
+}
+:host-context([darktheme="false"]) li[role="menuitem"]:hover
+{
+  cursor:default;
+  background-color:rgba(128,128,128,0.2);
+  opacity:0.85;
+}
+li[role="menuitem"]
+{
+  transition : opacity 0.3s ease,background-color 0.2s ease ;
+  opacity:0.75;
+}
+
+
+
+li[role="menuitem"]
+{
+  padding:0.5em 0.8em;
+  border-radius:0px;
+  cursor:default;
+}
+
+ul
+{
+  width:100%;
+  overflow-x:hidden;
+  display:inline-block;
+}
+
+ul,span.dropdown-item-1
+{
+  padding:0px;
+  border-radius:0px;
+  white-space: nowrap;
+  border:none;
+}
diff --git a/src/components/radiolist/radiolist.template.html b/src/components/radiolist/radiolist.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..f46b731b9636b6835a948e77c427112c8e4dcd58
--- /dev/null
+++ b/src/components/radiolist/radiolist.template.html
@@ -0,0 +1,16 @@
+<ul
+  [ngClass]="ulClass"
+  [ngStyle]="{'opacity': isMobile? '1' : '0.8', 'border-color': darktheme? 'white' : 'black'}"
+  role="menu">
+  <li 
+    *ngFor="let input of inputArray" 
+    [ngClass]="checkSelected(selectedItem, input) ? 'selected' : 'notselected'" 
+    (click)="itemSelected.emit({previous: selectedItem, current: input})" 
+    role="menuitem">
+    
+    <span 
+      class="dropdown-item-1" 
+      [innerHTML] = "listDisplay(input)">
+    </span>
+  </li>
+</ul>
\ No newline at end of file
diff --git a/src/components/readmoore/readmore.animations.ts b/src/components/readmoore/readmore.animations.ts
index 14a11e870249e49e2db2a18721fa10e6289524d1..aaf355c99adbae6b43191ad4ae749d3bf6200fcd 100644
--- a/src/components/readmoore/readmore.animations.ts
+++ b/src/components/readmoore/readmore.animations.ts
@@ -10,18 +10,18 @@ import {
 export const readmoreAnimations : AnimationTriggerMetadata = trigger('collapseState',[
   state('collapsed',
     style({ 'height' : '{{ collapsedHeight }}px' }),
-    { params : { collapsedHeight : 45, fullHeight : 200 } }
+    { params : { collapsedHeight : 45, fullHeight : 200, animationLength: 180 } }
   ),
   state('visible',
     style({ 'height' : '*' }),
-    { params : { collapsedHeight : 45, fullHeight : 200 } }
+    { params : { collapsedHeight : 45, fullHeight : 200, animationLength: 180 } }
   ),
   transition('collapsed => visible',[
-    animate('180ms', style({
+    animate('{{ animationLength }}ms', style({
       'height' : '{{ fullHeight }}px'
     }))
   ]),
   transition('visible => collapsed',[
-    animate('180ms')
+    animate('{{ animationLength }}ms')
   ])
 ])
\ No newline at end of file
diff --git a/src/components/readmoore/readmore.component.ts b/src/components/readmoore/readmore.component.ts
index eb1993d3a5292166d1781a0b154bb251873ad350..260fd13ab2f8a81cd5f1baf3e2f5f7c1b1bbdfca 100644
--- a/src/components/readmoore/readmore.component.ts
+++ b/src/components/readmoore/readmore.component.ts
@@ -13,6 +13,7 @@ import { readmoreAnimations } from "./readmore.animations";
 export class ReadmoreComponent implements OnChanges, AfterContentChecked{
   @Input() collapsedHeight : number = 45
   @Input() show : boolean = false
+  @Input() animationLength: number = 180
   @ViewChild('contentContainer') contentContainer : ElementRef
   
   public fullHeight : number = 200
diff --git a/src/components/readmoore/readmore.template.html b/src/components/readmoore/readmore.template.html
index a54e54bee9f5b799b7575d78c0ab53d344122e2a..20fdb98aeca3cfb760d23090d3cde70888b5c63c 100644
--- a/src/components/readmoore/readmore.template.html
+++ b/src/components/readmoore/readmore.template.html
@@ -1,5 +1,5 @@
 <div
-  [@collapseState] = "{ value : show ? 'visible' : 'collapsed', params : { collapsedHeight : collapsedHeight, fullHeight : fullHeight } }"
+  [@collapseState] = "{ value : show ? 'visible' : 'collapsed', params : { collapsedHeight: collapsedHeight, fullHeight: fullHeight, animationLength: animationLength } }"
   content>
   <div #contentContainer>
     <ng-content>
@@ -9,10 +9,10 @@
 <div 
   (click)="toggle($event)" 
   sliver
-  hoverable>
+  [hoverable]="{translateY:-1}">
 
   <i 
-    [ngClass] = "show ? 'glyphicon-chevron-up' : 'glyphicon-chevron-down'" 
-    class = "glyphicon">
+    [ngClass] = "show ? 'fa-chevron-up' : 'fa-chevron-down'" 
+    class = "fas">
   </i>
 </div>
\ No newline at end of file
diff --git a/src/components/toast/toast.component.ts b/src/components/toast/toast.component.ts
index 93b9233ba7b4048b8923cb282b4647530591650a..c0ca259a5e4bbf2da6d61f4027a81a15ad44bd93 100644
--- a/src/components/toast/toast.component.ts
+++ b/src/components/toast/toast.component.ts
@@ -12,6 +12,7 @@ import { toastAnimation } from "./toast.animation";
 
 export class ToastComponent{
   @Input() message : string 
+  @Input() htmlMessage: string
   @Input() timeout : number = 0
   @Input() dismissable : boolean = true
 
diff --git a/src/components/toast/toast.template.html b/src/components/toast/toast.template.html
index 17e173c70db4a6228c917cff8e5db70d2961c097..95032c74b9fbc154f0bd1e7aa9542f3453bf4fa0 100644
--- a/src/components/toast/toast.template.html
+++ b/src/components/toast/toast.template.html
@@ -4,12 +4,26 @@
 
     </ng-template>
   </div>
-  <div message *ngIf = "message">
+  <div message
+    [innerHTML]="htmlMessage"
+    *ngIf = "htmlMessage">
+  </div>
+  <div
+    message
+    *ngIf="message && !htmlMessage">
     {{ message }}
   </div>
-  <div (click) = "dismiss($event)" *ngIf = "dismissable" close>
-    <i class = "glyphicon glyphicon-remove"></i>
+  <div
+    (click)="dismiss($event)"
+    class="ml-2"
+    *ngIf="dismissable" close>
+    <i class="fas fa-times"></i>
   </div>
-  <timer-component (timerEnd)="dismissed.emit(false)" [pause] = "hover" [timeout] = "timeout" timer>
+  <timer-component
+    *ngIf="timeout > 0"
+    (timerEnd)="dismissed.emit(false)"
+    [pause]="hover"
+    [timeout]="timeout"
+    timer>
   </timer-component>
 </div>
\ No newline at end of file
diff --git a/src/components/tree/tree.component.ts b/src/components/tree/tree.component.ts
index 4bc3063a2111bbc742cbabb7a2808d702de07288..5ec84da8ecfb4ed956d4ccc95bcb32c383004d02 100644
--- a/src/components/tree/tree.component.ts
+++ b/src/components/tree/tree.component.ts
@@ -96,10 +96,10 @@ export class TreeComponent extends ParseAttributeDirective implements OnChanges,
     return this.children ? 
       this.children.length > 0 ?
         this.childrenExpanded ? 
-          'glyphicon-chevron-down' :
-          'glyphicon-chevron-right' :
-        'glyphicon-none' :
-      'glyphicon-none'
+          'fa-chevron-down' :
+          'fa-chevron-right' :
+        'fa-none' :
+      'fa-none'
   }
 
   public handleEv(event:Event){
diff --git a/src/components/tree/tree.style.css b/src/components/tree/tree.style.css
index 1fa2da45772b8c39ceefd2d684ecc91794409bcb..b05982290b10f2674d9868aa9d958274d762979d 100644
--- a/src/components/tree/tree.style.css
+++ b/src/components/tree/tree.style.css
@@ -9,7 +9,7 @@ div[itemContainer]
   display:flex;
 }
 
-div[itemContainer] > [glyphicon]
+div[itemContainer] > [fas]
 {
   flex: 0 0 1.2em;
   align-self: center;
diff --git a/src/components/tree/tree.template.html b/src/components/tree/tree.template.html
index 8e7dff2d794b095bd4eaf712bac498339a5bb96c..10b6318bfe862cd8da5813da78aabd16e3ff6782 100644
--- a/src/components/tree/tree.template.html
+++ b/src/components/tree/tree.template.html
@@ -4,8 +4,8 @@
     <i
       (click) = "toggleChildrenShow($event)"
       [ngClass] = "chevronClass"
-      class = "glyphicon"
-      glyphicon>
+      class = "fas"
+      fas>
     </i>
     <span 
       (mouseleave)="handleMouseLeave({inputItem:inputItem,node:this});handleEv($event)"
diff --git a/src/css/darkBtns.css b/src/css/darkBtns.css
new file mode 100644
index 0000000000000000000000000000000000000000..35e4e24ea3ba616e1b5227c7a874758d6d957f21
--- /dev/null
+++ b/src/css/darkBtns.css
@@ -0,0 +1,12 @@
+
+:host-context([darktheme="true"]) .btn.btn-default
+{
+  background-color:rgba(128,128,128,0.5);
+  color:rgba(240,240,240,0.9);
+  border: none;
+}
+
+:host-context([darktheme="true"]) .btn.btn-default > *
+{
+  font-size: 100%;
+}
\ No newline at end of file
diff --git a/src/index.html b/src/index.html
index 55ed7b6156871cd5192c5499c6607793000deddd..3a45da9bca5a8f7038f93470f1089bdfe3d696b6 100644
--- a/src/index.html
+++ b/src/index.html
@@ -4,7 +4,8 @@
   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   <meta http-equiv="X-UA-Compatible" content="ie=edge">
-  <link rel = "stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
+  <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
+  <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.8.1/css/all.css" integrity="sha384-50oBUHEmvpQ+1lW4y57PTFmhCaXp0ML5d60M1M7uH2+nqUivzIebhndOJK28anvf" crossorigin="anonymous">
   <link rel = "stylesheet" href = "extra_styles.css">
   <link rel = "stylesheet" href = "plugin_styles.css">
   <title>Interactive Atlas Viewer</title>
@@ -48,4 +49,4 @@
 
   </script>
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/src/main.module.ts b/src/main.module.ts
index f122c7d48a38e97d62ac2b7593636100e1f46bbc..2f9b6680dd711e089a9ee63d7de9ff0924c33c57 100644
--- a/src/main.module.ts
+++ b/src/main.module.ts
@@ -1,10 +1,11 @@
 import { NgModule } from "@angular/core";
 import { ComponentsModule } from "./components/components.module";
+import { DragDropModule } from '@angular/cdk/drag-drop'
 import { UIModule } from "./ui/ui.module";
 import { LayoutModule } from "./layouts/layout.module";
 import { AtlasViewer } from "./atlasViewer/atlasViewer.component";
-import { StoreModule } from "@ngrx/store";
-import { viewerState, dataStore,spatialSearchState,uiState, ngViewerState, pluginState } from "./services/stateStore.service";
+import { StoreModule, Store, select } from "@ngrx/store";
+import { viewerState, dataStore,spatialSearchState,uiState, ngViewerState, pluginState, viewerConfigState } from "./services/stateStore.service";
 import { GetNamesPipe } from "./util/pipes/getNames.pipe";
 import { CommonModule } from "@angular/common";
 import { GetNamePipe } from "./util/pipes/getName.pipe";
@@ -13,19 +14,30 @@ import { FormsModule } from "@angular/forms";
 import { AtlasViewerDataService } from "./atlasViewer/atlasViewer.dataService.service";
 import { WidgetUnit } from "./atlasViewer/widgetUnit/widgetUnit.component";
 import { WidgetServices } from './atlasViewer/widgetUnit/widgetService.service'
-import { GlyphiconTooltipScreenshotDirective,GlyphiconTooltipInfoSignDirective,GlyphiconTooltipLogInDirective,GlyphiconTooltipNewWindowDirective,GlyphiconTooltipQuestionSignDirective,GlyphiconTooltipRemoveDirective,GlyphiconTooltipRemoveSignDirective } from "./util/directives/glyphiconTooltip.directive";
+import { fasTooltipScreenshotDirective,fasTooltipInfoSignDirective,fasTooltipLogInDirective,fasTooltipNewWindowDirective,fasTooltipQuestionSignDirective,fasTooltipRemoveDirective,fasTooltipRemoveSignDirective } from "./util/directives/glyphiconTooltip.directive";
 import { TooltipModule } from "ngx-bootstrap/tooltip";
+import { TabsModule } from 'ngx-bootstrap/tabs'
 import { ModalModule } from 'ngx-bootstrap/modal'
 import { ModalUnit } from "./atlasViewer/modalUnit/modalUnit.component";
 import { AtlasViewerURLService } from "./atlasViewer/atlasViewer.urlService.service";
 import { ToastComponent } from "./components/toast/toast.component";
 import { GetFilenameFromPathnamePipe } from "./util/pipes/getFileNameFromPathName.pipe";
-import { FilterNameBySearch } from "./util/pipes/filterNameBySearch.pipe";
 import { AtlasViewerAPIServices } from "./atlasViewer/atlasViewer.apiService.service";
 import { PluginUnit } from "./atlasViewer/pluginUnit/pluginUnit.component";
 import { NewViewerDisctinctViewToLayer } from "./util/pipes/newViewerDistinctViewToLayer.pipe";
 import { ToastService } from "./services/toastService.service";
 import { AtlasWorkerService } from "./atlasViewer/atlasViewer.workerService.service";
+import { HelpDirective } from "./util/directives/help.directive";
+import { ToastContainerDirective } from "./util/directives/toastContainer.directive";
+import { DockedContainerDirective } from "./util/directives/dockedContainer.directive";
+import { FloatingContainerDirective } from "./util/directives/floatingContainer.directive";
+import { PluginFactoryDirective } from "./util/directives/pluginFactory.directive";
+import { FloatingMouseContextualContainerDirective } from "./util/directives/floatingMouseContextualContainer.directive";
+import { AuthService } from "./services/auth.service";
+import { ViewerConfiguration } from "./services/state/viewerConfig.store";
+import { FixedMouseContextualContainerDirective } from "./util/directives/FixedMouseContextualContainerDirective.directive";
+import { DatabrowserService } from "./ui/databrowserModule/databrowser.service";
+import { TransformOnhoverSegmentPipe } from "./atlasViewer/onhoverSegment.pipe";
 
 @NgModule({
   imports : [
@@ -33,17 +45,20 @@ import { AtlasWorkerService } from "./atlasViewer/atlasViewer.workerService.serv
     CommonModule,
     LayoutModule,
     ComponentsModule,
+    DragDropModule,
     UIModule,
     
     ModalModule.forRoot(),
     TooltipModule.forRoot(),
+    TabsModule.forRoot(),
     StoreModule.forRoot({
-      viewerState ,
-      dataStore ,
+      pluginState,
+      viewerConfigState,
+      ngViewerState,
+      viewerState,
+      dataStore,
       spatialSearchState,
       uiState,
-      ngViewerState,
-      pluginState
     })
   ],
   declarations : [
@@ -53,19 +68,26 @@ import { AtlasWorkerService } from "./atlasViewer/atlasViewer.workerService.serv
     PluginUnit,
 
     /* directives */
-    GlyphiconTooltipScreenshotDirective,
-    GlyphiconTooltipInfoSignDirective,
-    GlyphiconTooltipLogInDirective,
-    GlyphiconTooltipNewWindowDirective,
-    GlyphiconTooltipQuestionSignDirective,
-    GlyphiconTooltipRemoveDirective,
-    GlyphiconTooltipRemoveSignDirective,
+    fasTooltipScreenshotDirective,
+    fasTooltipInfoSignDirective,
+    fasTooltipLogInDirective,
+    fasTooltipNewWindowDirective,
+    fasTooltipQuestionSignDirective,
+    fasTooltipRemoveDirective,
+    fasTooltipRemoveSignDirective,
+    HelpDirective,
+    ToastContainerDirective,
+    DockedContainerDirective,
+    FloatingContainerDirective,
+    PluginFactoryDirective,
+    FloatingMouseContextualContainerDirective,
+    FixedMouseContextualContainerDirective,
 
     /* pipes */
     GetNamesPipe,
     GetNamePipe,
+    TransformOnhoverSegmentPipe,
     GetFilenameFromPathnamePipe,
-    FilterNameBySearch,
     NewViewerDisctinctViewToLayer
   ],
   entryComponents : [
@@ -81,6 +103,13 @@ import { AtlasWorkerService } from "./atlasViewer/atlasViewer.workerService.serv
     AtlasViewerAPIServices,
     ToastService,
     AtlasWorkerService,
+    AuthService,
+
+    /**
+     * TODO
+     * once nehubacontainer is separated into viewer + overlay, migrate to nehubaContainer module
+     */
+    DatabrowserService
   ],
   bootstrap : [
     AtlasViewer
@@ -88,5 +117,24 @@ import { AtlasWorkerService } from "./atlasViewer/atlasViewer.workerService.serv
 })
 
 export class MainModule{
+  
+  constructor(
+    authServce: AuthService,
+    store: Store<ViewerConfiguration>,
 
+    /**
+     * instantiate singleton
+     * allow for pre fetching of dataentry
+     * TODO only fetch when traffic is idle
+     */
+    dbSerivce: DatabrowserService
+  ){
+    authServce.authReloadState()
+    store.pipe(
+      select('viewerConfigState')
+    ).subscribe(({ gpuLimit }) => {
+      if (gpuLimit)
+        window.localStorage.setItem('iv-gpulimit', gpuLimit.toString())
+    })
+  }
 }
\ No newline at end of file
diff --git a/src/plugin_examples/README.md b/src/plugin_examples/README.md
index 87eea6043b05daff5b00543de0be7494c60643d3..1549c0963fcea559271259fa0fd121187394e066 100644
--- a/src/plugin_examples/README.md
+++ b/src/plugin_examples/README.md
@@ -25,7 +25,8 @@ The manifest JSON file describes the metadata associated with the plugin.
       "nestedKey1" : "nestedValue1"
     }
   },
-  "initStateUrl": "http://LINK-TO-PLUGIN-STATE"
+  "initStateUrl": "http://LINK-TO-PLUGIN-STATE",
+  "persistency": false
 }
 ```
 *NB* 
@@ -98,9 +99,10 @@ The script will always be appended **after** the rendering of the template.
 })()
 ```
 *NB*
+- JS is loaded and executed **before** the attachment of DOM (template). This is to allow webcomponents have a chance to be loaded. If your script needs the DOM to be attached, use a `setTimeout` callback to delay script execution.
 - ensure the script is scoped locally, instead of poisoning the global scope
 - for every observable subscription, call *unsubscribe()* in the *onShutdown* callback
 - some frameworks such as *jquery2*, *jquery3*, *react/reactdom* and *webcomponents* can be loaded via *interactiveViewer.pluinControl.loadExternalLibraries([LIBRARY_NAME_1, LIBRARY_NAME_2])*. if the libraries are loaded, remember to hook *interactiveViewer.pluginControl.unloadExternalLibraries([LIBRARY_NAME_1,LIBRARY_NAME_2])* in the *onShutdown* callback
 - when/if using webcomponents, please be aware that the `connectedCallback()` and `disconnectedCallback()` will be called everytime user toggle between *floating* and *docked* modes. 
-- when user navigate to a new template all existing widgets will be destroyed.
+- when user navigate to a new template all existing widgets will be destroyed, unless the `persistency` is set to `true` in `mannifest.json`.
 - for a list of APIs, see [plugin_api.md](plugin_api.md)
diff --git a/src/plugin_examples/plugin_api.md b/src/plugin_examples/plugin_api.md
index ad769c5deb46c433afa8f58f124e91ff9b718e6b..131e564e47fc8852553f0815d14ce8c23baca2aa 100644
--- a/src/plugin_examples/plugin_api.md
+++ b/src/plugin_examples/plugin_api.md
@@ -100,7 +100,7 @@ window.interactiveViewer
   - *remove3DLandmarks(IDs)* removes the landmarks by their IDs
   ```js
   window.interactiveViewer.viewerHandle
-    .remove3DLandmarks(['fzj-xg-jugex-1', 'fzj-xg-jugex-1'])
+    .remove3DLandmarks(['fzj-xg-jugex-1', 'fzj-xg-jugex-2'])
   /* removes the landmarks added above */
   ```
 
@@ -136,9 +136,12 @@ window.interactiveViewer
     - *show()* : Show the toast
     - *hide()* : Dynamically hides the toast
     - message : message on the toast
+    - htmlMessage : HTML message. If used to display user content, beware of script injection. Angular strips `style` attribute, so use `class` and bootstrap for styling.
     - dismissable : allow user dismiss the toast via x 
     - timeout : auto hide (in ms). set to 0 for not auto hide.
 
+  - *launchNewWidget(manifest)* returns a Promise. expects a JSON object, with the same key value as a plugin manifest. the *name* key must be unique, or the promise will be rejected. 
+  
 - pluginControl
 
   - *loadExternalLibraries([LIBRARY_NAME_1,LIBRARY_NAME_2])* Function that loads external libraries. Pass the name of the libraries as an Array of string, and returns a Promise. When promise resolves, the libraries are loaded. **n.b.** while unlikely, there is a possibility that multiple requests to load external libraries in quick succession can cause the promise to resolve before the library is actually loaded. 
@@ -159,12 +162,12 @@ window.interactiveViewer
   - **[PLUGINNAME]** returns a plugin handler. This would be how to interface with the plugins.
 
     
-    - *blink(sec?:number)* : Function that causes the floating widget to blink, attempt to grab user attention
-    - *shutdown()* : Function that causes the widget to shutdown dynamically. (triggers onShutdown callback)
+    - *blink(sec?:number)* : Function that causes the floating widget to blink, attempt to grab user attention (silently fails if called on startup).
+    - *shutdown()* : Function that causes the widget to shutdown dynamically. (triggers onShutdown callback, silently fails if called on startup)
     - *onShutdown(callback)* : Attaches a callback function, which is called when the plugin is shutdown.
     - *initState* : passed from `manifest.json`. Useful for setting initial state of the plugin. Can be any JSON valid value (array, object, string).
     - *initStateUrl* : passed from `manifest.json`. Useful for setting initial state of the plugin.  Can be any JSON valid value (array, object, string).
-    - *setInitManifestUrl(url|null)* set/unset the url for a manifest json that will be fetched on atlas viewer startup. the argument should be a valid URL, has necessary CORS header, and returns a valid manifest json file. null will unset the search param.
+    - *setInitManifestUrl(url|null)* set/unset the url for a manifest json that will be fetched on atlas viewer startup. the argument should be a valid URL, has necessary CORS header, and returns a valid manifest json file. null will unset the search param. Useful for passing/preserving state. If called multiple times, the last one will take effect.
 
     ```js
     const pluginHandler = window.interactiveViewer.pluginControl[PLUGINNAME]
diff --git a/src/res/css/extra_styles.css b/src/res/css/extra_styles.css
index 96f68781bdfb562b982f9809a96de6181d06e15d..512af93ba31be4eee8ef8f8ccf558a345fc28be9 100644
--- a/src/res/css/extra_styles.css
+++ b/src/res/css/extra_styles.css
@@ -2,6 +2,7 @@ html
 {
   width:100%;
   height:100%;
+  font-size:90%;
 }
 body
 {
@@ -10,7 +11,7 @@ body
   margin:0;
   border:0;
 
-  /* required for glyphicon tooltip directives */
+  /* required for fas tooltip directives */
   overflow:hidden;
 }
 div.scale-bar-container
@@ -149,7 +150,7 @@ markdown-dom pre code
   background-color:rgba(150,150,0,0.5);
 }
 
-.glyphicon-none
+.fa-none
 {
   width:1em;
 }
@@ -244,4 +245,98 @@ markdown-dom pre code
 {
   background-color: rgba(70, 70 , 70, 1.0);
   color: rgba(255, 255, 255, 1.0);
+}
+
+.darktheme.popover
+{
+  background-color:rgba(0, 0, 0, 0.8);
+}
+
+.darktheme.popover .popover-body
+{
+  color:white;
+}
+
+.darktheme.popover.popover-bottom>.arrow::after
+{
+  border-bottom-color: rgba(0, 0, 0, 0.8);
+}
+
+.darktheme.popover.popover-right>.arrow::after
+{
+  border-right-color: rgba(0, 0, 0, 0.8);
+}
+
+.r-90
+{
+  transform: rotate(90deg)!important;
+}
+
+.ws-no-wrap
+{
+  white-space: nowrap!important;
+}
+.ws-initial
+{
+  white-space: initial!important;
+}
+
+.mw-100
+{
+  max-width: 100%!important;
+}
+
+.mw-50
+{
+  max-width: 50%!important;
+}
+
+.mw-60
+{
+  max-width: 60%!important;
+}
+
+.mh-20em
+{
+  max-height: 20em;
+}
+
+.pe-all
+{
+  pointer-events: all;
+}
+
+.t-a-ease-500
+{
+  transition: all ease 500ms;
+}
+
+.h-0
+{
+  height: 0px;
+}
+
+.pe-none
+{
+  pointer-events: none;
+}
+
+.h-100
+{
+  height:100%;
+}
+
+.overflow-x-hidden
+{
+  overflow-x:hidden;
+}
+
+.muted
+{
+  opacity : 0.5!important;
+}
+
+.text-semi-transparent
+{
+  opacity: 0.5;
 }
\ No newline at end of file
diff --git a/src/res/css/plugin_styles.css b/src/res/css/plugin_styles.css
index f69c0a7c5638f9efed54cb6157018fc4ba1ee38a..6293983f2091641ec1659c85ffab9049101a2679 100644
--- a/src/res/css/plugin_styles.css
+++ b/src/res/css/plugin_styles.css
@@ -32,6 +32,7 @@
 /* colour */
 [darktheme="true"] [plugincontainer] .btn,
 [darktheme="true"] [plugincontainer] input[type="text"],
+[darktheme="true"] [plugincontainer] input[type="number"],
 [darktheme="true"] [plugincontainer] .panel
 {
   background-color:rgba(81,81,81,0.8);
diff --git a/src/res/cvtLyon.js b/src/res/cvtLyon.js
new file mode 100644
index 0000000000000000000000000000000000000000..073d04bca2c38e6bb48d2f436f1ef00d0da386c4
--- /dev/null
+++ b/src/res/cvtLyon.js
@@ -0,0 +1,23 @@
+const fs = require('fs')
+fs.readFile(
+  './ext/***REMOVED***.json', 
+  // './ext/***REMOVED***.json', 
+  'utf-8', (err, data) => {
+  const json = JSON.parse(data)
+  const newFile = json.map(item => {
+    return {
+      name: item.name.replace('DESj', 'item2'),
+      // name: item.name.replace('BATg', 'item1'),
+      templateSpace: item.templateSpace,
+      geometry: item.geometry
+    }
+  })
+
+  fs.writeFile(
+    './raw/exportForOliver/item2.json', 
+    // './raw/exportForOliver/item1.json', 
+    JSON.stringify(newFile), 'utf-8', err => {
+    if (err) throw err
+    console.log('done')
+  })
+})
\ No newline at end of file
diff --git a/src/res/ext/MNI152.json b/src/res/ext/MNI152.json
index 6613ce28a1ca8c300ba5626580cb0e55ba599bd6..204f5bb8f44a11e7819ce0c1f2ccbd3d891c4159 100644
--- a/src/res/ext/MNI152.json
+++ b/src/res/ext/MNI152.json
@@ -1 +1 @@
-{"name":"MNI 152 ICBM 2009c Nonlinear Asymmetric","type":"template","species":"Human","useTheme":"dark","ngId":"mni152","nehubaConfigURL":"res/json/MNI152NehubaConfig.json","parcellations":[{"ngId":"fibre bundle long","type":"parcellation","surfaceParcellation":true,"ngData":null,"name":"Fibre Bundle Atlas - Long Bundle","regions":[{"name":"Arcuate - Left","children":[],"labelIndex":"1"},{"name":"Arcuate - Right","children":[],"labelIndex":"31"},{"name":"Arcuate_Anterior - Left","children":[],"labelIndex":"2"},{"name":"Arcuate_Anterior - Right","children":[],"labelIndex":"32"},{"name":"Arcuate_Posterior - Left","children":[],"labelIndex":"3"},{"name":"Arcuate_Posterior - Right","children":[],"labelIndex":"33"},{"name":"Cingulum_Long - Left","children":[],"labelIndex":"4"},{"name":"Cingulum_Long - Right","children":[],"labelIndex":"34"},{"name":"Cingulum_Short - Left","children":[],"labelIndex":"5"},{"name":"Cingulum_Short - Right","children":[],"labelIndex":"35"},{"name":"Cingulum_Temporal - Left","children":[],"labelIndex":"6"},{"name":"Cingulum_Temporal - Right","children":[],"labelIndex":"36"},{"name":"CorpusCallosum_Body","children":[],"labelIndex":"7"},{"name":"CorpusCallosum_Genu","children":[],"labelIndex":"8"},{"name":"CorpusCallosum_Rostrum","children":[],"labelIndex":"9"},{"name":"CorpusCallosum_Splenium","children":[],"labelIndex":"10"},{"name":"CorticoSpinalTract - Left","children":[],"labelIndex":"11"},{"name":"CorticoSpinalTract - Right","children":[],"labelIndex":"41"},{"name":"ExternalCapsule - Left","children":[],"labelIndex":"12"},{"name":"ExternalCapsule - Right","children":[],"labelIndex":"42"},{"name":"Fornix - Left","children":[],"labelIndex":"13"},{"name":"Fornix - Right","children":[],"labelIndex":"43"},{"name":"InferiorFrontoOccipital - Left","children":[],"labelIndex":"14"},{"name":"InferiorFrontoOccipital - Right","children":[],"labelIndex":"44"},{"name":"InferiorLongitudinal - Left","children":[],"labelIndex":"15"},{"name":"InferiorLongitudinal - Right","children":[],"labelIndex":"45"},{"name":"InferiorLongitudinal_Lateral - Left","children":[],"labelIndex":"16"},{"name":"InferiorLongitudinal_Lateral - Right","children":[],"labelIndex":"46"},{"name":"MediumLongitudinal - Left","children":[],"labelIndex":"17"},{"name":"MediumLongitudinal - Right","children":[],"labelIndex":"47"},{"name":"SpinoThalamicTract - Left","children":[],"labelIndex":"18"},{"name":"SpinoThalamicTract - Right","children":[],"labelIndex":"48"},{"name":"ThalamicRadiations_Anterior - Left","children":[],"labelIndex":"19"},{"name":"ThalamicRadiations_Anterior - Right","children":[],"labelIndex":"49"},{"name":"ThalamicRadiations_Posterior - Left","children":[],"labelIndex":"20"},{"name":"ThalamicRadiations_Posterior - Right","children":[],"labelIndex":"50"},{"name":"Uncinate - Left","children":[],"labelIndex":"21"},{"name":"Uncinate - Right","children":[],"labelIndex":"51"}]},{"ngId":"fibre bundle short","type":"parcellation","surfaceParcellation":true,"ngData":null,"name":"Fibre Bundle Atlas - Short Bundle","regions":[{"name":"Left Hemisphere","children":[{"name":"Caudal Middle Frontal - Precuneus","children":[{"name":"lh_CAC-PrCu_0","children":[],"labelIndex":"38","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CAC-PrCu_0.nii"}]},{"name":"Caudal Middle Frontal – Pars Opercularis","children":[{"name":"lh_CMF-Op_0","children":[],"labelIndex":"37","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-Op_0.nii"}]},{"name":"Caudal Middle Frontal - Poscentral","children":[{"name":"lh_CMF-PoC_0","children":[],"labelIndex":"62","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-PoC_0.nii"}]},{"name":"Caudal Middle Frontal - Precentral","children":[{"name":"lh_CMF-PrC_0","children":[],"labelIndex":"97","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-PrC_0.nii"},{"name":"lh_CMF-PrC_1","children":[],"labelIndex":"99","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-PrC_1.nii"}]},{"name":"Caudal Middle Frontal – Rostral Middle Frontal","children":[{"name":"lh_CMF-RMF_0","children":[],"labelIndex":"49","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-RMF_0.nii"}]},{"name":"Caudal Middle Frontal – Superior Frontal","children":[{"name":"lh_CMF-SF_0","children":[],"labelIndex":"68","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_CMF-SF_0.nii"}]},{"name":"Fusiform – Lateral Occipital","children":[{"name":"lh_Fu-LO_0","children":[],"labelIndex":"51","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_Fu-LO_0.nii"}]},{"name":"Isthmus Cingulate - Precuneus","children":[{"name":"lh_IC-PrCu_0","children":[],"labelIndex":"40","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IC-PrCu_0.nii"}]},{"name":"Inferior Parietal – Inferior Temporal","children":[{"name":"lh_IP-IT_0","children":[],"labelIndex":"59","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-IT_0.nii"}]},{"name":"Inferior Parietal – Lateral Occipital","children":[{"name":"lh_IP-LO_1","children":[],"labelIndex":"86","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-LO_1.nii"}]},{"name":"Inferior Parietal – Middle Temporal\t","children":[{"name":"lh_IP-MT_0","children":[],"labelIndex":"33","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-MT_0.nii"}]},{"name":"Inferior Parietal - Supramarginal","children":[{"name":"lh_IP-SM_0","children":[],"labelIndex":"80","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-SM_0.nii"}]},{"name":"Inferior Pariertal – Superior Parietal","children":[{"name":"lh_IP-SP_0","children":[],"labelIndex":"82","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-SP_0.nii"},{"name":"lh_IP-SP_1","children":[],"labelIndex":"84","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IP-SP_1.nii"}]},{"name":"Inferior Temporal – Middle Temporal","children":[{"name":"lh_IT-MT_0","children":[],"labelIndex":"89","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_IT-MT_0.nii"}]},{"name":"Lateral Orbitofrontal – Pars Orbitalis","children":[{"name":"lh_LOF-Or_0","children":[],"labelIndex":"48","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_LOF-Or_0.nii"}]},{"name":"Lateral Orbitofrontal – Rostral Middle Frontal","children":[{"name":"lh_LOF-RMF_0","children":[],"labelIndex":"29","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_LOF-RMF_0.nii"},{"name":"lh_LOF-RMF_1","children":[],"labelIndex":"31","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_LOF-RMF_1.nii"}]},{"name":"Lateral Orbitofrontal – Superior Temporal","children":[{"name":"lh_LOF-ST_0","children":[],"labelIndex":"25","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_LOF-ST_0.nii"}]},{"name":"Middle Orbitofrontal – Superior Temporal","children":[{"name":"lh_MOF-ST_0","children":[],"labelIndex":"12","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_MOF-ST_0.nii"}]},{"name":"Middle Temporal - Supramarginal","children":[{"name":"lh_MT-SM_0","children":[],"labelIndex":"72","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_MT-SM_0.nii"}]},{"name":"Middle Temporal – Superior Temporal","children":[{"name":"lh_MT-ST_0","children":[],"labelIndex":"95","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_MT-ST_0.nii"}]},{"name":"Pars Opercularis - Insula","children":[{"name":"lh_Op-Ins_0","children":[],"labelIndex":"27","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_Op-Ins_0.nii"}]},{"name":"Pars Opercularis - Precentral`","children":[{"name":"lh_Op-PrC_0","children":[],"labelIndex":"18","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_Op-PrC_0.nii"}]},{"name":"Pars Opercularis – Superior Frontal","children":[{"name":"lh_Op-SF_0","children":[],"labelIndex":"42","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_Op-SF_0.nii"}]},{"name":"Pars Orbitalis - Insula","children":[{"name":"lh_Or-Ins_0","children":[],"labelIndex":"16","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_Or-Ins_0.nii"}]},{"name":"Postcentral - Insula","children":[{"name":"lh_PoC-Ins_0","children":[],"labelIndex":"53","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-Ins_0.nii"}]},{"name":"Poscentral - Precuneus","children":[{"name":"lh_PoCi-PrCu_0","children":[],"labelIndex":"44","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoCi-PrCu_0.nii"},{"name":"lh_PoCi-PrCu_1","children":[],"labelIndex":"45","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoCi-PrCu_1.nii"}]},{"name":"Posterior Cingulate – Rostral Anterior Cingulate","children":[{"name":"lh_PoCi-RAC_0","children":[],"labelIndex":"10","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoCi-RAC_0.nii"}]},{"name":"Posterior Cingulate – Superior Frontal","children":[{"name":"lh_PoCi-SF_0","children":[],"labelIndex":"79","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoCi-SF_0.nii"}]},{"name":"Poscentral - Precentral","children":[{"name":"lh_PoC-PrC_0","children":[],"labelIndex":"1","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-PrC_0.nii"},{"name":"lh_PoC-PrC_1","children":[],"labelIndex":"3","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-PrC_1.nii"},{"name":"lh_PoC-PrC_2","children":[],"labelIndex":"5","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-PrC_2.nii"},{"name":"lh_PoC-PrC_3","children":[],"labelIndex":"7","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-PrC_3.nii"}]},{"name":"Poscentral - Supramarginal","children":[{"name":"lh_PoC-SM_0","children":[],"labelIndex":"74","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-SM_0.nii"},{"name":"lh_PoC-SM_1","children":[],"labelIndex":"76","PMapURL":"https://neuroglancer.humanbrainproject.org/precomputed/Fiber_Bundle/ProbabilityMaps/lh_PoC-SM_1.nii"}]},{"name":"Precentral - 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diff --git a/src/res/ext/allen3DReconAggregated.json b/src/res/ext/allen3DReconAggregated.json
index d0e5e966552685c8f4ec1ad88f27443db8b07819..9e35efd7ce68e42a57107a3108d5432163e552ad 100644
--- a/src/res/ext/allen3DReconAggregated.json
+++ b/src/res/ext/allen3DReconAggregated.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/src/res/ext/allen3DVolumeAggregated.json b/src/res/ext/allen3DVolumeAggregated.json
index cc4bb70bf31b1e95598ec8ed51a5914429c70dfa..ade5283cc44cfbe2682555ef84ca3dd6b4bd9768 100644
--- a/src/res/ext/allen3DVolumeAggregated.json
+++ b/src/res/ext/allen3DVolumeAggregated.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/src/res/ext/allenAggregated.json b/src/res/ext/allenAggregated.json
index c4c1c2f0f0708526aec8ebe09418ad32ca55efc9..8e04c3304331c484a701351bdf5ecb6a2b382845 100644
--- a/src/res/ext/allenAggregated.json
+++ b/src/res/ext/allenAggregated.json
@@ -1 +1 @@
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diff --git a/src/res/ext/allenMouse.json b/src/res/ext/allenMouse.json
index f49dbb4a6ecdeaeb31be89f202789ea0e1a39dd4..a3e9d578dc5310c7ee7cd8ed316ee1b611028132 100644
--- a/src/res/ext/allenMouse.json
+++ b/src/res/ext/allenMouse.json
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+            "st_level": null,
+            "hemisphere_id": 3,
+            "parent_structure_id": 997
+          }
+        },
+        {
+          "name": "retina",
+          "labelIndex": 304325711,
+          "rgb": [
+            127,
+            46,
+            126
+          ],
+          "children": [],
+          "ontologyMetadata": {
+            "id": 304325711,
+            "atlas_id": null,
+            "ontology_id": 1,
+            "acronym": "retina",
+            "name": "retina",
+            "color_hex_triplet": "7F2E7E",
+            "graph_order": 1309,
+            "st_level": null,
+            "hemisphere_id": 3,
+            "parent_structure_id": 997
+          }
+        }
+      ],
+      "properties": {
+        "publications": [
+          {
+            "doi": "https://doi:10.1038/nature05453",
+            "citation": "Lein, E.S. et al. (2007) Genome-wide atlas of gene expression in the adult mouse brain, Nature 445: 168-176. "
+          }
+        ]
+      }
+    }
+  ],
+  "properties": {
+    "name": "Allen adult mouse brain reference atlas V3 Brain Atlas",
+    "description": "The Allen adult mouse brain reference atlas V3 Brain Atlas includes a full-color, high-resolution anatomical reference atlas accompanied by a systematic, hierarchically organized taxonomy of mouse brain structures. Anatomical annotations in classical histological atlas plates were extracted to create a comprehensive volumetric reference atlas of the mouse brain."
+  }
+}
\ No newline at end of file
diff --git a/src/res/ext/allenTestAggregated.json b/src/res/ext/allenTestAggregated.json
index a2a6561648f4dce323a248d8347a34a0f6afb861..c963b8ccc3f7d0e82040adbe3db112333141a013 100644
--- a/src/res/ext/allenTestAggregated.json
+++ b/src/res/ext/allenTestAggregated.json
@@ -1 +1 @@
-[{"type":"Allen Dataset","name":"hbp-00005","regionName":[{"regionName":"Hippocampal region","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00005","name":"hbp-00005","mimetype":"raw"}],"kgID":"Project/e4da3b7fbbce2345d7772b0674a318d5"},{"type":"Allen Dataset","name":"hbp-00011_Tg2576","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00011_Tg2576","name":"hbp-00011_Tg2576","mimetype":"raw"}],"kgID":"Project/6512bd43d9caa6e02c990b0a82652dca"},{"type":"Allen Dataset","name":"hbp-00012_APD12","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00012_APD12","name":"hbp-00012_APD12","mimetype":"raw"}],"kgID":"Project/c20ad4d76fe97759aa27a0c99bff6710"},{"type":"Allen Dataset","name":"hbp-00013_APD13","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00013_APD13","name":"hbp-00013_APD13","mimetype":"raw"}],"kgID":"Project/c51ce410c124a10e0db5e4b97fc2af39"},{"type":"Allen Dataset","name":"hbp-00014_APD14","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00014_APD14","name":"hbp-00014_APD14","mimetype":"raw"}],"kgID":"Project/aab3238922bcc25a6f606eb525ffdc56"},{"type":"Allen Dataset","name":"hbp-00015_sIPSCs","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00015_sIPSCs","name":"hbp-00015_sIPSCs","mimetype":"raw"}],"kgID":"Project/9bf31c7ff062936a96d3c8bd1f8f2ff3"},{"type":"Allen Dataset","name":"hbp-00023_GAD67","regionName":[],"targetParcellation":"Allen Mouse Brain 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Dataset","name":"hbp-00648_AFmr1_150519","regionName":[],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00648_AFmr1_150519","name":"hbp-00648_AFmr1_150519","mimetype":"raw"}]},{"type":"Allen Dataset","name":"hbp-00810_VC-ramp","regionName":[],"targetParcellation":"Allen Mouse Brain Atlas","files":[{"filename":"hbp-00810_VC-ramp","name":"hbp-00810_VC-ramp","mimetype":"raw"}]}]
\ No newline at end of file
+[{"type":"Allen Dataset","name":"hbp-00005","regionName":[{"regionName":"Hippocampal region","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00005","name":"hbp-00005","mimetype":"raw"}],"kgID":"Project/e4da3b7fbbce2345d7772b0674a318d5"},{"type":"Allen Dataset","name":"hbp-00011_Tg2576","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00011_Tg2576","name":"hbp-00011_Tg2576","mimetype":"raw"}],"kgID":"Project/6512bd43d9caa6e02c990b0a82652dca"},{"type":"Allen Dataset","name":"hbp-00012_APD12","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00012_APD12","name":"hbp-00012_APD12","mimetype":"raw"}],"kgID":"Project/c20ad4d76fe97759aa27a0c99bff6710"},{"type":"Allen Dataset","name":"hbp-00013_APD13","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00013_APD13","name":"hbp-00013_APD13","mimetype":"raw"}],"kgID":"Project/c51ce410c124a10e0db5e4b97fc2af39"},{"type":"Allen Dataset","name":"hbp-00014_APD14","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00014_APD14","name":"hbp-00014_APD14","mimetype":"raw"}],"kgID":"Project/aab3238922bcc25a6f606eb525ffdc56"},{"type":"Allen Dataset","name":"hbp-00015_sIPSCs","regionName":[{"regionName":"Field CA1","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00015_sIPSCs","name":"hbp-00015_sIPSCs","mimetype":"raw"}],"kgID":"Project/9bf31c7ff062936a96d3c8bd1f8f2ff3"},{"type":"Allen Dataset","name":"hbp-00023_GAD67","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00023_GAD67","name":"hbp-00023_GAD67","mimetype":"raw"}]},{"type":"Allen Dataset","name":"hbp-00028","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00028","name":"hbp-00028","mimetype":"raw"}]},{"type":"Allen Dataset","name":"hbp-00552_GCaMP6.json","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00552_GCaMP6.json","name":"hbp-00552_GCaMP6.json","mimetype":"raw"}],"kgID":null},{"type":"Allen Dataset","name":"hbp-00648_AFmr1_140213","regionName":[{"regionName":"Prelimbic area","relationship":"equals"},{"regionName":"Primary motor area","relationship":"equals"},{"regionName":"Primary somatosensory area","relationship":"equals"},{"regionName":"Anteromedial visual area","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00648_AFmr1_140213","name":"hbp-00648_AFmr1_140213","mimetype":"raw"}],"kgID":"Subject/9703f44d9e8e3e89b04fe32df38d3a50"},{"type":"Allen Dataset","name":"hbp-00648_AFmr1_140220","regionName":[{"regionName":"Prelimbic area","relationship":"equals"},{"regionName":"Primary motor area","relationship":"equals"},{"regionName":"Primary somatosensory area","relationship":"equals"},{"regionName":"Anteromedial visual area","relationship":"equals"}],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00648_AFmr1_140220","name":"hbp-00648_AFmr1_140220","mimetype":"raw"}],"kgID":"Subject/62a12ea44022cc61bc50622c25d9ba90"},{"type":"Allen Dataset","name":"hbp-00648_AFmr1_140304","regionName":[{"regionName":"Prelimbic area","relationship":"equals"},{"regionName":"Primary motor 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Dataset","name":"hbp-00648_AFmr1_150505","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00648_AFmr1_150505","name":"hbp-00648_AFmr1_150505","mimetype":"raw"}]},{"type":"Allen Dataset","name":"hbp-00648_AFmr1_150519","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00648_AFmr1_150519","name":"hbp-00648_AFmr1_150519","mimetype":"raw"}]},{"type":"Allen Dataset","name":"hbp-00810_VC-ramp","regionName":[],"targetParcellation":"Allen adult mouse brain reference atlas V3 Brain Atlas","files":[{"filename":"hbp-00810_VC-ramp","name":"hbp-00810_VC-ramp","mimetype":"raw"}]}]
\ No newline at end of file
diff --git a/src/res/ext/allenTestPlane.json b/src/res/ext/allenTestPlane.json
index c9087d74295d94fe39d4182badda5ab7841baf37..9598e75886e6d488915dbf3601ae237014f75858 100644
--- a/src/res/ext/allenTestPlane.json
+++ b/src/res/ext/allenTestPlane.json
@@ -1 +1 @@
-[{"type":"Test Allen plane spatial anchor","name":"Plane 01","templateSpace":"Allen Mouse","geometry":{"type":"plane","corners":[[4.2925,1.425,2.8925],[-0.0875,1.425,3.312500000000001],[-0.0875,-2.975,3.312500000000001],[4.2925,-2.975,2.8925]]},"properties":{"description":"This spatial plane represent an observation plane of the mouse.","publications":[]},"files":[{"filename":"DATA1/DATA1_FILE1","name":"DATA1_FILE1","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA1 FILE1","publications":[]}},{"filename":"DATA1/DATA1_FILE2","name":"DATA1 FILE2","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA1 FILE2","publications":[]}},{"filename":"DATA2/DATA2_FILE1","name":"DATA2_FILE1","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA2 FILE1","publications":[]}},{"filename":"DATA2/DATA2_FILE2","name":"DATA2_FILE2","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA2 FILE2","publications":[]}}],"originalJson":{"reference_atlas":"CCF v3","name":"hbp-00552_GCaMP6.json","HBP_Storage_path":"bp00sp01/Pavone_SGA1_1.3.2/hbp-00552/GCaMP6/","Primary region(s)":"Anterior cingulate area, dorsal part; Anterolateral visual area; Posteromedial visual area; Primary motor area; Primary somatosensory area, barrel field; Primary somatosensory area, lower limb; Primary somatosensory area, nose; Primary somatosensory area, trunk; Primary somatosensory area, unassigned; Primary somatosensory area, upper limb; Primary visual area; Retrosplenial area, dorsal part; Retrosplenial area, lateral agranular part; Retrosplenial area, ventral part; Rostrolateral area; Secondary motor area","Landmarks":[{"name":"upper left corner","x":230,"y":294,"z":322.5},{"name":"upper right corner","x":54.8,"y":277.2,"z":322.5},{"name":"lower right corner","x":54.8,"y":277.2,"z":146.5},{"name":"lower left corner","x":230,"y":294,"z":146.5}]},"processedData":{"transformedCorners":[[230,294,322.5],[54.8,277.2,322.5],[54.8,277.2,146.5],[230,294,146.5]]}}]
\ No newline at end of file
+[{"type":"Test Allen plane spatial anchor","name":"Plane 01","templateSpace":"Allen adult mouse brain reference atlas V3","geometry":{"type":"plane","corners":[[4.2925,1.425,2.8925],[-0.0875,1.425,3.312500000000001],[-0.0875,-2.975,3.312500000000001],[4.2925,-2.975,2.8925]]},"properties":{"description":"This spatial plane represent an observation plane of the mouse.","publications":[]},"files":[{"filename":"DATA1/DATA1_FILE1","name":"DATA1_FILE1","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA1 FILE1","publications":[]}},{"filename":"DATA1/DATA1_FILE2","name":"DATA1 FILE2","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA1 FILE2","publications":[]}},{"filename":"DATA2/DATA2_FILE1","name":"DATA2_FILE1","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA2 FILE1","publications":[]}},{"filename":"DATA2/DATA2_FILE2","name":"DATA2_FILE2","mimetype":"application/raw","url":"http://about:blank","properties":{"description":"This is the description for the DATA2 FILE2","publications":[]}}],"originalJson":{"reference_atlas":"CCF v3","name":"hbp-00552_GCaMP6.json","HBP_Storage_path":"bp00sp01/Pavone_SGA1_1.3.2/hbp-00552/GCaMP6/","Primary region(s)":"Anterior cingulate area, dorsal part; Anterolateral visual area; Posteromedial visual area; Primary motor area; Primary somatosensory area, barrel field; Primary somatosensory area, lower limb; Primary somatosensory area, nose; Primary somatosensory area, trunk; Primary somatosensory area, unassigned; Primary somatosensory area, upper limb; Primary visual area; Retrosplenial area, dorsal part; Retrosplenial area, lateral agranular part; Retrosplenial area, ventral part; Rostrolateral area; Secondary motor area","Landmarks":[{"name":"upper left corner","x":230,"y":294,"z":322.5},{"name":"upper right corner","x":54.8,"y":277.2,"z":322.5},{"name":"lower right corner","x":54.8,"y":277.2,"z":146.5},{"name":"lower left corner","x":230,"y":294,"z":146.5}]},"processedData":{"transformedCorners":[[230,294,322.5],[54.8,277.2,322.5],[54.8,277.2,146.5],[230,294,146.5]]}}]
\ No newline at end of file
diff --git a/src/res/ext/bigbrain.json b/src/res/ext/bigbrain.json
index 98a8fa262adcfac87b78f8363ce05a793bc236d4..ee281176d8238f65782385574149b92ee38d6405 100644
--- a/src/res/ext/bigbrain.json
+++ b/src/res/ext/bigbrain.json
@@ -1 +1 @@
-{"name":"Big Brain (Histology)","type":"template","species":"Human","useTheme":"light","nehubaId":" grey value: ","nehubaConfigURL":"res/json/bigbrainNehubaConfig.json","parcellations":[{"name":"Grey/White matter","type":"parcellation","ngData":null,"ngId":" tissue type: ","regions":[{"name":"Grey matter","labelIndex":100,"rgb":[200,200,200],"children":[]},{"name":"White matter","labelIndex":200,"rgb":[255,255,255],"children":[]}]}],"properties":{"name":"Big Brain (Histology)","description":"An ultrahigh resolution 3D model of a complete human brain (20 micron isotropic resolution), developed in a collaborative effort between the teams of Dr. Katrin Amunts and Dr. Karl Zilles (Forschungszentrum Jülich) and Dr. Alan Evans (Montreal Neurological Institute). Based on 7404 digitized histological brain sections, this so far unique reconstruction provides unprecedented neuroanatomical insight. The dataset contains a complete gray and white matter classification with corresponding surface reconstructions","publications":[{"doi":"https://doi.org/10.1126/science.1235381","citation":"K. Amunts, A. Evans et al.: BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science 2013"},{"doi":"http://bigbrain.loris.ca","citation":"http://bigbrain.loris.ca"}]}}
\ No newline at end of file
+{"name":"Big Brain (Histology)","type":"template","species":"Human","useTheme":"light","nehubaId":" grey value: ","nehubaConfigURL":"nehubaConfig/bigbrainNehubaConfig","parcellations":[{"name":"Grey/White matter","type":"parcellation","ngData":null,"ngId":" tissue type: ","regions":[{"name":"Grey matter","labelIndex":100,"rgb":[200,200,200],"children":[]},{"name":"White matter","labelIndex":200,"rgb":[255,255,255],"children":[]}]},{"name":"Cytoarchitectonic Maps","properties":{"description":"This dataset contains cytoarchitectonic maps of brain regions in the BigBrain space [Amunts et al. 2013]. The mappings were created using the semi-automatic method presented in Schleicher et al. 1999, based on coronal histological sections on 1 micron resolution. Mappings are available on approximately every 100th section for each region. They were then transformed to the sections of the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). Only a few cytoarchitectonic maps in the Big Brain are currently **fully mapped**, based on a workflow that automatically fills in missing sections based on expert annotations. Other 3D maps are available in a preliminary version, in which the expert annotations in the Big Brain space were simply **interpolated**."},"regions":[{"name":"telencephalon","children":[{"name":"cerebral cortex","children":[{"name":"temporal lobe","children":[{"name":"Heschl's gyrus","children":[{"name":"Area TE 1.0 (HESCHL)","ngId":"interpolated","status":"interpolated","labelIndex":4,"rgb":[0,146,63],"children":[],"position":[4363384,836825,4887117]},{"name":"Area TE 1.1 (HESCHL)","ngId":"interpolated","status":"interpolated","labelIndex":5,"rgb":[132,194,37],"children":[],"position":[-11860944,-3841071,6062770]},{"name":"Area TE 1.2 (HESCHL)","ngId":"interpolated","status":"interpolated","labelIndex":6,"rgb":[117,197,240],"children":[],"position":[19474750,7932494,3511322]}]},{"name":"superior temporal gyrus","children":[{"name":"Area TE 3 (STG)","ngId":"interpolated","status":"interpolated","labelIndex":7,"rgb":[231,120,23],"children":[],"position":[3479937,2702958,3819372]}]},{"name":"superior temporal sulcus","children":[{"name":"Area STS1 (STS)","ngId":"interpolated","status":"interpolated","labelIndex":24,"children":[],"position":[-3185950,3919067,-8346900]},{"name":"Area STS2 (STS)","ngId":"interpolated","status":"interpolated","labelIndex":25,"children":[],"position":[8584703,6170348,-11790982]}]}]},{"name":"frontal lobe","children":[{"name":"precentral gyrus","children":[{"name":"Area 6d1 (PreCG)","ngId":"interpolated","status":"interpolated","labelIndex":1,"children":[],"position":[-10496194,13643679,42286812]},{"name":"Area 6d2 (PreCG)","ngId":"interpolated","status":"interpolated","labelIndex":2,"children":[],"position":[-9255504,27432072,43445689]},{"name":"Area 6ma (preSMA, mesial SFG)","ngId":"interpolated","status":"interpolated","labelIndex":22,"children":[],"position":[-9349145,27783957,38734627]},{"name":"Area 6mp (SMA, mesial SFG)","ngId":"interpolated","status":"interpolated","labelIndex":23,"children":[],"position":[-11566856,15797100,42172031]}]},{"name":"superior frontal sulcus","children":[{"name":"Area 6d3 (SFS)","ngId":"interpolated","status":"interpolated","labelIndex":3,"children":[],"position":[-8973604,28973429,35691250]}]},{"name":"inferior frontal sulcus","children":[{"name":"Area ifj1 (IFS/PreCS)","ngId":"interpolated","status":"interpolated","labelIndex":9,"children":[],"position":[-7394436,33562602,19086364]},{"name":"Area ifj2 (IFS/PreCS)","ngId":"interpolated","status":"interpolated","labelIndex":10,"children":[],"position":[-26787581,30975651,16855869]},{"name":"Area ifs1 (IFS)","ngId":"interpolated","status":"interpolated","labelIndex":11,"children":[],"position":[-4044465,40212624,17596493]},{"name":"Area ifs2 (IFS)","ngId":"interpolated","status":"interpolated","labelIndex":12,"children":[],"position":[6807265,40114241,18114896]},{"name":"Area ifs3 (IFS)","ngId":"interpolated","status":"interpolated","labelIndex":13,"children":[],"position":[-2260366,37593844,19960703]},{"name":"Area ifs4 (IFS)","ngId":"interpolated","status":"interpolated","labelIndex":14,"children":[],"position":[-3440565,37895181,17378851]}]}]},{"name":"limbic lobe","children":[{"name":"hippocampal formation","children":[{"name":"Entorhinal Cortex","ngId":"interpolated","status":"interpolated","labelIndex":8,"rgb":[153,153,255],"children":[],"position":[4800238,8859989,-24872710]}]}]},{"name":"parietal lobe","children":[{"name":"intraparietal sulcus","children":[{"name":"Area hIP4 (IPS)","ngId":"interpolated","status":"interpolated","labelIndex":15,"children":[],"position":[-5671181,-44793673,21692004]},{"name":"Area hIP5 (IPS)","ngId":"interpolated","status":"interpolated","labelIndex":16,"children":[],"position":[-13546343,-38230309,26252296]},{"name":"Area hIP6 (IPS)","ngId":"interpolated","status":"interpolated","labelIndex":17,"children":[],"position":[-3723403,-33064127,32569712]},{"name":"Area hIP7 (IPS)","ngId":"interpolated","status":"interpolated","labelIndex":18,"children":[],"position":[-5344588,-43655931,24702722]}]},{"name":"parieto-occipital sulcus","children":[{"name":"Area hPO1 (POS)","ngId":"interpolated","status":"interpolated","labelIndex":21,"children":[],"position":[-4455614,-44097379,28855803]}]}]},{"name":"occipital lobe","children":[{"name":"occipital cortex","children":[{"name":"Area hOc1 (V1, 17, CalcS)","ngId":"v1","status":"fully mapped","labelIndex":1,"position":[3187941,-50436479,3430986],"rgb":[250,30,250],"children":[]},{"name":"Area hOc2 (V2, 18)","ngId":"v2","status":"fully 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This datasets provides a 3D segmentation of all cortical and laminar surfaces in the BigBrain, a high-resolution, 3D histological model of the human brain. The segmentation has been computed automatically based on histological intensities along 3D cortical profiles which were sampled between the pial and white matter throughout the dataset. These cortical profiles were segmented into layers using a convolutional neural network. Training profiles were generated from examples of manually segmented layers on cortical regions from 2D histological sections of the BigBrain. From the segmented intensity profiles, surface meshes and voxel masks of all six cortical layers in the space of the Big Brain have been computed.","publications":[{"doi":"https://doi.org/10.1101/580597","citation":"Konrad Wagstyl, Stéphanie Larocque, Guillem Cucurull, Claude Lepage, Joseph Paul Cohen, Sebastian Bludau, Nicola Palomero-Gallagher, Thomas Funck, Hannah Spitzer, Timo Dicksheid, Paul C Fletcher, Adriana Romero, Karl Zilles, Katrin Amunts, Yoshua Bengio, Alan C. 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Based on 7404 digitized histological brain sections, this so far unique reconstruction provides unprecedented neuroanatomical insight. The dataset contains a complete gray and white matter classification with corresponding surface reconstructions","publications":[{"doi":"https://doi.org/10.1126/science.1235381","citation":"K. Amunts, A. Evans et al.: BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science 2013"},{"doi":"http://bigbrain.loris.ca","citation":"http://bigbrain.loris.ca"}]}}
\ No newline at end of file
diff --git a/src/res/ext/bigbrainNehubaConfig.json b/src/res/ext/bigbrainNehubaConfig.json
index bb88d61ad3751766fa6c0723eb7aae6bbdcb4c97..185ac5077ab33ef3004edf4e326602aefa446020 100644
--- a/src/res/ext/bigbrainNehubaConfig.json
+++ b/src/res/ext/bigbrainNehubaConfig.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/src/res/ext/cachedKgDS.20190225.json b/src/res/ext/cachedKgDS.20190225.json
new file mode 100644
index 0000000000000000000000000000000000000000..dbb4b5b9ec588de8031adafc9d2fb739e972ab59
--- /dev/null
+++ b/src/res/ext/cachedKgDS.20190225.json
@@ -0,0 +1,23551 @@
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "name": "Parvalbumin interneruron distribution in the mouse brain",
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+        {
+          "name": "MNI Colin 27"
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+      ],
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "parcellationRegion": [
+        {
+          "name": "Area FG3 (FusG)"
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Grill-Spector, Kalanit",
+        "Bludau, Sebastian",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Caspers, Julian",
+        "Weiner, Kevin S.",
+        "Lorenz, Simon"
+      ],
+      "kgReference": [
+        "10.25493/E7VG-4NG"
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+      "publications": [
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+          "cite": "Lorenz, S., Weiner, K. S., Caspers, J., Mohlberg, H., Schleicher, A., Bludau, S., … Amunts, K. (2015). Two New Cytoarchitectonic Areas on the Human Mid-Fusiform Gyrus. Cerebral Cortex, bhv225. ",
+          "doi": "10.1093/cercor/bhv225"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Posterior Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
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+      "contributors": [
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+      "publications": [
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+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Isthmus Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "The dataset contains the complete reconstruction of the cerebellum from an L7-GFP mouse (PND 10), where all Purkinje cells are fluorescently labeled. The dataset includes raw imaging data obtained with confocal light sheet microscopy of cleared sample and cell localization (position of somata expressed as xyz coordinates).",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Purkinje cell distribution in mouse cerebellum",
+      "files": [],
+      "contributors": [
+        "Pavone, Francesco S.",
+        "Silvestri, Ludovico"
+      ],
+      "kgReference": [
+        "10.25493/76F0-87N"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc4lp (LOC) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc4lp (LOC). The probability map of Area hOc4lp (LOC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc4lp.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc4lp (LOC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc4lp (LOC)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc4lp.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc4lp.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Malikovic, Aleksandar",
+        "Zilles, Karl",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola",
+        "Kujovic, Milenko",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/W4H2-WTM"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lp",
+          "cite": "Malikovic, A., Amunts, K., Schleicher, A., Mohlberg, H., Kujovic, M., Palomero-Gallagher, N., … Zilles, K. (2015). Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lp. Brain Structure and Function, 221(4), 1877–1897. ",
+          "doi": "10.1007/s00429-015-1009-8"
+        }
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+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Weber, Bruno"
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+      "description": "The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the rat/mouse brain. To that end synchrotron radiation-based X-ray microscopy images were collected. Then the data is translated into 3D mesh and volumetric models (graph).  \nThis dataset contains the model of part of cortical vasculature of the rat brain as a graph structure (discrete mathematics).  \nThe data is a derived dataset using computational methods from the dataset “3D high resolution SRXTM image data of cortical vasculature of rat brain” ([doi: 10.25493/HCPQ-MY8](https://doi.org/10.25493/HCPQ-MY8)).",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Graphical representation of rat cortical vasculature reconstructed from high resolution 3D SRXTM data.",
+      "files": [],
+      "contributors": [
+        "Weber, Bruno",
+        "Menze, Bjoern H.",
+        "Székely, Gábor",
+        "Hirsch, Sven",
+        "Schneider, Matthias"
+      ],
+      "kgReference": [
+        "10.25493/K243-13K"
+      ],
+      "publications": [
+        {
+          "name": "Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters",
+          "cite": "Schneider, M., Hirsch, S., Weber, B., Székely, G., & Menze, B. H. (2015). Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters. Medical Image Analysis, 19(1), 220–249. ",
+          "doi": "10.1016/j.media.2014.09.007"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic Area 5L (SPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 5L (SPL). The probability map of Area 5L (SPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-5L.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 5L (SPL)"
+        }
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+      "name": "Probabilistic cytoarchitectonic map of Area 5L (SPL)",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Hermann, K.",
+        "Eickhoff, Simon B.",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Hoemke, L.",
+        "Amunts, Katrin",
+        "Scheperjans, Filip"
+      ],
+      "kgReference": [
+        "10.25493/A5V4-HFH"
+      ],
+      "publications": [
+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
+        },
+        {
+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoC-PrC_0"
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+      "name": "Probabilistic maps of rh_PoC-PrC_0",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hIP2 (IPS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hIP2 (IPS). The probability map of Area hIP2 (IPS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hIP2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hIP2 (IPS)"
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+      "name": "Probabilistic cytoarchitectonic map of Area hIP2 (IPS)",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hIP2.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Choi, Hi-Jae",
+        "Amunts, Katrin",
+        "Armstrong, E.",
+        "Fink, Gereon R.",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/FR6P-6HW"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus",
+          "cite": "Choi, H.-J., Zilles, K., Mohlberg, H., Schleicher, A., Fink, G. R., Armstrong, E., & Amunts, K. (2006). Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. The Journal of Comparative Neurology, 495(1), 53–69. ",
+          "doi": "10.1002/cne.20849"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Pars Triangularis and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_Tr-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_Tr-SF_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_Tr-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_Tr-SF_0.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area opercularis 9 (OP9) in the frontal operculum. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area OP9. The probability map of Area OP9 is given in file jubrain-pmap-v22c_space-mnicolin27_Area-OP9.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area OP9"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area OP9",
+      "files": [],
+      "contributors": [
+        "Saal, Martin",
+        "Amunts, Katrin",
+        "Caspers, Svenja",
+        "Mohlberg, Hartmut",
+        "Bludau, Sebastian"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "3D TIFF, MP4"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "This dataset is about 3D-imaging at high resolution of parvalbumin expressing neurons of selected regions (area 17 of visual cortex) of human brain. The dataset contains the final fused volume in tiff format, the originally acquired 3D stacks, as well as stitching file generated by ZetaStitcher.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Visual cortex area 17"
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+      "name": "Maps of neurons stained with anti-Parvalbumin antibody",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000770/Parvalbumin staining/fused.tiff",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000770/Parvalbumin staining/stitch.yml",
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+          "name": "TIFF_3D.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000770/Parvalbumin staining/TIFF_3D.zip",
+          "contentType": "application/zip"
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+      ],
+      "contributors": [
+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/RQKZ-PCP"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Language task that was used as a localizer of brain regions involved in semantic processing. It was adapted from a study dedicated to exploring the particular role of the anterior temporal lobe (ATL) on semantic integration. The paradigm comprised two categories of blocks: (1) story blocks and (2) math blocks. Math blocks served as a control condition in this context, since it is likely to involve both auditory processing and attentional demands. Both type of blocks exhibited auditory stimuli in short epochs, which in turn finished with a final question followed by two possible answers. During story blocks, in which participants were presented with Aesop’s fables, the final question targeted the topic of the story. Conversely, math blocks showed arithmetic problems for which the correct solution must be selected. The response was provided after the two possible options were displayed, through pressing the corresponding button of the response box. The difficulty levels of the problems, presented for both categories, were adjusted throughout the experiment, in order to keep the participants engaged in the task and, thus, ensure accurate performances. The task was composed by eleven blocks per run. For the first run, six story blocks and five math blocks were interleaved, respectively. The reverse amount and order of blocks were used during the second run. The number of trials per block varied between one and four. Nevertheless, it was assured that both block categories matched their length of presentation at every run. There was a cue during two seconds at the beginning of each block, indicating its category. The duration of the trials within a block varied from ten to thirty seconds. Finally, the presentation of the auditory stimuli was always accompanied by the display of a fixation cross on the screen throughout the entire run.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Language task",
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+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
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+        "Ruest, Torsten",
+        "Amadon, Alexis"
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Bludau, Sebastian",
+        "Eickhoff, Simon B.",
+        "Gerboga, Fatma",
+        "Schleicher, Axel",
+        "Palomero-Gallagher, Nicola",
+        "Zilles, Karl",
+        "Henssen, Anton"
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+        "10.25493/N14D-JQT"
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+          "cite": "Henssen, A., Zilles, K., Palomero-Gallagher, N., Schleicher, A., Mohlberg, H., Gerboga, F., … Amunts, K. (2016). Cytoarchitecture and probability maps of the human medial orbitofrontal cortex. Cortex, 75, 87–112. ",
+          "doi": "10.1016/j.cortex.2015.11.006"
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+    {
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+      "custodians": [
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+      "description": "Transgenic mice carrying the Arctic (E693G) and Swedish (KM670/6701NL) amyloid-b precursor protein (AβPP) develop amyloid-beta (Aβ) deposits in the brain that resemble Alzheimer’s disease neuropathology. The tg-ArcSwe atlas provides access to Aβx-40 immunolabeled histological section images from representative 12 month old tg-ArcSwe mice (Lord et al., Neurobiol. Aging 27:67-77, 2006), showing the morphology and spatial distribution of Aβx-40 plaque deposits across the brain. Analyses show that plaques are primarily distributed in the cerebral cortex, hippocampus, and thalamus. The average Aβ burden in the cortex and hippocampus is similar across animals, with coefficients of variance of 22% and 25%, respectively (Lillehaug et al., Neurobiol Aging 35:556-564, 2014).",
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+        "Bjaalie, Jan G.",
+        "Leergaard, Trygve B.",
+        "Syverstad, Gry H.",
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+      "description": "Local field potentials were recorded in the cerebral cortex of a mouse model of Fragile X syndrome during slow wave activity under ketamine anesthesia. Different parameters of the recorded signal were compared to those of wild type littermates to assess functional biomarkers characterizing Fragile X syndrome. The approach was similar to that in previous publications of our group (Castano-Prat et al., 2017; Ruiz-Mejias et al., 2016).\n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
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+        "Sanchez-Vives, Maria"
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+        "NIFTI"
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Middle Temporal and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+      "custodians": [
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+        "Palomero-Gallagher, Nicola"
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+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 4p using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
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+      "contributors": [
+        "Zilles, Karl",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
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+        "10.25493/J5JR-YH0"
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+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
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+    {
+      "formats": [],
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+      "custodians": [
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+        "Creative Commons Attribution 4.0 International"
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+      "name": "Pituitary homeobox 3 tetracycline-transactivator expression: horizontal sections (case 5154)",
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+        "Lillehaug, Sveinung",
+        "Yetman, Michael J.",
+        "Jankowsky, Joanna L.",
+        "Bjaalie, Jan G.",
+        "Checinska, Martyna M.",
+        "Leergaard, Trygve B."
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+      "description": "Study and characterization of the intrinsic properties of different cerebellar neuronal types.",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Free"
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "name": "Recordings of passive cellular parameters of cerebellar neurons",
+      "files": [],
+      "contributors": [
+        "Prestori, Francesca",
+        "Tognolina, Marialuisa",
+        "D'Angelo, Egidio",
+        "Soda, Teresa",
+        "Locatelli, Francesca"
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+        "10.25493/MVHQ-4YA"
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+    },
+    {
+      "formats": [],
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+      "custodians": [
+        "Merchan-Perez, Angel"
+      ],
+      "description": "\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [],
+      "embargoStatus": [
+        "Embargoed"
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+      "license": [],
+      "parcellationRegion": [],
+      "name": "Data on synapses in the somatosensory cortex of the adult mouse, obtained with three-dimensional electron microscopy (FIB-SEM).",
+      "files": [],
+      "contributors": [
+        "Turegano, Marta",
+        "Rodriguez, Rodrigo",
+        "Merchan-Perez, Angel",
+        "DeFelipe, Javier"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Caudal Anterior Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_CAC-PrCu_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_CAC-PrCu_0",
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 3b using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp, pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 3b"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area 3b",
+      "files": [
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+          "name": "3b_pr_examples.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_3b/3b_pr_examples.zip",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/TZBY-96W"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc3v (LingG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc3v (LingG). The probability map of Area hOc3v (LingG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc3v.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc3v (LingG)"
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+      "name": "Probabilistic cytoarchitectonic map of Area hOc3v (LingG)",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Rottschy, Claudia",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Kujovic, Milenko",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/3K39-DNC"
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+      "publications": [
+        {
+          "name": "Ventral visual cortex in humans: Cytoarchitectonic mapping of two extrastriate areas",
+          "cite": "Rottschy, C., Eickhoff, S. B., Schleicher, A., Mohlberg, H., Kujovic, M., Zilles, K., & Amunts, K. (2007). Ventral visual cortex in humans: Cytoarchitectonic mapping of two extrastriate areas. Human Brain Mapping, 28(10), 1045–1059. ",
+          "doi": "10.1002/hbm.20348"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Middle Frontal and Rostral Middle Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CMF-RMF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CMF-RMF_0",
+      "files": [
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
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+      ]
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+    {
+      "formats": [
+        "ZIP, 3D-TIFF"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "This dataset contains images of neurofilament in human cortex obtained using serial two-photon microscopy. The individual 3D tiles acquired with the two-photon fluorescence microscope were fused together to produce the final image.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Visual cortex area 17"
+        }
+      ],
+      "name": "Map of neurofilament in human brain cortex",
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+          "name": "stitch.yml",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000326/mosaic1/stitch.yml",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000326/mosaic1/TIFF_3D.zip",
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+          "contentType": "image/tiff"
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+      ],
+      "contributors": [
+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/76F3-ZMZ"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Precentral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PrC-SM_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PrC-SM_0",
+      "files": [
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "name": "lh_PrC-SM_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PrC-SM_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial coronal brain sections showing Pituitary homeobox 3 (Pitx3) promoter expression in a bigenic Pitx3-tetracycline-transactivator (tTA) mouse brain (case 3435, adult male), using a driver-reporter construct in which the Pitx3-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data show that the Pitx3-tTA promoter is spatially restricted to the substantia nigra, ventral tegmental area, and a few other regions.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Pituitary homeobox 3 tetracycline-transactivator expression: coronal sections (case 3435)",
+      "files": [],
+      "contributors": [
+        "Jankowsky, Joanna L.",
+        "Leergaard, Trygve B.",
+        "Puchades, Maja A.",
+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Yetman, Michael J.",
+        "Checinska, Martyna M."
+      ],
+      "kgReference": [
+        "10.25493/FMGJ-4N3"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "D'Angelo, Egidio"
+      ],
+      "description": "Study and characterization of the intrinsic excitability properties of different cerebellar neuronal types.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Recordings of cerebellar neuronal firing induced by currents steps",
+      "files": [],
+      "contributors": [
+        "Dieudonne, S.",
+        "Forti, L.",
+        "D'Angelo, Egidio",
+        "Isope, P.",
+        "Bidoret, C.",
+        "Pietrajtis, K.",
+        "Cesana, E."
+      ],
+      "kgReference": [
+        "10.25493/4AF6-WSD"
+      ],
+      "publications": [
+        {
+          "name": "Granule Cell Ascending Axon Excitatory Synapses onto Golgi Cells Implement a Potent Feedback Circuit in the Cerebellar Granular Layer",
+          "cite": "Cesana, E., Pietrajtis, K., Bidoret, C., Isope, P., D’Angelo, E., Dieudonne, S., & Forti, L. (2013). Granule Cell Ascending Axon Excitatory Synapses onto Golgi Cells Implement a Potent Feedback Circuit in the Cerebellar Granular Layer. Journal of Neuroscience, 33(30), 12430–12446. ",
+          "doi": "10.1523/JNEUROSCI.4897-11.2013"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Postcentral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoC-SP_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PoC-SP_1",
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+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "name": "rh_PoC-SP_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PoC-SP_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Rostral Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_RMF-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_RMF-SF_0",
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+        {
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+          "name": "lh_RMF-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_RMF-SF_0.nii",
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area FG2 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, ar\n\nkainate (glutamate; [³H]kainate): fp, ar\n\nNMDA (glutamate; [³H]MK-801): fp, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area FG2"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area FG2",
+      "files": [
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+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_FG2/FG2_ar_examples.zip",
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+          "name": "FG2_fp_20180321.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_FG2/FG2_fp_20180321.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Caspers, Julian",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/VFCW-HXZ"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus",
+          "cite": "Caspers, J., Palomero-Gallagher, N., Caspers, S., Schleicher, A., Amunts, K., & Zilles, K. (2013). Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus. Brain Structure and Function, 220(1), 205–219. ",
+          "doi": "10.1007/s00429-013-0646-z"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [],
+      "description": "Epidural EEG signal (16 recording electrodes) in response to electrical stimulation of left secondary motor cortex from head restrained rats, during wakefulness and during the exposure of low and high dosages of anesthetics (sevoflurane, propofol and ketamine). The electrical stimulation consists in about 100 single monophasic pulses of 1 ms, 50 uA, delivered at the rate of 0.1 Hz.\n\nThis dataset is temporarily under embargo. The data will become available for download after the embargo period.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Embargoed"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "PCI-like measure in rodents",
+      "files": [],
+      "contributors": [],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area FG1 (FusG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area FG1 (FusG). The probability map of Area FG1 (FusG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-FG1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area FG1 (FusG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area FG1 (FusG)",
+      "files": [
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-FG1.nii",
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Caspers, Julian",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B.",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/534D-V55"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus",
+          "cite": "Caspers, J., Zilles, K., Eickhoff, S. B., Schleicher, A., Mohlberg, H., & Amunts, K. (2012). Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus. Brain Structure and Function, 218(2), 511–526. ",
+          "doi": "10.1007/s00429-012-0411-8"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area TE 1.2 (HESCHL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area TE 1.2 (HESCHL). The probability map of Area TE 1.2 (HESCHL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-TE-12.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area TE 1.2 (HESCHL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area TE 1.2 (HESCHL)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-TE-12.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-TE-12.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Rademacher, Jörg",
+        "Freund, H.-J.",
+        "Amunts, Katrin",
+        "Schormann, Thorsten",
+        "Werner, C.",
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Morosan, Patricia"
+      ],
+      "kgReference": [
+        "10.25493/T1WA-MBT"
+      ],
+      "publications": [
+        {
+          "name": "Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System",
+          "cite": "Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System. NeuroImage, 13(4), 684–701. ",
+          "doi": "10.1006/nimg.2000.0715"
+        },
+        {
+          "name": "Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex",
+          "cite": "Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.-J., & Zilles, K. (2001). Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex. NeuroImage, 13(4), 669–683. ",
+          "doi": "10.1006/nimg.2000.0714"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the neonate, C57BL/6 (wild-type), positive pairing condition. \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Embargoed"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from neonate C57BL/6 (wild-type) mice, positive pairing",
+      "files": [],
+      "contributors": [
+        "Marchetti, Cristina",
+        "Cherubini, Enrico",
+        "Sgritta, Martina"
+      ],
+      "kgReference": [
+        "10.25493/NKRY-KA7"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the adult, C57BL/6 (wild-type), positive pairing condition. \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from adult C57BL/6 (wild-type) mice, positive pairing",
+      "files": [],
+      "contributors": [
+        "Marchetti, Cristina",
+        "Sgritta, Martina",
+        "Cherubini, Enrico"
+      ],
+      "kgReference": [
+        "10.25493/K9VK-PPM"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial horizontal brain sections showing Purkinje cell protein 2 (Pcp2) promoter expression in a bigenic Pcp2-tetracycline-transactivator (tTA) mouse brain (case 3292, adult female), generated using a driver-reporter construct in which the PCP2-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data confirm earlier reports that Pcp2-tTA promoter expression is restricted to cerebellar Purkinje cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Purkinje cell protein 2 tetracycline-transactivator expression: horizontal sections (case 3292)",
+      "files": [],
+      "contributors": [
+        "Jankowsky, Joanna L.",
+        "Checinska, Martyna M.",
+        "Yetman, Michael J.",
+        "Puchades, Maja A.",
+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Leergaard, Trygve B."
+      ],
+      "kgReference": [
+        "10.25493/D1PR-55A"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area PFop (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PFop (IPL). The probability map of Area PFop (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PFop.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PFop (IPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area PFop (IPL)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-PFop.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-PFop.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Schleicher, Axel",
+        "Geyer, Stefan",
+        "Caspers, S.",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B.",
+        "Scheperjans, Filip"
+      ],
+      "kgReference": [
+        "10.25493/XYBW-69Q"
+      ],
+      "publications": [
+        {
+          "name": "The human inferior parietal lobule in stereotaxic space",
+          "cite": "Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481–495. ",
+          "doi": "10.1007/s00429-008-0195-z"
+        },
+        {
+          "name": "The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability",
+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
+          "doi": "10.1016/j.neuroimage.2006.06.054"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "electrophysiological data",
+        ".png",
+        ".mat"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Storm, Johan"
+      ],
+      "description": "Epidural EEG and hippocampal LFP signal in response to electrical stimulations of left secondary motor cortex from head restrained rats, during wakefulness and during the exposure of low dosages of anesthetics (sevoflurane). The electrical stimulations consist in monophasic pulses of 1 ms and multiple intesities (25, 50, 75, 100 uA; about 60 pulses for each intensity), delivered at the rate of 0.1 Hz.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial 4.0 International"
+      ],
+      "embargoStatus": [
+        "Embargoed"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Mechanistic analysis of ERP in rodents",
+      "files": [],
+      "contributors": [
+        "Alessandro, Arena"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Pars Opercularis and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
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+          "name": "lh_Op-PrC_0"
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+      "custodians": [
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+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area hOc1 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc1"
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+      "name": "Density measurements of different receptors for Area hOc1",
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+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Scheperjans, Filip",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/P8SD-JMH"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Analysis of neurotransmitter receptor distribution patterns in the cerebral cortex",
+          "cite": "Eickhoff, S. B., Schleicher, A., Scheperjans, F., Palomero-Gallagher, N., & Zilles, K. (2007). Analysis of neurotransmitter receptor distribution patterns in the cerebral cortex. NeuroImage, 34(4), 1317–1330. ",
+          "doi": "10.1016/j.neuroimage.2006.11.016"
+        }
+      ]
+    },
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+        "NIFTI"
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Caudal Middle Frontal and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
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+      "parcellationRegion": [
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+          "name": "lh_CMF-PrC_0"
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Tagged Image File Format"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "Waxholm Space rat brain atlas v.2.0"
+        }
+      ],
+      "custodians": [
+        "Witter, Menno P."
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+      "description": "The hippocampal region, comprising the hippocampal formation and the parahippocampal region, has a complex three-dimensional structure which often makes it challenging to identify regional boundaries in experimental material. The three-plane architectonic atlas of the rat hippocampal region includes a collection of histological section images cut in the three standard sectional planes (coronal, horizontal and sagittal), with adjacent sections stained to visualize neurons and the chemical marker substances parvalbumin and calbindin. Using this material, boundary definitions for the different planes have been cross-checked and described in detail by Boccara et al. (Hippocampus 25:838-57, 2015; doi: 10.1002/hipo.22407).",
+      "licenseInfo": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
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+        "Free"
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+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Three-plane architectonic atlas of the rat hippocampal region",
+      "files": [],
+      "contributors": [
+        "Leergaard, Trygve B.",
+        "Bjaalie, Jan G.",
+        "Witter, Menno P.",
+        "Hammer, Ingvild M.",
+        "Boccara, Charlotte N.",
+        "Kjonigsen, Lisa J."
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "A three‐plane architectonic atlas of the rat hippocampal region",
+          "cite": "",
+          "doi": "10.1002/hipo.22407"
+        },
+        {
+          "name": "Digital atlas of anatomical subdivisions and boundaries of the rat hippocampal region",
+          "cite": "",
+          "doi": "10.3389/fninf.2011.00002"
+        },
+        {
+          "name": "10.1002/hipo.22407; 10.3389/fninf.2011.00002",
+          "cite": "",
+          "doi": "10.1002/hipo.22407"
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+    {
+      "formats": [
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+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
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+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Social task that intended to provide evidence for brain activity related to social cognition. The paradigm included two categories of blocks, in which movies were presented during short epochs. The movies consisted in triangle-shape clip art, moving in a predetermined fashion. Putative social interactions could be inferred from movements in the so-called social condition. In contrast, objects appeared to be randomly moving in the random condition. The task was constituted by ten blocks per run, five for each category, whose order was pseudo-randomized for every run, but fixed for all participants. There was only one trial per block. It consisted of a twenty-second period of video-clip presentation plus three seconds maximum of a response period, indicated by a momentary instruction on the screen. Thus, the total duration of a block was approximately twenty three seconds. A fixation-cross period of fifteen seconds was always displayed between blocks.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Social task",
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+        "Thirion, Bertrand",
+        "Hertz-Pannier, Lucie",
+        "Dehaene, Stanislas",
+        "Pallier, Christophe",
+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
+        "Dohmatob, Elvis",
+        "Fabre, Murielle",
+        "Ruest, Torsten",
+        "Amadon, Alexis"
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+      "kgReference": [
+        "10.25493/3JXW-AFS"
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+          "cite": "Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., … Thirion, B. (2018). Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Scientific Data, 5, 180105. ",
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+        "Gerboga, Fatma",
+        "Schleicher, Axel",
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+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B.",
+        "Amunts, Katrin",
+        "Scheperjans, Filip",
+        "Schleicher, Axel",
+        "Hoemke, L.",
+        "Zilles, Karl",
+        "Hermann, K."
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+        "10.25493/AHQS-ZR8"
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+          "doi": "10.1093/cercor/bhm116"
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+      "custodians": [
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+      "contributors": [
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+        "Amunts, Katrin",
+        "Eickhoff, Simon B.",
+        "Mohlberg, Hartmut",
+        "Bludau, Sebastian",
+        "Caspers, Svenja"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Pars Opercularis and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
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+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area PFm using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
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+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
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+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
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+          "name": "Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics",
+          "cite": "Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics. Cerebral Cortex, 23(3), 615–628. ",
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+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Area 44v"
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+      ],
+      "contributors": [
+        "Friederici, Angela D.",
+        "Morosan, Patricia",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike",
+        "Zilles, Karl"
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+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Broca's Region: Novel Organizational Principles and Multiple Receptor Mapping",
+          "cite": "Amunts, K., Lenzen, M., Friederici, A. D., Schleicher, A., Morosan, P., Palomero-Gallagher, N., & Zilles, K. (2010). Broca’s Region: Novel Organizational Principles and Multiple Receptor Mapping. PLoS Biology, 8(9), e1000489. ",
+          "doi": "10.1371/journal.pbio.1000489"
+        }
+      ]
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+        "NIFTI"
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Lateral Orbitofrontal and Rostral Middle Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
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+      "embargoStatus": [
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+      "license": [],
+      "parcellationRegion": [
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+          "name": "lh_LOF-RMF_1"
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
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+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V2"
+        }
+      ],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial coronal brain sections showing neuropsin (Nop) promoter expression in a bigenic Nop-tetracycline-transactivator (tTA) mouse brain, (case 2849 adult female), using a driver-reporter construct in which the Nop-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Our data show that the Nop-tTA promoter mainly is expressed in the entorhinal cortex, but also in several other cortical regions.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Neuropsin tetracycline-transactivator expression: coronal sections (case 2849)",
+      "files": [],
+      "contributors": [
+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Yetman, Michael J.",
+        "Jankowsky, Joanna L.",
+        "Leergaard, Trygve B."
+      ],
+      "kgReference": [
+        "10.25493/WB6K-V72"
+      ],
+      "publications": [
+        {
+          "name": "Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex",
+          "cite": "Yetman, M. J., Lillehaug, S., Bjaalie, J. G., Leergaard, T. B., & Jankowsky, J. L. (2015). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex. Brain Structure and Function, 221(4), 2231–2249. ",
+          "doi": "10.1007/s00429-015-1040-9"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "DeFelipe, Javier"
+      ],
+      "description": "This dataset contains the density of VGAT-ir Complex basket formations (Cbk-formations) in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 5 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain.",
+      "licenseInfo": [],
+      "embargoStatus": [],
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+      "parcellationRegion": [
+        {
+          "name": "Brodmann’s area 3b"
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+          "name": "Brodmann’s area 32"
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+          "name": "Brodmann’s area 24"
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+          "name": "Brodmann’s area 38"
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+          "name": "Brodmann’s area 22"
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+          "name": "Brodmann’s area 21"
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+          "name": "Brodmann’s area 47"
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+          "name": "Brodmann’s area 13"
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+          "name": "Brodmann’s area 12"
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+          "name": "Brodmann’s area 11"
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+        {
+          "name": "Brodmann’s area 45"
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+        {
+          "name": "Brodmann’s area 10"
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+        {
+          "name": "Brodmann’s area 9"
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+        {
+          "name": "Brodmann’s area 4"
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+        {
+          "name": "Brodmann’s area 18"
+        },
+        {
+          "name": "Brodmann’s area 17"
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+      ],
+      "name": "Distribution and density of Complex basket formations in the human cerebral cortex",
+      "files": [],
+      "contributors": [
+        "Blazquez-Llorca, Lidia",
+        "DeFelipe, Javier",
+        "García-Marín, Virginia"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "GABAergic complex basket formations in the human neocortex",
+          "cite": "Blazquez-Llorca, L., García-Marín, V., & DeFelipe, J. (2010). GABAergic complex basket formations in the human neocortex. The Journal of Comparative Neurology, 518(24), 4917–4937. ",
+          "doi": "10.1002/cne.22496"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic Area TE 3 (STG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area TE 3 (STG). The probability map of Area TE 3 (STG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-TE-3.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Area TE 3 (STG)"
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Morosan, Patricia",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/V09X-3EW"
+      ],
+      "publications": [
+        {
+          "name": "Multimodal architectonic mapping of human superior temporal gyrus",
+          "cite": "Morosan, P., Schleicher, A., Amunts, K., & Zilles, K. (2005). Multimodal architectonic mapping of human superior temporal gyrus. Anatomy and Embryology, 210(5-6), 401–406. ",
+          "doi": "10.1007/s00429-005-0029-1"
+        },
+        {
+          "name": "Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics",
+          "cite": "Schleicher, A., Amunts, K., Geyer, S., Morosan, P., & Zilles, K. (1999). Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics. NeuroImage, 9(1), 165–177. ",
+          "doi": "10.1006/nimg.1998.0385"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Riess, Olaf"
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+      "description": "Bright-field microscopy images of serial coronal brain sections showing prion promoter (Prnp) promoter expression in a bigenic Prnp-tetracycline-transactivator (tTA) mouse brain (case 388.12, adult female), using a driver-reporter construct in which the Prnp-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Boy et al., Neuroimage 33:449-62, 2006. Our data show that the Prnp-tTA promoter is spatially expressed in multiple brain regions, including the cerebral cortex, several basal ganglia regions, hippocampus, tectum, and cerebellum.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Prion promoter tetracycline-transactivator expression: coronal sections (case 388.12)",
+      "files": [],
+      "contributors": [
+        "Riess, Olaf",
+        "Holzmann, Carsten",
+        "Odeh, Francis",
+        "Nuber, Silke",
+        "Wree, Andreas",
+        "Leergaard, Trygve B.",
+        "Bjaalie, Jan G.",
+        "Bichelmeier, Ulrike",
+        "Boy, Jana",
+        "Bujard, Hermann",
+        "Prusiner, Stanley B.",
+        "Schmidt, Thorsten"
+      ],
+      "kgReference": [
+        "10.25493/T9P1-WH4"
+      ],
+      "publications": [
+        {
+          "name": "Expression mapping of tetracycline-responsive prion protein promoter: Digital atlasing for generating cell-specific disease models",
+          "cite": "Boy, J., Leergaard, T. B., Schmidt, T., Odeh, F., Bichelmeier, U., Nuber, S., … Bjaalie, J. G. (2006). Expression mapping of tetracycline-responsive prion protein promoter: Digital atlasing for generating cell-specific disease models. NeuroImage, 33(2), 449–462. ",
+          "doi": "10.1016/j.neuroimage.2006.05.055"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Danbolt,  Niels C."
+      ],
+      "description": "Glutamate is the major excitatory transmitter in the central nervous system (Danbolt, Prog. Neurobiol. 65:1-105, 2001). It is inactivated by cellular uptake, mostly catalyzed by the glutamate transporters GLT1 (slc1a2, excitatory amino acid transporter [EAAT2]) subtype expressed at high levels in brain astrocytes and at lower levels in neurons. Three C-terminal variants of EAAT2 exist: GLT1a (Pines et al., Nature 360:464-467, 1992), GLT1b (Utsunomiya-Tate et al., FEBS Lett 416:312-326,1997), and GLT1c (Rauen et al., Neurochem. Int. 45:1095-1106, 2004). This dataset is brain-wide collection of microscopic images showing the brain-wide distribution of GLT1 in the mouse and rat brain, visualized by immunohistochemistry using antibodies against GLT1a and GLT1b. To facilitate identification of anatomical location adjacent section were stained to reveal cyto- and myeloarchitecture.",
+      "licenseInfo": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Brain-wide distribution of glutamate type 1 transporter protein (GLT1)",
+      "files": [],
+      "contributors": [
+        "Holmseth, Silvia",
+        "Danbolt, Nils C.",
+        "Bjaalie, Jan G.",
+        "Leergaard, Trygve B.",
+        "Lehre, K.P.",
+        "Real, Katia",
+        "Scott, Heather A."
+      ],
+      "kgReference": [
+        "10.25493/Y147-2CE"
+      ],
+      "publications": [
+        {
+          "name": "The concentrations and distributions of three C-terminal variants of the GLT1 (EAAT2; slc1a2) glutamate transporter protein in rat brain tissue suggest differential regulation",
+          "cite": "Holmseth, S., Scott, H. A., Real, K., Lehre, K. P., Leergaard, T. B., Bjaalie, J. G., & Danbolt, N. C. (2009). The concentrations and distributions of three C-terminal variants of the GLT1 (EAAT2; slc1a2) glutamate transporter protein in rat brain tissue suggest differential regulation. Neuroscience, 162(4), 1055–1071. ",
+          "doi": "10.1016/j.neuroscience.2009.03.048"
+        }
+      ]
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+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
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+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Emotional task aiming to capture neural activity related to the perception of fear and anger. Affective facial expressions were used as visual stimuli due to their importance in adaptive social behavior. The paradigm included two categories of blocks, namely face and shape blocks. All blocks consisted of a series of events, in which images of faces or shapes were displayed, respectively. There were always three faces/shapes per image; one face/shape was shown at the top and two faces/shapes were shown at the bottom. The participants were then asked to decide which face/shape at the bottom, i.e. left or right face/shape, matched the one displayed at the top, by pressing the corresponding button of the response box. The task was formed by twelve blocks per run, i.e. six face blocks and six shape blocks. The two block categories were alternately presented for each run. All blocks contained six trials and they were always initiated by a cue of three seconds. In turn, the trials included a visual-stimulus period of two seconds and a fixation-cross period of one second; the total duration of the trial was thus three seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Emotion",
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+      "contributors": [
+        "Pinho, Ana Luísa",
+        "Thirion, Bertrand",
+        "Hertz-Pannier, Lucie",
+        "Dehaene, Stanislas",
+        "Pallier, Christophe",
+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
+        "Dohmatob, Elvis",
+        "Fabre, Murielle",
+        "Ruest, Torsten",
+        "Amadon, Alexis"
+      ],
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+        "10.25493/ZXMK-AH0"
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+          "doi": "10.1038/sdata.2018.105"
+        }
+      ]
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+    {
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Superior Temporal and Transverse Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+      "formats": [],
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+      "custodians": [
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+      ],
+      "description": "The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the rat/mouse brain. To that end synchrotron radiation-based X-ray microscopy images were collected. Then the data is translated into 3D mesh and volumetric models (graph).  \nThis dataset contains high resolution tiff image array of part of cortical vasculature of the rat brain.  \nThis dataset is used as an input to computational methods that results in the dataset “Graphical representation of rat cortical vasculature reconstructed from high resolution 3D SRXTM data” ([doi: 10.25493/K243-13K](https://doi.org/10.25493/K243-13K)).\ufeff",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "embargoStatus": [
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+      "name": "3D high resolution SRXTM image data of cortical vasculature of rat brain.",
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+        "Schneider, Matthias"
+      ],
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+      "publications": []
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+      "formats": [
+        "NIFTI"
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+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 33 (ACC) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 33 (ACC). The probability map of Area 33 (ACC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-33.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 33 (ACC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 33 (ACC)",
+      "files": [
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Vogt, Brent A.",
+        "Schleicher, Axel",
+        "Hoffstaedter, Felix",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/S6PK-Z7E"
+      ],
+      "publications": [
+        {
+          "name": "Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity",
+          "cite": "Palomero-Gallagher, N., Eickhoff, S. B., Hoffstaedter, F., Schleicher, A., Mohlberg, H., Vogt, B. A., … Zilles, K. (2015). Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity. NeuroImage, 115, 177–190. ",
+          "doi": "10.1016/j.neuroimage.2015.04.053"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area Id7 (Insular dysgranular area 7) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains, obtained from the body donor program of the University of Düsseldorf. Subsequently, the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain, where each voxel is assigned with the probability to belong to Area Id7 (Insula). The probability map of Area Id7 (Insula) in right and left hemisphere is given in files jubrain-pmap-v22c_space-mnicolin27_Area-Id7.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. ",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Area Id7 (Insula)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area Id7 (Insula)",
+      "files": [],
+      "contributors": [
+        "Amunts, Katrin",
+        "Adalat, Reza",
+        "Mohlberg, Hartmut",
+        "Bludau, Sebastian",
+        "Iannilli, Francesca"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area ifs1 (IFS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifs1 (IFS). The probability map of  Area ifs1 (IFS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifs1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifs1 (IFS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifs1 (IFS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "D'Angelo, Egidio"
+      ],
+      "description": "Study and characterization of the intrinsic properties of granule cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Recordings of cerebellar granule cells current-voltage relations",
+      "files": [],
+      "contributors": [
+        "D'Angelo, Egidio",
+        "Tognolina, Marialuisa"
+      ],
+      "kgReference": [
+        "10.25493/MDAR-XEB"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area PGp using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PGp"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area PGp",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.5072/AW9Y-VEU"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics",
+          "cite": "Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics. Cerebral Cortex, 23(3), 615–628. ",
+          "doi": "10.1093/cercor/bhs048"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Middle Frontal and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CMF-PrC_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CMF-PrC_0",
+      "files": [
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+          "name": "rh_CMF-PrC_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_CMF-PrC_0.nii",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Posterior Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoCi-PrCu_2"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PoCi-PrCu_2",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_PoCi-PrCu_2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PoCi-PrCu_2.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
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+        "Minnerop, Martina",
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+        "Bludau, Sebastian",
+        "Tellmann, Stefanie"
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+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+        "NIFTI"
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+      "custodians": [
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+      "description": "Five functional subregions have been defined in the right dorsal premotor cortex by computing meta-analytic connectivity modeling (MACM) in the BrainMap database.",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+          "name": "Ventral subregion of right dorsal premotor cortex"
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+      ],
+      "contributors": [
+        "Genon, Sarah",
+        "Eickhoff, Simon B.",
+        "Jiang, Tianzi",
+        "Amunts, Katrin",
+        "Caspers, S.",
+        "Moebus, Susanne",
+        "Fox, P.T.",
+        "Grefkes, Christian",
+        "Langner, Robert",
+        "Reid, Andrew T.",
+        "Hoffstaedter, Felix",
+        "Cieslik, Edna C.",
+        "Müller, Veronika I.",
+        "Fan, Lingzhong",
+        "Li, Hai"
+      ],
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+        "10.25493/3MA9-GYF"
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+      "publications": [
+        {
+          "name": "The Right Dorsal Premotor Mosaic: Organization, Functions, and Connectivity",
+          "cite": "Genon, S., Li, H., Fan, L., Müller, V. I., Cieslik, E. C., Hoffstaedter, F., … Eickhoff, S. B. (2016). The Right Dorsal Premotor Mosaic: Organization, Functions, and Connectivity. Cerebral Cortex, bhw065. ",
+          "doi": "10.1093/cercor/bhw065"
+        }
+      ]
+    },
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+      "custodians": [
+        "DeFelipe, Javier"
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+      "description": "We have processed brain material from transgenic mice expressing the enhanced green fluorescent protein (EGFP) coupled with the synaptic protein PSD95 (EGFP95) in order to accomplish the automated counting process using 3D connected components of fluorescent puncta in stacks of confocal microscope images for the CA1 region of the hippocampal formation. Furthermore, we have developed a PSDs volume segmentation and quantification algorithm designed to be executed in a supercomputer in order to obtain results in multiple brain regions in a short time. We have currently uploaded 6 examples of the 3D automated counting process of the PSD95 protein using confocal microscopy, from the following CA1 regions: stratum oriens (SO), CA1 stratum radiatum (SR), CA1 stratum lacunosum moleculare (SLM).\\r\\nIn addition, brain sections in the same regions and layers as those proccesed for confocal microscopy, have been processed for 3D electron microscopy using FIB/SEM in order to crossvalidate and interpret confocal data with ultrastructural data with on the number and size of PSD puncta in different brain regions. We have currently uploaded 1 example of the 3D counting process of PSD puncta using FIB/SEM from the CA1 SR region.\n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
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+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "name": "Synapse maps - confocal",
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+      "contributors": [
+        "Gonzalez, Santiago",
+        "Kastanauskaite, Asta",
+        "Alvarez, Carmen",
+        "Benevides, Ruth",
+        "Rodriguez, Rodrigo",
+        "Valdes, Lorena",
+        "DeFelipe, Javier",
+        "Muñoz, A",
+        "Tapia, Silvia",
+        "Merchan-Perez, Angel",
+        "Santuy, Andrea"
+      ],
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+    {
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+        "NIFTI"
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+          "name": "MNI Colin 27"
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+      ],
+      "custodians": [
+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic Area FG2 (FusG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area FG2 (FusG). The probability map of Area FG2 (FusG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-FG2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area FG2 (FusG)"
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Caspers, Julian",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B.",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/X835-SK7"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus",
+          "cite": "Caspers, J., Zilles, K., Eickhoff, S. B., Schleicher, A., Mohlberg, H., & Amunts, K. (2012). Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus. Brain Structure and Function, 218(2), 511–526. ",
+          "doi": "10.1007/s00429-012-0411-8"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic CA3 (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to CA3 (Hippocampus). The probability map of CA3 (Hippocampus) is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA3.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "CA3 (Hippocampus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of CA3 (Hippocampus)",
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+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/CQSD-FDR"
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+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Interposed Nucleus (Cerebellum) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Interposed Nucleus (Cerebellum). The probability map of Interposed Nucleus (Cerebellum) is given in file jubrain-pmap-v22c_space-mnicolin27_Cerebellum-Ninterp.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Interposed Nucleus (Cerebellum)"
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+      "name": "Probabilistic cytoarchitectonic map of Interposed Nucleus (Cerebellum)",
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+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Minnerop, Martina",
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+        "Bludau, Sebastian",
+        "Tellmann, Stefanie"
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+          "doi": "10.3389/fnana.2015.00054"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Supramarginal and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_SM-Ins_0"
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
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+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
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+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 45 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 45"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area 45",
+      "files": [
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+          "name": "45_fp_20171202.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_45/45_fp_20171202.xlsx",
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+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
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+          "name": "45_pr_examples.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_45/45_pr_examples.zip",
+          "contentType": "application/zip"
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+          "name": "45_ar_examples.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_45/45_ar_examples.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Friederici, Angela D.",
+        "Morosan, Patricia",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/QFSY-YWC"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Broca's Region: Novel Organizational Principles and Multiple Receptor Mapping",
+          "cite": "Amunts, K., Lenzen, M., Friederici, A. D., Schleicher, A., Morosan, P., Palomero-Gallagher, N., & Zilles, K. (2010). Broca’s Region: Novel Organizational Principles and Multiple Receptor Mapping. PLoS Biology, 8(9), e1000489. ",
+          "doi": "10.1371/journal.pbio.1000489"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc2 (V2, 18) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc2 (V2, 18). The probability map of  Area hOc2 (V2, 18) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc2 (V2, 18)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc2 (V2, 18)",
+      "files": [
+        {
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc2.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Schormann, Thorsten",
+        "Mohlberg, Hartmut",
+        "Malikovic, Aleksandar"
+      ],
+      "kgReference": [
+        "10.25493/FKMC-ZX7"
+      ],
+      "publications": [
+        {
+          "name": "Brodmann's Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable?",
+          "cite": "Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., & Zilles, K. (2000). Brodmann’s Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable? NeuroImage, 11(1), 66–84. ",
+          "doi": "10.1006/nimg.1999.0516"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc4la (LOC) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc4la (LOC). The probability map of Area hOc4la (LOC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc4la.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc4la (LOC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc4la (LOC)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc4la.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc4la.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Malikovic, Aleksandar",
+        "Zilles, Karl",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola",
+        "Kujovic, Milenko",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/FCQW-EZU"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lp",
+          "cite": "Malikovic, A., Amunts, K., Schleicher, A., Mohlberg, H., Kujovic, M., Palomero-Gallagher, N., … Zilles, K. (2015). Cytoarchitecture of the human lateral occipital cortex: mapping of two extrastriate areas hOc4la and hOc4lp. Brain Structure and Function, 221(4), 1877–1897. ",
+          "doi": "10.1007/s00429-015-1009-8"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Postcentral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoC-SM_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoC-SM_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+        {
+          "byteSize": 68240520,
+          "name": "lh_PoC-SM_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoC-SM_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Middle Orbitofrontal and Superior Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_MOF-ST_0"
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+      ],
+      "name": "Probabilistic maps of lh_MOF-ST_0",
+      "files": [
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+          "name": "lh_MOF-ST_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_MOF-ST_0.nii",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Motor task that was designed with the intent of extracting maps on gross motor topography, in particular responses associated with movements of the foot, hand and tongue. There were thus five categories of motor tasks blocks involving the left foot, the right foot, the left hand, the right hand, and the tongue, respectively. The blocks always started with visual cues referring to which part of the body should be moved. The cues were then followed by a set of events that were indicated by flashing arrows on the screen. The subjects had to move in synchrony with the flashes. The task was formed by five blocks per category, with a total of twenty blocks per run. The order of the block categories was pseudo-randomized during each run, but fixed for all participants. A fixation-dot period of fifteen seconds was inserted between some blocks. All blocks contained ten trials. Every trial included a cue of one second and a period of performance of twelve seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Motor task",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Pinho, Ana Luísa",
+        "Thirion, Bertrand",
+        "Hertz-Pannier, Lucie",
+        "Dehaene, Stanislas",
+        "Pallier, Christophe",
+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
+        "Dohmatob, Elvis",
+        "Fabre, Murielle",
+        "Ruest, Torsten",
+        "Amadon, Alexis"
+      ],
+      "kgReference": [
+        "10.25493/PR7B-HND"
+      ],
+      "publications": [
+        {
+          "name": "Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping",
+          "cite": "Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., … Thirion, B. (2018). Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Scientific Data, 5, 180105. ",
+          "doi": "10.1038/sdata.2018.105"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area Id1 (Insula) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area Id1 (Insula). The probability map of Area Id1 (Insula) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-Id1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Id1 (Insula)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area Id1 (Insula)",
+      "files": [
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-Id1.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Kurth, F.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Hoemke, L.",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/KPB6-2AX"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex",
+          "cite": "Kurth, F., Eickhoff, S. B., Schleicher, A., Hoemke, L., Zilles, K., & Amunts, K. (2009). Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex. Cerebral Cortex, 20(6), 1448–1461. ",
+          "doi": "10.1093/cercor/bhp208"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti, Csv, Txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "#TitleARCHI fMRI Database: social paradigm##SummaryThis dataset contains functional magnetic resonance images (fMRIs) of 77 subjects performing different tasks of a  social paradigm: 1) speech versus non-speech sounds, 2) false belief- versus mechanistic kind of inference after visual or auditory presentation, 3) interacting versus non-interacting figures in the movie clip. The tasks were executed in a (small) block design (5-7s each) with acquisition duration of  489s.\nVisual stimuli were displayed in four 250-ms epochs, separated by 100ms intervals (i.e., 1.3s in total). Auditory stimuli were drawn from a recorded male voice (i.e., a total of 1.6s for motor instructions, 1.2-1.7s for sentences, and 1.2-1.3s for subtraction). The auditory or visual stimuli were shown to the participants for passive viewing or button response in event-related paradigms. Post-scan questions verified that the experimental tasks were understood and followed correctly. \nWhole-brain EPI data were acquired with the same Siemens Trio with a 32 channel head coil (TR=2400ms, TE=30ms, flip angle=60°, in-plane FOV= 19.2 * 19.2 cm, 40 slices, 3.0mm isotropic voxels). A posterior-anterior phase encoding scheme was used for all images. Standard preprocessing was performed with SPM, including slice timing, motion correction, alignment, and spatial normalization.",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "ARCHI_fMRI",
+      "files": [],
+      "contributors": [
+        "Yeh, Chun-Hung",
+        "Lebois, Alice",
+        "Poupon, Fabrice",
+        "Rivière, Denis",
+        "Roca, Pauline",
+        "Duclap, Delphine",
+        "Thirion, Bertrand",
+        "Poupon, Cyril",
+        "Pinel, Philippe"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "This study aimed to address the functional role of transynaptic signaling in the formation, stabilization and plasticity of the synapses and lastly, to generate modeling data.   \n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Short-term plasticity (STP) at GABAergic synapses",
+      "files": [],
+      "contributors": [
+        "Pizzarelli, Rocco",
+        "Giustizieri, Michela",
+        "Marinelli, Silvia",
+        "Pimpinella, Domenico"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 7A using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\n kainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp, pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 7A"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area 7A",
+      "files": [
+        {
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+          "name": "7A_fp_20180321.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_7A/7A_fp_20180321.xlsx",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_7A/7A_pr_examples.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Grefkes, Christian",
+        "Scheperjans, Filip",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/DQJ7-KC8"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Transmitter receptors reveal segregation of cortical areas in the human superior parietal cortex: Relations to visual and somatosensory regions",
+          "cite": "Scheperjans, F., Palomero-Gallagher, N., Grefkes, C., Schleicher, A., & Zilles, K. (2005). Transmitter receptors reveal segregation of cortical areas in the human superior parietal cortex: Relations to visual and somatosensory regions. NeuroImage, 28(2), 362–379. ",
+          "doi": "10.1016/j.neuroimage.2005.06.028"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "png"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Evans, Alan C.",
+        "Amunts, Katrin"
+      ],
+      "description": "BigBrain is a ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. This dataset contains the reconstituted sections in the sagittal dimension. In addition, the final aligned sections in the coronal and axial dimension, and 3D histological volumes in histological space are also available.\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Coronal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/1f50424206be7a0c1b8379bf8817be5e)\"\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Axial](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/c051f7cb18b7c998d815fcb71ed6b5d7)\"\n- Dataset \"[BigBrain High-resolution whole brain model: Downscaled volumes in minc and NIfTI format](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/5cd7a689c75124a7455e1c31c9f478e0)\"",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "BigBrain High-resolution whole brain model: 2D Sections/Sagittal",
+      "files": [],
+      "contributors": [
+        "Amunts, Katrin",
+        "Evans, Alan C.",
+        "Zilles, Karl",
+        "Lippert, T.",
+        "Shah, Nadim J.",
+        "Oros-Peusquens, Ana-Maria",
+        "Lewis, Lindsay B.",
+        "Bazin, P.-L.",
+        "Bludau, Sebastian",
+        "Rousseau, M.-E.",
+        "Dickscheid, Timo",
+        "Mohlberg, Hartmut",
+        "Borgeat, L.",
+        "Lepage, Claude"
+      ],
+      "kgReference": [
+        "10.1126/science.1235381"
+      ],
+      "publications": [
+        {
+          "name": "BigBrain: An Ultrahigh-Resolution 3D Human Brain Model",
+          "cite": "Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-E., … Evans, A. C. (2013). BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science, 340(6139), 1472–1475. ",
+          "doi": "10.1126/science.1235381"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Middle Temporal and Superior Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_MT-ST_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_MT-ST_0",
+      "files": [
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_MT-ST_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Precentral and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PrC-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PrC-SF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_PrC-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PrC-SF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
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+        "Nifti Files, gifti files, csv files, json files"
+      ],
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+      "custodians": [
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+        "Creative Commons CC0 - No Rights Reserved"
+      ],
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+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Precentral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+      "custodians": [
+        "Merchan-Perez, Angel"
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+      "description": "Data on synapses in the somatosensory cortex of the adult mouse, obtained with three-dimensional electron microscopy (FIB-SEM).\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
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+      "name": "hbp-00964_SS-FIBSEM_Dataset",
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+        "DeFelipe, Javier",
+        "Rodriguez, Rodrigo",
+        "Turegano, Marta"
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+      "formats": [
+        "NIFTI"
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+          "name": "MNI Colin 27"
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+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic Area hOc1 (V1, 17, CalcS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc1 (V1, 17, CalcS). The probability map of  Area hOc1 (V1, 17, CalcS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Zilles, Karl",
+        "Schormann, Thorsten",
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+        "Malikovic, Aleksandar"
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+          "doi": "10.1006/nimg.1999.0516"
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+        {
+          "name": "MNI Colin 27"
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+      "description": "This dataset contains the distinct architectonic Fastigial Nucleus (Cerebellum) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Fastigial Nucleus (Cerebellum). The probability map of Fastigial Nucleus (Cerebellum) is given in file jubrain-pmap-v22c_space-mnicolin27_Cerebellum-Nfast.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
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+        "Free"
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+      "parcellationRegion": [
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+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Minnerop, Martina",
+        "Eickhoff, Simon B.",
+        "Bludau, Sebastian",
+        "Tellmann, Stefanie"
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+        "10.25493/JNN1-V3Q"
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+          "name": "Cytoarchitectonic mapping of the human brain cerebellar nuclei in stereotaxic space and delineation of their co-activation patterns",
+          "cite": "Tellmann, S., Bludau, S., Eickhoff, S., Mohlberg, H., Minnerop, M., & Amunts, K. (2015). Cytoarchitectonic mapping of the human brain cerebellar nuclei in stereotaxic space and delineation of their co-activation patterns. Frontiers in Neuroanatomy, 09. ",
+          "doi": "10.3389/fnana.2015.00054"
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+          "name": "MNI Colin 27"
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+      "contributors": [
+        "Scheperjans, Filip",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Hoemke, L.",
+        "Mohlberg, Hartmut",
+        "Zilles, Karl",
+        "Hermann, K.",
+        "Eickhoff, Simon B."
+      ],
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+        "10.25493/CG41-Q6U"
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+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
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+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V2"
+        }
+      ],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial horizontal brain sections showing neuropsin (Nop) promoter expression in a bigenic Nop-tetracycline-transactivator (tTA) mouse brain (case 2877, adult male), using a driver-reporter construct in which the Nop-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Our data show that the Nop-tTA promoter mainly is expressed in the entorhinal cortex, but also in several other cortical regions.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Neuropsin tetracycline-transactivator expression: horizontal sections (case 2877)",
+      "files": [],
+      "contributors": [
+        "Yetman, Michael J.",
+        "Leergaard, Trygve B.",
+        "Lillehaug, Sveinung",
+        "Jankowsky, Joanna L.",
+        "Bjaalie, Jan G."
+      ],
+      "kgReference": [
+        "10.25493/AYBB-BXV"
+      ],
+      "publications": [
+        {
+          "name": "Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex",
+          "cite": "Yetman, M. J., Lillehaug, S., Bjaalie, J. G., Leergaard, T. B., & Jankowsky, J. L. (2015). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex. Brain Structure and Function, 221(4), 2231–2249. ",
+          "doi": "10.1007/s00429-015-1040-9"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Waxholm Space rat brain atlas v.2.0"
+        }
+      ],
+      "custodians": [
+        "Leergaard, Trygve B."
+      ],
+      "description": "Brain-wide serial microscopic images of coronal and sagittal rat brain sections stained with thionine to visualize cytoarchitecture, or with Woelche's myelin staining method to visualize myeloarchitecture.",
+      "licenseInfo": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Atlas of normal rat brain cyto- and myeloarchitecture",
+      "files": [],
+      "contributors": [
+        "Lillehaug, Sveinung",
+        "Leergaard, Trygve B.",
+        "Bjaalie, Jan G.",
+        "Dale, Anders M."
+      ],
+      "kgReference": [
+        "10.25493/C63A-FEY"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Pars Orbitalis and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_Or-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_Or-Ins_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_Or-Ins_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_Or-Ins_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Middle Temporal and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_MT-SM_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_MT-SM_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_MT-SM_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_MT-SM_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "DeFelipe, Javier"
+      ],
+      "description": "Density and distribution of different types of cells in the somatosensory cortex in fixed brains.   \n \n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Embargoed"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Cell density and distribution in the somatosensory cortex of the mouse brain",
+      "files": [],
+      "contributors": [
+        "Alonso-Nanclares, Lidia",
+        "Tapia,Silvia",
+        "Garcia,Ana",
+        "Fernaud,Isabel",
+        "Muñoz,Alberto",
+        "Anton, Alejandro",
+        "Cano, Deborah",
+        "Alvarez, Carmen"
+      ],
+      "kgReference": [
+        "10.25493/4HFC-6ZX"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 7PC (SPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 7PC (SPL). The probability map of Area 7PC (SPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-7PC.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 7PC (SPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 7PC (SPL)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-7PC.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-7PC.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B.",
+        "Hoemke, L.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Hermann, K."
+      ],
+      "kgReference": [
+        "10.25493/27J7-2YX"
+      ],
+      "publications": [
+        {
+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        },
+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Lateral Orbitofrontal and Rostral Middle Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_LOF-RMF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_LOF-RMF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_LOF-RMF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_LOF-RMF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area ifs4 (IFS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifs4 (IFS). The probability map of  Area ifs4 (IFS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifs4.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifs4 (IFS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifs4 (IFS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area PFop using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PFop"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area PFop",
+      "files": [
+        {
+          "byteSize": 16283,
+          "name": "PFop_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_PFop/PFop_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        },
+        {
+          "byteSize": 44096,
+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/9G1P-02S"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics",
+          "cite": "Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics. Cerebral Cortex, 23(3), 615–628. ",
+          "doi": "10.1093/cercor/bhs048"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Relational task that employed a relational match-to-sample paradigm, featuring a second-order comparison of relations between two pairs of objects. It served primarily as a localizer of the rostrolateral prefrontal cortex, since relational matching mechanisms have been shown to yield activity in this region. Similar to some previous tasks, the paradigm included two categories of blocks, namely relational-processing and control-matching blocks. All blocks were constituted by a set of events. In the relational-processing block, visual stimuli consisted of images representing two pairs of objects, in which one pair was placed at the top and the other one at the bottom of the image, respectively. Objects within a pair may differ in two features: shape and texture. The participants had to identify whether the pair of objects from the top differed in a specific feature and, subsequently, they were asked to determine whether the pair from the bottom changed along the same feature. For the control block, one pair of objects was displayed at the top of the image and a single object at the bottom of the same image. In addition, a cue was shown in the middle of that image, indicating which feature is relevant. The participants had thus to indicate whether the object from the bottom was matching either of the two objects from the top, according to the feature specified as a cue. This task included twelve blocks per run, with six blocks per category. Block categories were, in turn, interleaved for display within a run. A fixation-cross period of sixteen seconds was inserted between some blocks. All blocks contained six trials and they were always initiated by a cue of two seconds. The trials were described by a visual-stimulus plus response period followed by a fixation-cross period, lasting up to ten seconds. The duration of the former was nine seconds and 7.6 seconds during the relational-processing block and controlmatching block, respectively.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
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+      "description": "This dataset contains the distinct architectonic Area ifj1 (IFS/PreS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifj1 (IFS/PreS). The probability map of  Area ifj1 (IFS/PreS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifj1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifj1 (IFS/PreS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifj1 (IFS/PreS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Lateral Orbitofrontal and Superior Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_LOF-ST_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_LOF-ST_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_LOF-ST_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_LOF-ST_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Pars Opercularis and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_Op-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_Op-SF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_Op-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_Op-SF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "png"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Evans, Alan C.",
+        "Amunts, Katrin"
+      ],
+      "description": "BigBrain is a ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. This dataset contains the reconstituted sections in the coronal dimension. In addition, the final aligned sections in the axial and sagittal dimension, and 3D histological volumes in histological space are also available.\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Axial](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/c051f7cb18b7c998d815fcb71ed6b5d7)\"\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Sagittal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/9e36fc587ce8193795137371a85a351e)\"\n- Dataset \"[BigBrain High-resolution whole brain model: Downscaled volumes in minc and NIfTI format](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/5cd7a689c75124a7455e1c31c9f478e0)\"",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "BigBrain High-resolution whole brain model: 2D Sections/Coronal",
+      "files": [],
+      "contributors": [
+        "Amunts, Katrin",
+        "Evans, Alan C.",
+        "Zilles, Karl",
+        "Lippert, T.",
+        "Shah, Nadim J.",
+        "Oros-Peusquens, Ana-Maria",
+        "Lewis, Lindsay B.",
+        "Bazin, P.-L.",
+        "Bludau, Sebastian",
+        "Rousseau, M.-E.",
+        "Dickscheid, Timo",
+        "Mohlberg, Hartmut",
+        "Borgeat, L.",
+        "Lepage, Claude"
+      ],
+      "kgReference": [
+        "10.1126/science.1235381"
+      ],
+      "publications": [
+        {
+          "name": "BigBrain: An Ultrahigh-Resolution 3D Human Brain Model",
+          "cite": "Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-E., … Evans, A. C. (2013). BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science, 340(6139), 1472–1475. ",
+          "doi": "10.1126/science.1235381"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "electrophysiological data",
+        "xml"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "<Unknown>"
+        }
+      ],
+      "custodians": [
+        "Brochier, Thomas",
+        "Denker, Michael",
+        "Zehl, Lyuba",
+        "Riehle, Alexa"
+      ],
+      "description": "We provide two electrophysiological datasets recorded via a 10-by-10 multi-electrode array chronically implanted in the motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task. The datasets contain the continuous measure of extracellular potentials at each electrode sampled at 30 kHz, the local field potentials sampled at 1 kHz and the timing of the online and offline extracted spike times. It also includes the timing of several task-related and behavioral events recorded along with the electrophysiological data. Finally, the datasets provide a complete set of metadata structured in a standardized format. These metadata allow easy access to detailed information about the datasets such as the settings of the recording hardware, the array specifications, the location of the implant in the motor cortex, information about the monkeys, or the offline spike sorting. The two datasets can be exploited to address crucial issues in neurophysiology such as: What are the principles of neural interactions in a local cortical network and how are these interactions modulated during a well-described behavioral task? How different neuronal signals such as single-unit activity, multi-unit activity or LFPs relate to each other? Which spike sorting methods provide the best estimate of single unit activity? The dataset is explain in detail in: Brochier, T., Zehl, L., Hao, Y., Duret, M., Sprenger, J., Denker, M., Grün, S., and Riehle, A. (2018). Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Scientific Data 5, 180055.",
+      "licenseInfo": [],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [
+        {
+          "name": "Dorsolateral Motor Cortex"
+        },
+        {
+          "name": "Dorsolateral Premotor Cortex"
+        }
+      ],
+      "name": "Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task",
+      "files": [],
+      "contributors": [
+        "Brochier, Thomas",
+        "Riehle, Alexa",
+        "Grün, Sonja",
+        "Denker, Michael",
+        "Sprenger, Julia",
+        "Duret, Margaux",
+        "Hao, Yaoyao",
+        "Zehl, Lyuba"
+      ],
+      "kgReference": [
+        "10.12751/g-node.f83565"
+      ],
+      "publications": [
+        {
+          "name": "Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task",
+          "cite": "Brochier, T., Zehl, L., Hao, Y., Duret, M., Sprenger, J., Denker, M., … Riehle, A. (2018). Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Scientific Data, 5, 180055. ",
+          "doi": "10.1038/sdata.2018.55"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CMF-SF_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CMF-SF_1",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_CMF-SF_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_CMF-SF_1.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area s24 (sACC) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area s24 (sACC). The probability map of Area s24 (sACC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-s24.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area s24 (sACC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area s24 (sACC)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-s24.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-s24.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Vogt, Brent A.",
+        "Schleicher, Axel",
+        "Hoffstaedter, Felix",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/FQ3R-3JX"
+      ],
+      "publications": [
+        {
+          "name": "Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity",
+          "cite": "Palomero-Gallagher, N., Eickhoff, S. B., Hoffstaedter, F., Schleicher, A., Mohlberg, H., Vogt, B. A., … Zilles, K. (2015). Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity. NeuroImage, 115, 177–190. ",
+          "doi": "10.1016/j.neuroimage.2015.04.053"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Parietal and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IP-SM_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_IP-SM_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_IP-SM_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_IP-SM_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Storm, Johan"
+      ],
+      "description": "The aim of this task is to develop a protocol and a set-up for recording EEG signal from a large number of cortical areas in head-restrained rats exposed to anesthesia and during wakefulness. In order to quantify cortical complexity and its relation to (un)consciousness, a large scale EEG that samples activity from most of cortical areas is needed (Massimini et al. Science, 2005; Casali et al. Sci Transl Med, 2013). Unfortunately, only few existing probes allow this large scale recording in rodents (Megévand et al. Neuroimage, 2008; Choi et al. J Neurophysiol, 2010). Besides, since those probes are designed to be laid down on the skull surface, the obtained scalp EEG is subjected to the filtering action of the skull tissue, which separates the brain surface from the electrode. This would reduce the amplitude and the high frequency resolution of the cortical signal (Buzsàki et al. Nat Rev Neuroscience, 2012). Alternatively, epidural screw electrodes, by directly contacting the outer surface of the dura mater, showed a high signal amplitude and resolution, even for high frequencies (Arena et al. J Physiol, 2016). Therefore, we expanded the spatial density of epidural electrodes and optimized a protocol for the surgical implantation of a chronic array of 17 stainless steel electrodes by covering most of the cortical areas of a rat brain. Then, in combination with protocol for head restraining (Moore et al. Nat Protocols, 2014), we obtained multi-channel spontaneous EEG recordings from rats during wakefulness and during the exposure to low and high doses of 3 different anesthetics (propofol, sevoflurane and ketamine).   \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial 4.0 International"
+      ],
+      "embargoStatus": [
+        "Embargoed"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Large scale multi-channel EEG in rats",
+      "files": [],
+      "contributors": [
+        "Alessandro, Arena",
+        "Storm, Johan"
+      ],
+      "kgReference": [
+        "10.25493/4SPM-V00"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Inferior Parietal and Superior Parietal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_IP-SP_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_IP-SP_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_IP-SP_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_IP-SP_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+        "Gonzalez, Santiago",
+        "Valdes, Lorena",
+        "Tapia, Silvia",
+        "Rodriguez, Rodrigo",
+        "DeFelipe, Javier",
+        "Merchan-Perez, Angel",
+        "Alvarez, Carmen",
+        "Santuy, Andrea",
+        "Kastanauskaite, Asta",
+        "Muñoz, A"
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+      ],
+      "contributors": [
+        "Bludau, Sebastian",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Fox, P.T.",
+        "Laird, A.R.",
+        "Caspers, S.",
+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B."
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+        "10.25493/RPDP-VMG"
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+          "name": "Cytoarchitecture, probability maps and functions of the human frontal pole",
+          "cite": "Bludau, S., Eickhoff, S. B., Mohlberg, H., Caspers, S., Laird, A. R., Fox, P. T., … Amunts, K. (2014). Cytoarchitecture, probability maps and functions of the human frontal pole. NeuroImage, 93, 260–275. ",
+          "doi": "10.1016/j.neuroimage.2013.05.052"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Inferior Parietal and Lateral Occipital gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+        }
+      ]
+    },
+    {
+      "formats": [
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+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
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+      ],
+      "custodians": [
+        "Amunts, Katrin"
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+      "description": "This dataset contains the distinct architectonic Area Fo3 (OFC) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area Fo3 (OFC). The probability map of Area Fo3 (OFC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-Fo3.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Fo3 (OFC)"
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+      "name": "Probabilistic cytoarchitectonic map of Area Fo3 (OFC)",
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+          "contentType": "application/octet-stream"
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+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Bludau, Sebastian",
+        "Eickhoff, Simon B.",
+        "Gerboga, Fatma",
+        "Schleicher, Axel",
+        "Palomero-Gallagher, Nicola",
+        "Zilles, Karl",
+        "Henssen, Anton"
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+        "10.25493/9ZWB-EEZ"
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+          "doi": "10.1016/j.cortex.2015.11.006"
+        }
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+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Gambling task. Its aim was to localize brain structures of the reward system, namely the basal ganglia complex. The paradigm included eight blocks and each block was composed of eight events. For every event, the participants were asked to play a game. The goal was to guess whether the next number to be displayed, which ranged from one to nine, would be smaller or larger than five while a question mark was shown on the screen. The answer was given by pressing the respective button of the response box. Feedback on the correct number was provided afterwards. There was an equal amount of blocks; the participants experienced a majority of either reward or loss events in each of them. The task was constituted by eight blocks per run, in which each half related to reward and loss experience, respectively. The order of the two block cate gories was pseudo-randomized during a single run, but fixed for all participants. A fixation-cross period of fifteen seconds was displayed between blocks. All blocks contained eight trials. The trials included a question-mark visual stimulus lasting up to 1.5 seconds, a feedback period of one second and a fixation-cross period of one second, as well; the total duration of the trial was then 3.5 seconds approximately.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
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+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Gambling task",
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+          "name": "sub-02_ses-04_task-HcpGambling_acq-pa_bold.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-02/ses-04/func/sub-02_ses-04_task-HcpGambling_acq-pa_bold.nii.gz",
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+        },
+        {
+          "byteSize": 1519,
+          "name": "sub-04_ses-01_task-HcpGambling_acq-ap_events.tsv",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-04/ses-01/func/sub-04_ses-01_task-HcpGambling_acq-ap_events.tsv",
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+          "name": "sub-13_ses-01_task-HcpGambling_acq-ap_events.tsv",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-13/ses-01/func/sub-13_ses-01_task-HcpGambling_acq-ap_events.tsv",
+          "contentType": "text/tab-separated-values"
+        }
+      ],
+      "contributors": [
+        "Pinho, Ana Luísa",
+        "Thirion, Bertrand",
+        "Hertz-Pannier, Lucie",
+        "Dehaene, Stanislas",
+        "Pallier, Christophe",
+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
+        "Dohmatob, Elvis",
+        "Fabre, Murielle",
+        "Ruest, Torsten",
+        "Amadon, Alexis"
+      ],
+      "kgReference": [
+        "10.25493/Z8J1-1H3"
+      ],
+      "publications": [
+        {
+          "name": "Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping",
+          "cite": "Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., … Thirion, B. (2018). Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Scientific Data, 5, 180105. ",
+          "doi": "10.1038/sdata.2018.105"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Entorhinal Cortex in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Entorhinal Cortex. The probability map of Entorhinal Cortex is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-EC.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Entorhinal Cortex"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Entorhinal Cortex",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Hippocampus-EC.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Hippocampus-EC.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Habel, U.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/373S-NVK"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "ASC, DAT"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Mansvelder, Huibert D."
+      ],
+      "description": "To populate models of synapses, neurons and networks of neurons in the human brain with accurate morphological and physiological parameters of human neurons, data from neuronal recordings are being exchanged continuously between Partner in SP2 (Mansvelder, VU) and in SP4 and SP6 (Segev, HUJI). A reference data set with digitally reconstructed neurons with matching physiological data from these neurons is provided by Mansvelder and used by Segev to model single neuron properties. Also morphologies and physiologies of synaptically connected neuron pairs have been exchanged. Using feature extraction, active properties of dendrites and axons of different types of neurons are currently being established (Eyal et al, 2014).",
+      "licenseInfo": [],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Brodmann area 21"
+        }
+      ],
+      "name": "Morphological data of human neocortical pyramidal neurons",
+      "files": [
+        {
+          "byteSize": 253798,
+          "name": "google-doc.pdf",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000043/google-doc.pdf",
+          "contentType": "application/pdf"
+        },
+        {
+          "byteSize": 16785104,
+          "name": "EPhys_Morpho_Human_PYR.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000043/EPhys_Morpho_Human_PYR.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "de Kock, Christiaan P.J.",
+        "Mansvelder, Huibert D.",
+        "Segev, Idan",
+        "Baayen, Johannes C.",
+        "Narayanan, Rajeevan T.",
+        "Boudewijns, Zimbo S.R.M.",
+        "Obermayer, Joshua",
+        "Testa-Silva, Guilherme",
+        "van der Sluis, Sophie",
+        "Groot, Colin",
+        "Asamoah, Boateng",
+        "Goriounova, Natalia A.",
+        "Lodder, Brendan N.",
+        "Aardse, Romy",
+        "Eyal, Guy",
+        "Doreswamy, Keerthi K.",
+        "Verhoog, Matthijs B.",
+        "Mohan, Hemanth"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Dendritic and Axonal Architecture of Individual Pyramidal Neurons across Layers of Adult Human Neocortex",
+          "cite": "Mohan, H., Verhoog, M. B., Doreswamy, K. K., Eyal, G., Aardse, R., Lodder, B. N., … de Kock, C. P. J. (2015). Dendritic and Axonal Architecture of Individual Pyramidal Neurons across Layers of Adult Human Neocortex. Cerebral Cortex, 25(12), 4839–4853. ",
+          "doi": "10.1093/cercor/bhv188"
+        },
+        {
+          "name": "Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex",
+          "cite": "Deitcher, Y., Eyal, G., Kanari, L., Verhoog, M. B., Atenekeng Kahou, G. A., Mansvelder, H. D., … Segev, I. (2017). Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex. Cerebral Cortex, 27(11), 5398–5414. ",
+          "doi": "10.1093/cercor/bhx226"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "A general procedure to fit individual synaptic events recorded from voltage clamp experiments was tested with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. The data used here are obtained from coronal hippocampal slices (300 μm thick) from 3-4 months old wt (B6/SJL) female mice. The dataset contains 701 individual events recorded from 7 different neurons (expB1-B7).\nThe first column of each txt file contains the time in ms and the other columns contain spontaneous inhibitory synaptic currents (sIPSCs) in pA.\nComputational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In [1] we proposed a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. A Python version is available for public use, as a Jupyter Notebook in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.\nFitting experimental data against a number of different models is a common way to do this (reviewed in [2]), and can help in the subsequent interpretation of the data. In general, experimental traces are fitted using specific approaches for specific purposes (e.g., [3], [4]). However, to the best of our knowledge, there is no easy, user-friendly, general procedure available for this purpose, especially in computational neuroscience. Our aim was thus to identify the appropriate conceptual structure of a procedure to obtain good, reliable fits of raw experimental traces of spontaneous synaptic events. This is an important step because spontaneous synaptic events have been so far exclusively analyzed using traces obtained by averaging many events. However, as can be easily imagined, each synapse in any given neuron has its own, independent, history of activation. The most likely physiological consequence is that the variables relative to the subcellular processes underlying synaptic transmission are different for each synapse. If a researcher is interested in testing a specific kinetic scheme implemented for specific biochemical pathways, the use of individual events is the most appropriate choice, since this approach would give information on the different combinations of model parameters that are consistent with the observed events. Averaging traces will lose a lot of relevant information.\nWe therefore present here the implementation of a procedure leading to the development of a unifying optimization method for individual synaptic events. Experimental data, kinetic models of synaptic transmission, and fitting parameters and their dependencies can be user defined/provided or gathered from databases. They can be used to generate optimized groups of parameters able to represent a population of synapses, either for simulation purposes or to study the functional consequences of a particular protein or subcellular synaptic transmission pathway.\n[1] Lupascu CA, Morabito A, Merenda E, et al. A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory Synapses. Frontiers in Neuroinformatics. 2016;10:23. doi:10.3389/fninf.2016.00023  \n[2] Van Geit W., De Schutter E., Achard P. (2008). Automated neuron model optimization techniques: a review. Biol. Cybern. 99, 241–251. 10.1007/s00422-008-0257-6  \n[3] Bekkers J. M. (2003). Convolution of mini distributions for fitting evoked synaptic amplitude histograms. J. Neurosci. Methods. 130, 105–114. 10.1016/j.jneumeth.2003.09.018  \n[4] Meisl G., Kirkegaard J. B., Arosio P., Michaels T. C., Vendruscolo M., Dobson C. M., et al. . (2016). Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nat. Protoc. 11, 252–272. 10.1038/nprot.2016.010",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spontaneous inhibitory post-synaptic currents recorded from CA1 pyramidal neurons of adult wild type \nB6/SJL  mice",
+      "files": [],
+      "contributors": [
+        "Marinelli, Silvia",
+        "Cherubini, Enrico",
+        "Marchetti, Cristina"
+      ],
+      "kgReference": [
+        "10.25493/85F8-M92"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Precentral and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PrC-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PrC-Ins_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_PrC-Ins_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PrC-Ins_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Waxholm Space rat brain atlas v.2.0"
+        }
+      ],
+      "custodians": [
+        "D'Angelo, Egidio"
+      ],
+      "description": "Study of the synaptic responses of granule cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Loose-cell attached patch-clamp recordings from cerebellar granule cells",
+      "files": [],
+      "contributors": [
+        "D'Angelo, Egidio",
+        "Tognolina, Marialuisa"
+      ],
+      "kgReference": [
+        "10.25493/6R48-E3V"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Modulation of GABAergic currents by anti-neuroligin 2 and anti-gephyrin intrabodies (scFvGephcyto)",
+      "files": [],
+      "contributors": [
+        "Morabito, Annunziato",
+        "Giustizieri, Michela",
+        "Meli, Giovanni",
+        "Cherubini, Enrico",
+        "Cattaneo, Antonino",
+        "Marinelli, Silvia"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "nifti, Brainvisa Mesh, gifti, csv"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Dehaene-Lambertz, Ghislaine"
+      ],
+      "description": "The Infant brain template is a corrected three-dimensional reconstruction of a single MRI brain scan of a single human infant (female, age: 7.1 weeks) with a high resolution (1.1 mm isotropic). The inner cortical surface was segmented using a semi-automatic segmentation pipeline dedicated to T2-weighted MRI images of the infant brain, followed by manual correction, when local inaccuracies were detected. On the basis of the segmented sulci, 47 anatomical regions were delineated in each hemisphere, using the same principles as the AAL atlas (Automated Anatomical Labelling).",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Lateral ventricule and remaining tissues"
+        },
+        {
+          "name": "Thalamus"
+        },
+        {
+          "name": "Caudate"
+        },
+        {
+          "name": "Nuclei Putamen"
+        },
+        {
+          "name": "Anterior cingulate gyrus"
+        },
+        {
+          "name": "Middle cingulate gyrus"
+        },
+        {
+          "name": "Posterior cingulate gyrus"
+        },
+        {
+          "name": "Amygdala"
+        },
+        {
+          "name": "Limbic Parahippocampal gyrus"
+        },
+        {
+          "name": "Cuneus"
+        },
+        {
+          "name": "Calcarine sulcus"
+        },
+        {
+          "name": "Lingual gyrus"
+        },
+        {
+          "name": "Fusiform gyrus"
+        },
+        {
+          "name": "Superior occipital gyrus"
+        },
+        {
+          "name": "Middle occipital gyrus"
+        },
+        {
+          "name": "Inferior occipital gyrus"
+        },
+        {
+          "name": "Planum polare"
+        },
+        {
+          "name": "Planum temporale"
+        },
+        {
+          "name": "Planum parietale"
+        },
+        {
+          "name": "Heschl gyrus"
+        },
+        {
+          "name": "Superior temporal gyrus"
+        },
+        {
+          "name": "Middle temporal gyrus"
+        },
+        {
+          "name": "Inferior temporal gyrus"
+        },
+        {
+          "name": "Superior temporal pole"
+        },
+        {
+          "name": "Temporal Middle temporal pole"
+        },
+        {
+          "name": "Precuneus"
+        },
+        {
+          "name": "Superior parietal gyrus"
+        },
+        {
+          "name": "Inferior parietal gyrus"
+        },
+        {
+          "name": "Supramarginal gyrus"
+        },
+        {
+          "name": "Angular gyrus"
+        },
+        {
+          "name": "Paracentral lobule"
+        },
+        {
+          "name": "Postcentral gyrus"
+        },
+        {
+          "name": "Precentral gyrus"
+        },
+        {
+          "name": "Rolandic operculum"
+        },
+        {
+          "name": "Orbitary superior frontal gyrus"
+        },
+        {
+          "name": "Orbitary middle frontal gyrus"
+        },
+        {
+          "name": "Orbitary inferior frontal gyrus"
+        },
+        {
+          "name": "Orbitary frontal medial"
+        },
+        {
+          "name": "Gyrus rectus"
+        },
+        {
+          "name": "Olfactory bulb"
+        },
+        {
+          "name": "Supplementary motor area"
+        },
+        {
+          "name": "Medial superior frontal gyrus"
+        },
+        {
+          "name": "Superior frontal gyrus"
+        },
+        {
+          "name": "Middle frontal gyrus"
+        },
+        {
+          "name": "Inferior frontal gyrus, triangular part"
+        },
+        {
+          "name": "Inferior frontal gyrus, opercular part"
+        },
+        {
+          "name": "Insula"
+        }
+      ],
+      "name": "Corrected 3-D reconstruction and surface parcellation of an infant brain",
+      "files": [
+        {
+          "byteSize": 806639,
+          "name": "template_L.gii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_L.gii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 2281,
+          "name": "labeltable.csv",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/labeltable.csv",
+          "contentType": "text/csv"
+        },
+        {
+          "byteSize": 548781,
+          "name": "template_head.mesh",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_head.mesh",
+          "contentType": "model/mesh"
+        },
+        {
+          "byteSize": 3681640,
+          "name": "template_t1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_t1.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 13062,
+          "name": "template_L_atlas.gii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_L_atlas.gii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_R.gii",
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+          "name": "template_R_atlas.gii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_R_atlas.gii",
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+          "name": "template_R.mesh",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_R.mesh",
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+          "name": "template_t2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_t2.nii",
+          "contentType": "application/octet-stream"
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+        {
+          "byteSize": 604125,
+          "name": "template_L.mesh",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Infant/infant-template/template_L.mesh",
+          "contentType": "model/mesh"
+        }
+      ],
+      "contributors": [
+        "Kabdebon, C.",
+        "Dehaene-Lambertz, Ghislaine",
+        "Dubois, Jessica",
+        "Perrot, M.",
+        "Simmonet, H.",
+        "Leroy, Francois"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Anatomical correlations of the international 10–20 sensor placement system in infants",
+          "cite": "Kabdebon, C., Leroy, F., Simmonet, H., Perrot, M., Dubois, J., & Dehaene-Lambertz, G. (2014). Anatomical correlations of the international 10–20 sensor placement system in infants. NeuroImage, 99, 342–356. ",
+          "doi": "10.1016/j.neuroimage.2014.05.046"
+        }
+      ]
+    },
+    {
+      "formats": [
+        ".mhd",
+        ".raw",
+        " image/nii;version=0"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "Waxholm Space rat brain atlas v.2.0"
+        }
+      ],
+      "custodians": [
+        "Axer, Markus"
+      ],
+      "description": "The 3D fibre orientation model of a Wistar rat brain was derived from 3D-Polarized Light Imaging (3D-PLI) as described in Axer et al. (2011a). \\r\\nThe Wistar rat brain was immersion fixed in 4% paraformaldehyde. After cryoprotection (20% glycerin), the brain was deep frozen at -70°C and stored till further processing. The brain was serially sectioned in the coronal plane (section thickness 60 µm) using a large-scale cryostat microtome (Polycut CM 3500, Leica, Germany) and coverslipped with glycerin. Immediately after coverslipping, the sections were measured using the large-area polarimeter (LAP, pixel size: 64 µm x 64 µm, cf. Axer, 2011a). During sectioning, each blockface was digitized using a CCD camera mounted above the brain in order to obtain an undistorted reference image of each section. No staining was applied. This procedure resulted in an uninterrupted series of sections through the entire brain, which ultimately enabled the 3D reconstruction.\\r\\nThe application of the Jones calculus [Jones, 1941] describes the light transmittance through the LAP and enables the calculation of the individual spatial fiber orientation in each voxel (defined by pixel size and section thickness). The fiber orientation is defined by the pair of angles (α, ϕ) = (inclination, direction) indicating the fiber axis orientation out of and within the section plane, respectively. Inclination and direction angles are encoded in HSV color space to provide one fiber orientation map (FOM) per section. The entire data set of aligned FOMs (i.e. the fibre orientation model) is assembled in a single NIfTI file (http://nifti.nimh.nih.gov).\\r\\n3D reconstruction of the FOMs was done in a sequential application of affine and b-spline based image alignment steps with the reconstructed blockface volume acting as reference brain. Afterwards the fibre orientation model was transferred into the common rodent reference space, the Waxholm Space atlas [Papp et al., 2014]. The transformation of the brains into the same space was done in two steps: (i) a 3D affine registration ensured the correct spatial alignment of the brains and (ii) a subsequent 3D non-linear registration compensated non-linear distortions of the brain sections.\\r\\nThe fibre orientation model of the rat brain is not part of the proposed deliverables, but it is of utmost relevance for cross-SPs data integration and platform developments.\\r\\nThe application of polarization microscopy to unstained sections of brain tissue has been extensively described in peer-reviewed publications (e.g., Axer et al. 2011a,b, Larsen et al. 2007, Wiese et al. 2014). The 3D reconstruction of histological sections based on a blockface reference has been demonstrated by Palm et al. (2010). The Waxholm Space atlas [Papp et al. (2014)] defines the reference space for rodents in the HBP and it consists of high resolution, contrast enhanced structural (including T2 and T2*-weighted) and diffusion weighted MR images with 39 μm isotropic voxels for the MRI volume and 78 μm isotropic voxels for the DTI volume. In total 79 major anatomical structures were delineated based on the architectural details in both gray and white matter, demonstrated by the structural images.\\r\\nThe dataset comprises the following files:\\r\\n1.\\tR2022_reco_blockface.nii: reconstructed blockface volume\\r\\n2.\\tR2022_reco_foms.nii: reconstructed fiber orientation maps\\r\\n3.\\tWHS_MRI_toR2022.nii: WHS MRI volume aligned to R2022_reco_blockface.nii\\r\\n4.\\tWHS_atlas_toR2022.nii: WHS atlas template aligned to R2022_reco_blockface.nii",
+      "licenseInfo": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Wistar rat brain fibre orientation model",
+      "files": [],
+      "contributors": [
+        "Zilles, Karl",
+        "Bjaalie, Jan G.",
+        "Amunts, Katrin",
+        "Schubert, Nicole",
+        "Leergaard, Trygve B.",
+        "Huysegoms, Marcel",
+        "Huynh, Anh-Minh",
+        "Palomero-Gallagher, Nicola",
+        "Schober, Martin",
+        "Axer, Markus",
+        "Kirlangic, Mehmet E"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas.",
+          "cite": "Schubert N, Axer M, Schober M, Huynh A-M, Huysegoms M, Palomero-Gallagher N, Bjaalie JG, Leergaard TB, Kirlangic ME, Amunts K and Zilles K (2016) 3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas. Front. Neuroanat. 10:51. doi: 10.3389/fnana.2016.00051",
+          "doi": "10.3389/fnana.2016.00051"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial coronal brain sections showing Purkinje cell protein 2 (Pcp2) promoter expression in a bigenic Pcp2-tetracycline-transactivator (tTA) mouse brain (case 4340, adult male), generated using a driver-reporter construct in which the Pcp2-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data confirm earlier reports that Pcp2-tTA promoter expression is restricted to cerebellar Purkinje cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Purkinje cell protein 2 tetracycline-transactivator expression: coronal sections (case 4340)",
+      "files": [],
+      "contributors": [
+        "Bjaalie, Jan G.",
+        "Puchades, Maja A.",
+        "Yetman, Michael J.",
+        "Jankowsky, Joanna L.",
+        "Lillehaug, Sveinung",
+        "Leergaard, Trygve B.",
+        "Checinska, Martyna M."
+      ],
+      "kgReference": [
+        "10.25493/YCGE-ADC"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 45 (IFG) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 45 (IFG). The probability map of Area 45 (IFG) is given in file jubrain-pmap-v22c_space-mnicolin27_ Area-45.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 45 (IFG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 45 (IFG)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-45.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-45.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Weiss, Peter H.",
+        "Marshall, John C.",
+        "Bürgel, Uli",
+        "Amunts, Katrin",
+        "Gurd, Jennifer M.",
+        "Fink, Gereon R.",
+        "Pieperhoff, P.",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Uylings, Harry B.M."
+      ],
+      "kgReference": [
+        "10.25493/J2KZ-AZW"
+      ],
+      "publications": [
+        {
+          "name": "Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined stereotaxic space—The roles of Brodmann areas 44 and 45",
+          "cite": "Amunts, K., Weiss, P. H., Mohlberg, H., Pieperhoff, P., Eickhoff, S., Gurd, J. M., … Zilles, K. (2004). Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined stereotaxic space—The roles of Brodmann areas 44 and 45. NeuroImage, 22(1), 42–56. ",
+          "doi": "10.1016/j.neuroimage.2003.12.031"
+        },
+        {
+          "name": "Broca's region revisited: Cytoarchitecture and intersubject variability",
+          "cite": "Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H. B. M., & Zilles, K. (1999). Broca’s region revisited: Cytoarchitecture and intersubject variability. The Journal of Comparative Neurology, 412(2), 319–341. ",
+          "doi": "10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO;2-7"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the neonate, C57BL/6 (wild-type), negative pairing condition. \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from neonate C57BL/6 (wild-type) mice, negative pairing",
+      "files": [],
+      "contributors": [
+        "Sgritta, Martina",
+        "Cherubini, Enrico",
+        "Marchetti, Cristina"
+      ],
+      "kgReference": [
+        "10.25493/G3W4-X3P"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 6d2 (PreG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 6d2 (PreG). The probability map of Area 6d2 (PreG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-6d2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 6d2 (PreG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 6d2 (PreG)",
+      "files": [],
+      "contributors": [
+        "Sigl, Benjamin",
+        "Amunts, Katrin",
+        "Eickhoff, Simon B.",
+        "Mohlberg, Hartmut",
+        "Bludau, Sebastian",
+        "Caspers, Svenja"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Rostral Anterior Cingulate and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_RAC-SF_1"
+        }
+      ],
+      "name": "Probabilistic maps of lh_RAC-SF_1",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_RAC-SF_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_RAC-SF_1.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Parietal and Superior Parietal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IP-SP_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_IP-SP_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_IP-SP_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_IP-SP_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area Fp2 (Fpole) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area Fp2 (Fpole). The probability map of Area Fp2 (Fpole) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-Fp2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Fp2 (Fpole)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area Fp2 (Fpole)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-Fp2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-Fp2.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Bludau, Sebastian",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Fox, P.T.",
+        "Laird, A.R.",
+        "Caspers, S.",
+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/26WT-E3P"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture, probability maps and functions of the human frontal pole",
+          "cite": "Bludau, S., Eickhoff, S. B., Mohlberg, H., Caspers, S., Laird, A. R., Fox, P. T., … Amunts, K. (2014). Cytoarchitecture, probability maps and functions of the human frontal pole. NeuroImage, 93, 260–275. ",
+          "doi": "10.1016/j.neuroimage.2013.05.052"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
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+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Parietal and Lateral Occipital gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
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+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IP-LO_1"
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+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
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+      "formats": [
+        "TIFF"
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+      "custodians": [
+        "Haug, Smejda",
+        "Mogens, Finn"
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+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Whole brain"
+        }
+      ],
+      "name": "Waxholm Space rat brain reference atlas enriched with additional receptor data",
+      "files": [],
+      "contributors": [
+        "Mogens, Finn",
+        "Haug, Smejda"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Sulphide Silver Pattern and Cytoarchitectonics of Parahippocampal Areas in the Rat",
+          "cite": "",
+          "doi": "10.1007/9783642664489"
+        },
+        {
+          "name": "Heavy Metals in the Brain",
+          "cite": "",
+          "doi": "10.1007/978-3-642-51585-9"
+        },
+        {
+          "name": "Light microscopical mapping of the hippocampal region, the pyriform cortex and the corticomedial amygdaloid nuclei of the rat with Timm's sulphide silver method",
+          "cite": "",
+          "doi": "10.1007/bf00519123"
+        }
+      ]
+    },
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+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Modulation of GABAergic currents by anti-neuroligin 2 and anti-gephyrin intrabodies (delta2-188)",
+      "files": [],
+      "contributors": [
+        "Pizzarelli, Rocco",
+        "Cherubini, Enrico",
+        "Marinelli, Silvia",
+        "Pimpinella, Domenico"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
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+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Inferior Parietal and Middle Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_IP-MT_0"
+        }
+      ],
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area PFt using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PFt"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area PFt",
+      "files": [
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+          "byteSize": 44096,
+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        },
+        {
+          "byteSize": 16310,
+          "name": "PFt_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_PFt/PFt_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/E7PM-FDC"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics",
+          "cite": "Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics. Cerebral Cortex, 23(3), 615–628. ",
+          "doi": "10.1093/cercor/bhs048"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "tif, nii"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Axer, Markus"
+      ],
+      "description": "Registration and curation of 3D-PLI data for selected anatomical structures into the atlas. The data will be registered to the Big Brain template.",
+      "licenseInfo": [],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Hippocampus"
+        }
+      ],
+      "name": "Human temporal lobe with hippocampus",
+      "files": [],
+      "contributors": [
+        "Axer, Markus"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 3b (PostCG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 3b (PostCG). The probability map of Area 3b (PostCG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-3b.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 3b (PostCG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 3b (PostCG)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-3b.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-3b.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schormann, Thorsten",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/W4WS-B3P"
+      ],
+      "publications": [
+        {
+          "name": "Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex",
+          "cite": "Geyer, S., Schormann, T., Mohlberg, H., & Zilles, K. (2000). Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex. NeuroImage, 11(6), 684–696. ",
+          "doi": "10.1006/nimg.2000.0548"
+        },
+        {
+          "name": "Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex",
+          "cite": "Geyer, S., Schleicher, A., & Zilles, K. (1999). Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex. NeuroImage, 10(1), 63–83. ",
+          "doi": "10.1006/nimg.1999.0440"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Parietal and Middle Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IP-MT_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_IP-MT_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+        {
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+          "name": "lh_IP-MT_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_IP-MT_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Superior Temporal and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_ST-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_ST-Ins_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+          "name": "lh_ST-Ins_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_ST-Ins_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic DG (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to DG (Hippocampus). The probability map of DG (Hippocampus) is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-DG.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "DG (Hippocampus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of DG (Hippocampus)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Hippocampus-DG.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Hippocampus-DG.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/1KV8-B9G"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "nifti, mnc"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Evans, Alan C.",
+        "Amunts, Katrin"
+      ],
+      "description": "BigBrain is a ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. This dataset contains the volumes in different template spaces: The original Big Brain histological space, which corresponds to the 2D section release, as well as the MNI ICBM152 and ADNI coordinate systems. In addition, the final aligned sections in the coronal, axial and sagittal dimension are also available.\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Coronal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/1f50424206be7a0c1b8379bf8817be5e)\"\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Axial](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/c051f7cb18b7c998d815fcb71ed6b5d7)\"\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Sagittal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/9e36fc587ce8193795137371a85a351e)\"",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "BigBrain High-resolution whole brain model: Downscaled volumes in minc and NIfTI format",
+      "files": [],
+      "contributors": [
+        "Amunts, Katrin",
+        "Evans, Alan C.",
+        "Zilles, Karl",
+        "Lippert, T.",
+        "Shah, Nadim J.",
+        "Oros-Peusquens, Ana-Maria",
+        "Lewis, Lindsay B.",
+        "Bazin, P.-L.",
+        "Bludau, Sebastian",
+        "Rousseau, M.-E.",
+        "Dickscheid, Timo",
+        "Mohlberg, Hartmut",
+        "Borgeat, L.",
+        "Lepage, Claude"
+      ],
+      "kgReference": [
+        "10.1126/science.1235381"
+      ],
+      "publications": [
+        {
+          "name": "BigBrain: An Ultrahigh-Resolution 3D Human Brain Model",
+          "cite": "Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-E., … Evans, A. C. (2013). BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science, 340(6139), 1472–1475. ",
+          "doi": "10.1126/science.1235381"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing RSVP Language task to localize the areas implicated in language comprehension, participants were presented with stimuli consisting of sequences of words, pseudowords or nonwords. For some conditions, these sequences were composed by well-formed sentences. They were presented as visual stimuli, using a Rapid-Serial- Visual-Presentation (RSVP) paradigm. Concretely, there were six experimental conditions featuring the different types of stimuli: i) complex meaningful sentences, containing at least two clauses (e.g. a main and a relative clause); ii) simple meaningful sentences, with only one main clause; iii) jabberwocky, obtained from well-formed sentences whose content words were replaced by meaningless, yet pronounceable pseudowords; iv) lists of words; v) lists of pseudowords; and vi) list of non-words (aka consonantstrings). Following each sequence after a short delay, a probe (which could be a word, pseudoword or non-word) was displayed with a 50% probability of having been presented in the sequence. Participants had then to indicate, by pressing one of the two possible response buttons, whether the probe had appeared in the sentence. One session of data collection comprised six runs; each of them included sixty trials related to the six experimental conditions, i.e. ten trials corresponded to one condition. The order of the trials was pseudo-randomized within and between runs across participants, such that the same experimental condition did not occur in two immediately successive trials. A trial lasted ten seconds. It started with the display of a fixation cross for two seconds, followed by the display of a blank screen for 0.5 seconds. Afterwards, a sequence of ten stimuli was presented at a rate of one stimulus per 0.4 seconds. Further, a blank screen was displayed during a randomly varying period of time between one and 1.5 seconds, followed by the display of a fixation cross for 0.5 seconds plus a probe stimulus for 0.5 seconds. Finally, the stimuli were cleared and a response-time window opened for 2 seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: RSVP Language",
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+      "contributors": [
+        "Pinho, Ana Luísa",
+        "Thirion, Bertrand",
+        "Hertz-Pannier, Lucie",
+        "Dehaene, Stanislas",
+        "Pallier, Christophe",
+        "Varoquaux, Gaël",
+        "Eger, Evelyn",
+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
+        "Denghien, Isabelle",
+        "Dohmatob, Elvis",
+        "Fabre, Murielle",
+        "Ruest, Torsten",
+        "Amadon, Alexis"
+      ],
+      "kgReference": [
+        "10.25493/PD28-TRA"
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+      "publications": [
+        {
+          "name": "Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping",
+          "cite": "Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., … Thirion, B. (2018). Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Scientific Data, 5, 180105. ",
+          "doi": "10.1038/sdata.2018.105"
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+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
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+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area Te2 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387\n\n\nThe receptor density data of this area were also published in:\n\nMorosan P, Rademacher J, Palomero-Gallagher N, and Zilles K (2005) Anatomical organization of the human auditory cortex: Cytoarchitecture and transmitter receptors. In Heil P, Scheich H, Bundinger E, and König R (Eds), The Auditory Cortex - Towards a Synthesis of Human and Animal Research. (pp. 27-50) [Mahwah, New Jersey, USA: Lawrence Erlbaum Associates](https://www.taylorfrancis.com/books/e/9781135613365/chapters/10.4324%2F9781410613066-9)",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Te2"
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+      ],
+      "name": "Density measurements of different receptors for Area Te2",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
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+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        }
+      ],
+      "contributors": [
+        "Friederici, Angela D.",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike",
+        "Morosan, Patricia",
+        "Rademacher, Jörg",
+        "Amunts, Katrin",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/C279-428"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Lateral Orbitofrontal and Middle Orbitofrontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_LOF-MOF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_LOF-MOF_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_LOF-MOF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_LOF-MOF_0.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti, Csv, Txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "#Title\nARCHI fMRI Database: spatial mapping paradigmm\n##Summarm\nThis dataset contains functional magnetic resonance images (fMRIs) of 77 subjects performing different tasks of a spatial mapping paradigm: 1) grasping versus orientation judgement, 2) left/right hand versus hand side, and 3) effect of ocular saccades. The tasks were executed in a (small) block design (5-7s each) with acquisition duration of  516s.\nVisual stimuli were displayed in four 250-ms epochs, separated by 100ms intervals (i.e., 1.3s in total). Auditory stimuli were drawn from a recorded male voice (i.e., a total of 1.6s for motor instructions, 1.2-1.7s for sentences, and 1.2-1.3s for subtraction). The auditory or visual stimuli were shown to the participants for passive viewing or button response in event-related paradigms. Post-scan questions verified that the experimental tasks were understood and followed correctly. \nWhole-brain EPI data were acquired with the same Siemens Trio with a 32 channel head coil (TR=2400ms, TE=30ms, flip angle=60°, in-plane FOV= 19.2 * 19.2 cm, 40 slices, 3.0mm isotropic voxels). A posterior-anterior phase encoding scheme was used for all images. Standard preprocessing was performed with SPM, including slice timing, motion correction, alignment, and spatial normalization.",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "ARCHI fMRI Database: spatial mapping paradigmm",
+      "files": [],
+      "contributors": [
+        "Thirion, Bertrand",
+        "Pinel, Philippe",
+        "Poupon, Cyril",
+        "Grisel, Olivier",
+        "Varoquaux, Gaël"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Principal Component Regression Predicts Functional Responses across Individuals",
+          "cite": "Thirion, B., Varoquaux, G., Grisel, O., Poupon, C., & Pinel, P. (2014). Principal Component Regression Predicts Functional Responses across Individuals. Lecture Notes in Computer Science, 741–748. ",
+          "doi": "10.1007/978-3-319-10470-6_92"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hIP3 (IPS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hIP3 (IPS). The probability map of Area hIP3 (IPS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hIP3.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hIP3 (IPS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hIP3 (IPS)",
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hIP3.nii",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Zilles, Karl",
+        "Hoemke, L.",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Scheperjans, Filip",
+        "Hermann, K."
+      ],
+      "kgReference": [
+        "10.25493/J9T6-TX9"
+      ],
+      "publications": [
+        {
+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        },
+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area PFcm using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PFcm"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area PFcm",
+      "files": [
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+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
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+        {
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+          "name": "PFcm_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_PFcm/PFcm_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/5QDP-ARH"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics",
+          "cite": "Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics. Cerebral Cortex, 23(3), 615–628. ",
+          "doi": "10.1093/cercor/bhs048"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Pars Opercularis and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_Op-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_Op-Ins_0",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "3D TIFF"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "This dataset contains the image of spatial distribution of GLUR2 subunit in AMPA receptor in human cortex using serial two photons microscopy. The individual 3D tiles acquired with the two photon fluorescence microscope were fused together to produce the final image.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Visual cortex area 17"
+        }
+      ],
+      "name": "Maps of GLUR2 receptors in human cortex",
+      "files": [
+        {
+          "byteSize": 5128795283,
+          "name": "glur2.tiff",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000772/glur2.tiff",
+          "contentType": "image/tiff"
+        }
+      ],
+      "contributors": [
+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/CQD9-F38"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Precentral and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PrC-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PrC-Ins_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PrC-Ins_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti, bundles, bundlesdata,  minf"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "Structural MRI database acquired on 77 healthy human subjects and including : 1) a series of diffusion MRI data providing isomaps of long and short individual white matter bundles as well as corresponding atlases in the Talairach/MNI spaces ; 2) a series of T1-weighted anatomical scans for each individual",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Structural mapping of the brain (ARCHI database): ARCHI_dwi",
+      "files": [],
+      "contributors": [
+        "LeBihan, Denis",
+        "Poupon, Cyril",
+        "Parker, Geoff J.M.",
+        "Knoesche, Thomas R.",
+        "Huppi, Petra S.",
+        "Dyrby, Tim B.",
+        "Cohen, Yoram",
+        "Clark, Chris A.",
+        "Behrens, Tim E.J.",
+        "Bizzi, Albero",
+        "Jones, Derek K.",
+        "Alexander, Daniel C.",
+        "Assaf, Yaniv"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "The CONNECT project: Combining macro- and micro-structure",
+          "cite": "Assaf, Y., Alexander, D. C., Jones, D. K., Bizzi, A., Behrens, T. E. J., Clark, C. A., … Poupon, C. (2013). The CONNECT project: Combining macro- and micro-structure. NeuroImage, 80, 273–282. ",
+          "doi": "10.1016/j.neuroimage.2013.05.055"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hIP1 (IPS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hIP1 (IPS). The probability map of Area hIP1 (IPS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hIP1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hIP1 (IPS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hIP1 (IPS)",
+      "files": [
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+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hIP1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hIP1.nii",
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+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Choi, Hi-Jae",
+        "Amunts, Katrin",
+        "Armstrong, E.",
+        "Fink, Gereon R.",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/VWV1-FYY"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus",
+          "cite": "Choi, H.-J., Zilles, K., Mohlberg, H., Schleicher, A., Fink, G. R., Armstrong, E., & Amunts, K. (2006). Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. The Journal of Comparative Neurology, 495(1), 53–69. ",
+          "doi": "10.1002/cne.20849"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Parietal and Inferior Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IP-IT_0"
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+      "name": "Probabilistic maps of lh_IP-IT_0",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 5Ci (SPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 5Ci (SPL). The probability map of Area 5Ci (SPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-5Ci.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
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+        {
+          "name": "Area 5Ci (SPL)"
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+        "Hoemke, L.",
+        "Schleicher, Axel",
+        "Zilles, Karl",
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+          "doi": "10.1093/cercor/bhm116"
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+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
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+      "description": "This dataset contains the distinct architectonic HATA (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to HATA (Hippocampus). The probability map of HATA (Hippocampus) is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-HATA.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
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+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Habel, U.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
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+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
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+      "description": "This dataset contains the distinct architectonic Area PFt (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PFt (IPL). The probability map of Area PFt (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PFt.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
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+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Schleicher, Axel",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Scheperjans, Filip",
+        "Caspers, S.",
+        "Geyer, Stefan"
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+          "doi": "10.1007/s00429-008-0195-z"
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+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
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+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 9 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
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+        "Zilles, Karl",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
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+          "doi": "10.1016/j.cortex.2014.07.007"
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+        "NIFTI"
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+      "custodians": [
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Superior Temporal and Transverse Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
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+      "parcellationRegion": [
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+      "custodians": [
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Pars Triangularis and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+          "name": "Allen adult mouse brain reference atlas V3"
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+      ],
+      "custodians": [
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+      "description": "These datasets report single cell transcriptomes from mouse hippocampus and cerebellum at P40 generated on the Fluidigm C1 modified running a script to implement Smart-seq2",
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+        "Belgard, Grant",
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+          "name": "MNI Colin 27"
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+      "description": "This dataset contains the distinct architectonic Area hOc4d (Cuneus) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc4d (Cuneus). The probability map of Area hOc4d (Cuneus) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc4d.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
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+        "Kujovic, Milenko",
+        "Amunts, Katrin",
+        "Eickhoff, Simon B.",
+        "Rottschy, Claudia",
+        "Mohlberg, Hartmut",
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+        "Zilles, Karl"
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+          "name": "Cytoarchitectonic mapping of the human dorsal extrastriate cortex",
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+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 77 subjects performing different tasks of a  social paradigm: 1) speech versus non-speech sounds, 2) false belief- versus mechanistic kind of inference after visual or auditory presentation, 3) interacting versus non-interacting figures in the movie clip. The tasks were executed in a (small) block design (5-7s each) with acquisition duration of  489s.\nVisual stimuli were displayed in four 250-ms epochs, separated by 100ms intervals (i.e., 1.3s in total). Auditory stimuli were drawn from a recorded male voice (i.e., a total of 1.6s for motor instructions, 1.2-1.7s for sentences, and 1.2-1.3s for subtraction). The auditory or visual stimuli were shown to the participants for passive viewing or button response in event-related paradigms. Post-scan questions verified that the experimental tasks were understood and followed correctly. \nWhole-brain EPI data were acquired with the same Siemens Trio with a 32 channel head coil (TR=2400ms, TE=30ms, flip angle=60°, in-plane FOV= 19.2 * 19.2 cm, 40 slices, 3.0mm isotropic voxels). A posterior-anterior phase encoding scheme was used for all images. Standard preprocessing was performed with SPM, including slice timing, motion correction, alignment, and spatial normalization.",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "ARCHI fMRI Database: social paradigm",
+      "files": [],
+      "contributors": [
+        "Thirion, Bertrand",
+        "Pinel, Philippe",
+        "Poupon, Cyril",
+        "Grisel, Olivier",
+        "Varoquaux, Gaël"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Principal Component Regression Predicts Functional Responses across Individuals",
+          "cite": "Thirion, B., Varoquaux, G., Grisel, O., Poupon, C., & Pinel, P. (2014). Principal Component Regression Predicts Functional Responses across Individuals. Lecture Notes in Computer Science, 741–748. ",
+          "doi": "10.1007/978-3-319-10470-6_92"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic AStr (Amygdala) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to AStr (Amygdala). The probability map of AStr (Amygdala) is given in file jubrain-pmap-v22c_space-mnicolin27_Amygdala-AStr.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "AStr (Amygdala)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of AStr (Amygdala)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Amygdala-AStr.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Amygdala-AStr.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Habel, U.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/SH4V-6VC"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "DeFelipe, Javier"
+      ],
+      "description": "This dataset contains the density of Chandelier (Ch) cell axon terminals  in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 3 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain.",
+      "licenseInfo": [],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Brodmann’s area 1"
+        },
+        {
+          "name": "Brodmann’s area 32"
+        },
+        {
+          "name": "Brodmann’s area 24"
+        },
+        {
+          "name": "Brodmann’s area 38"
+        },
+        {
+          "name": "Brodmann’s area 22"
+        },
+        {
+          "name": "Brodmann’s area 21"
+        },
+        {
+          "name": "Brodmann’s area 20"
+        },
+        {
+          "name": "Brodmann’s area 47"
+        },
+        {
+          "name": "Brodmann’s area 14"
+        },
+        {
+          "name": "Brodmann’s area 13"
+        },
+        {
+          "name": "Brodmann’s area 12"
+        },
+        {
+          "name": "Brodmann’s area 11"
+        },
+        {
+          "name": "Brodmann’s area 46"
+        },
+        {
+          "name": "Brodmann’s area 45"
+        },
+        {
+          "name": "Brodmann’s area 10"
+        },
+        {
+          "name": "Brodmann’s area 9"
+        },
+        {
+          "name": "Brodmann’s area 4"
+        },
+        {
+          "name": "Brodmann’s area 18"
+        },
+        {
+          "name": "Brodmann’s area 17"
+        },
+        {
+          "name": "Brodmann’s area 3b"
+        }
+      ],
+      "name": "Distribution and density of Chandelier cell axon terminals in the human cerebral cortex",
+      "files": [],
+      "contributors": [
+        "Inda, MC",
+        "Muñoz, A",
+        "DeFelipe, Javier"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "The Distribution of Chandelier Cell Axon Terminals that Express the GABA Plasma Membrane Transporter GAT-1 in the Human Neocortex",
+          "cite": "Inda, M., DeFelipe, J., & Muñoz, A. (2006). The Distribution of Chandelier Cell Axon Terminals that Express the GABA Plasma Membrane Transporter GAT-1 in the Human Neocortex. Cerebral Cortex, 17(9), 2060–2071. ",
+          "doi": "10.1093/cercor/bhl114"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoC-PrC_3"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoC-PrC_3",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_PoC-PrC_3.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoC-PrC_3.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Rostral Anterior Cingulate and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_RAC-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_RAC-SF_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_RAC-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_RAC-SF_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoC-PrC_1"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoC-PrC_1",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_PoC-PrC_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoC-PrC_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "A general procedure to fit individual synaptic events recorded from voltage clamp experiments was tested with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. The data used here are obtained from coronal hippocampal slices (300 μm thick) from juvenile (P21-30) C57B16/J male mice receiving input from parvalbumin positive (PV+) interneurons. The dataset contains 614 individual events recorded from 3 different neurons (expC1-C3). The first column of each txt file contains the time in ms and the other columns contain spontaneous inhibitory synaptic currents (sIPSCs) in pA.\nComputational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In [1] we proposed a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. A Python version is available for public use, as a Jupyter Notebook in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.\nFitting experimental data against a number of different models is a common way to do this (reviewed in [2]), and can help in the subsequent interpretation of the data. In general, experimental traces are fitted using specific approaches for specific purposes (e.g., [3], [4]). However, to the best of our knowledge, there is no easy, user-friendly, general procedure available for this purpose, especially in computational neuroscience. Our aim was thus to identify the appropriate conceptual structure of a procedure to obtain good, reliable fits of raw experimental traces of spontaneous synaptic events. This is an important step because spontaneous synaptic events have been so far exclusively analyzed using traces obtained by averaging many events. However, as can be easily imagined, each synapse in any given neuron has its own, independent, history of activation. The most likely physiological consequence is that the variables relative to the subcellular processes underlying synaptic transmission are different for each synapse. If a researcher is interested in testing a specific kinetic scheme implemented for specific biochemical pathways, the use of individual events is the most appropriate choice, since this approach would give information on the different combinations of model parameters that are consistent with the observed events. Averaging traces will lose a lot of relevant information.\nWe therefore present here the implementation of a procedure leading to the development of a unifying optimization method for individual synaptic events. Experimental data, kinetic models of synaptic transmission, and fitting parameters and their dependencies can be user defined/provided or gathered from databases. They can be used to generate optimized groups of parameters able to represent a population of synapses, either for simulation purposes or to study the functional consequences of a particular protein or subcellular synaptic transmission pathway. \n[1] Lupascu CA, Morabito A, Merenda E, et al. A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory Synapses. Frontiers in Neuroinformatics. 2016;10:23. doi:10.3389/fninf.2016.00023   \n[2] Van Geit W., De Schutter E., Achard P. (2008). Automated neuron model optimization techniques: a review. Biol. Cybern. 99, 241–251. 10.1007/s00422-008-0257-6  \n[3] Bekkers J. M. (2003). Convolution of mini distributions for fitting evoked synaptic amplitude histograms. J. Neurosci. Methods. 130, 105–114. 10.1016/j.jneumeth.2003.09.018  \n[4] Meisl G., Kirkegaard J. B., Arosio P., Michaels T. C., Vendruscolo M., Dobson C. M., et al. . (2016). Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nat. Protoc. 11, 252–272. 10.1038/nprot.2016.010",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "sIPSCs from juvenile (P21-30) C57Bl6/J male mice from CA1 pyramidal neurons receiving input from PV+ interneurons",
+      "files": [],
+      "contributors": [
+        "Marchetti, Cristina",
+        "Marinelli, Silvia",
+        "Cherubini, Enrico"
+      ],
+      "kgReference": [
+        "10.25493/37YC-C88"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "png"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Evans, Alan C.",
+        "Amunts, Katrin"
+      ],
+      "description": "BigBrain is a ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. This dataset contains the reconstituted sections in the axial dimension. In addition, the final aligned sections in the coronal and sagittal dimension, and 3D histological volumes in histological space are also available.\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Coronal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/1f50424206be7a0c1b8379bf8817be5e)\"\n- Dataset \"[BigBrain High-resolution whole brain model: 2D Sections/Sagittal](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/9e36fc587ce8193795137371a85a351e)\"\n- Dataset \"[BigBrain High-resolution whole brain model: Downscaled volumes in minc and NIfTI format](https://www.humanbrainproject.eu/en/explore-the-brain/search/#Dataset/5cd7a689c75124a7455e1c31c9f478e0)\"",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "BigBrain High-resolution whole brain model: 2D Sections/Axial",
+      "files": [],
+      "contributors": [
+        "Amunts, Katrin",
+        "Evans, Alan C.",
+        "Zilles, Karl",
+        "Lippert, T.",
+        "Shah, Nadim J.",
+        "Oros-Peusquens, Ana-Maria",
+        "Lewis, Lindsay B.",
+        "Bazin, P.-L.",
+        "Bludau, Sebastian",
+        "Rousseau, M.-E.",
+        "Dickscheid, Timo",
+        "Mohlberg, Hartmut",
+        "Borgeat, L.",
+        "Lepage, Claude"
+      ],
+      "kgReference": [
+        "10.1126/science.1235381"
+      ],
+      "publications": [
+        {
+          "name": "BigBrain: An Ultrahigh-Resolution 3D Human Brain Model",
+          "cite": "Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-E., … Evans, A. C. (2013). BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science, 340(6139), 1472–1475. ",
+          "doi": "10.1126/science.1235381"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Genon, Sarah"
+      ],
+      "description": "Five multimodal clusters were defined by examining convergence across parcellations based on task-based functional connectivity, resting-state functional connectivity and anatomical connectivity.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Caudal subregion of left dorsal premotor cortex"
+        },
+        {
+          "name": "Rostral middle subregion of left dorsal premotor cortex"
+        },
+        {
+          "name": "Rostral inferior subregion of left dorsal premotor cortex"
+        },
+        {
+          "name": "Intermediate ventral subregion of left dorsal premotor cortex"
+        },
+        {
+          "name": "Central subregion of left dorsal premotor cortex"
+        }
+      ],
+      "name": "Left PMd subregions defined across CBP modalities",
+      "files": [
+        {
+          "byteSize": 2023,
+          "name": "Conj_IntermediateVentral_yellow.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000773/Left PMdParcels/Conj_IntermediateVentral_yellow.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 2097,
+          "name": "Conj_RostralMiddle_red.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000773/Left PMdParcels/Conj_RostralMiddle_red.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 2068,
+          "name": "Conj_RostralInferior_Pink.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000773/Left PMdParcels/Conj_RostralInferior_Pink.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 2075,
+          "name": "Conj_Central_blue.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000773/Left PMdParcels/Conj_Central_blue.nii.gz",
+          "contentType": "application/octet-stream"
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+        {
+          "byteSize": 2149,
+          "name": "Conj_Caudal_green.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000773/Left PMdParcels/Conj_Caudal_green.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Genon, Sarah",
+        "Eickhoff, Simon B.",
+        "Amunts, Katrin",
+        "Jiang, Tianzi",
+        "Fox, P.T.",
+        "Laird, A.R.",
+        "Grefkes, Christian",
+        "Langner, Robert",
+        "Hoffstaedter, Felix",
+        "Cieslik, Edna C.",
+        "Müller, Veronika I.",
+        "Fan, Lingzhong",
+        "Li, Hai",
+        "Reid, Andrew T."
+      ],
+      "kgReference": [
+        "10.25493/C21W-ADY"
+      ],
+      "publications": [
+        {
+          "name": "The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization",
+          "cite": "Genon, S., Reid, A., Li, H., Fan, L., Müller, V. I., Cieslik, E. C., … Eickhoff, S. B. (2018). The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. NeuroImage, 170, 400–411. ",
+          "doi": "10.1016/j.neuroimage.2017.02.034"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic CA1 (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to CA1 (Hippocampus). The probability map of CA1 (Hippocampus) is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "CA1 (Hippocampus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of CA1 (Hippocampus)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA1.nii",
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Habel, U.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/W4WK-QSK"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area 47 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, pr, ar\n\nkainate (glutamate; [³H]kainate): fp, pr, ar\n\nNMDA (glutamate; [³H]MK-801): fp, pr, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): pr, ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, pr, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, pr, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, pr, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, pr, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, pr, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, pr, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, pr, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, pr, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, pr, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, pr, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, pr, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, pr, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 47"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area 47",
+      "files": [
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+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
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+        {
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+          "name": "47_pr_examples.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_47/47_pr_examples.zip",
+          "contentType": "application/zip"
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_47/47_fp_20171202.xlsx",
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+        {
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+          "name": "47_ar_examples.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Area_47/47_ar_examples.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/4M1R-KCP"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area FG4 (FusG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area FG4 (FusG). The probability map of Area FG4 (FusG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-FG4.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area FG4 (FusG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area FG4 (FusG)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-FG4.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-FG4.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Grill-Spector, Kalanit",
+        "Bludau, Sebastian",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Caspers, Julian",
+        "Weiner, Kevin S.",
+        "Lorenz, Simon"
+      ],
+      "kgReference": [
+        "10.25493/6A73-MZS"
+      ],
+      "publications": [
+        {
+          "name": "Two New Cytoarchitectonic Areas on the Human Mid-Fusiform Gyrus",
+          "cite": "Lorenz, S., Weiner, K. S., Caspers, J., Mohlberg, H., Schleicher, A., Bludau, S., … Amunts, K. (2015). Two New Cytoarchitectonic Areas on the Human Mid-Fusiform Gyrus. Cerebral Cortex, bhv225. ",
+          "doi": "10.1093/cercor/bhv225"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area opercularis 8 (OP8) in the frontal operculum. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area OP8. The probability map of Area OP8 is given in file jubrain-pmap-v22c_space-mnicolin27_Area-OP8.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area OP8"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area OP8",
+      "files": [],
+      "contributors": [
+        "Saal, Martin",
+        "Amunts, Katrin",
+        "Caspers, Svenja",
+        "Mohlberg, Hartmut",
+        "Bludau, Sebastian"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Caudal Middle Frontal and Pars Opercularis gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_CMF-Op_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_CMF-Op_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "lh_CMF-Op_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_CMF-Op_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "pts, nii, gii, eeg, csv, pos, xlsx, docx"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Lachaux, Jean-Philippe"
+      ],
+      "description": "The Human Intracranial Database (HID) is a collection of stereotactic electroencephalography (sEEG) data in 30 patients, each performing eight behavioral tasks. The behavioral tasks were used as functional localizers: short and classic task paradigms designed to activate large-scale neural networks involved in language processing (LEC1 and LEC2), verbal and visuo-spatial working memory (MVEB and MVIS), visual attention (VISU), motor behavior (MOTO), high-level visual (MCSE) and auditory perception (AUDI). Furthermore, patients were also recorded during resting state (REST). The sEEG data were obtained via several sEEG depth-electrodes (linear arrays with up to 20 contacts) that were implanted in each patient in a stereotactic surgery. Coordinates of the sEEG electrode contacts are provided in the individual brain space of each patient as well as in a reference brain space (MNI). Semantically sEEG electrode contacts were linked to brain areas of the MarsAtlas.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Human Intracranial Database (HID)",
+      "files": [
+        {
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+          "name": "intracranial.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Intracranial/intracranial.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Jerbi, K.",
+        "Lachaux, Jean-Philippe",
+        "Berthoz, A.",
+        "Kahane, P.",
+        "Minotti, L.",
+        "Bertrand, O.",
+        "Ossandon, T.",
+        "Hamame, C. M.",
+        "Freyermuth, S.",
+        "Vidal, J. R."
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Long-Distance Amplitude Correlations in the High Gamma Band Reveal Segregation and Integration within the Reading Network",
+          "cite": "Vidal, J. R., Freyermuth, S., Jerbi, K., Hamame, C. M., Ossandon, T., Bertrand, O., … Lachaux, J.-P. (2012). Long-Distance Amplitude Correlations in the High Gamma Band Reveal Segregation and Integration within the Reading Network. Journal of Neuroscience, 32(19), 6421–6434. ",
+          "doi": "10.1523/JNEUROSCI.4363-11.2012"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic LB (Amygdala) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to LB (Amygdala). The probability map of LB (Amygdala) is given in file jubrain-pmap-v22c_space-mnicolin27_Amygdala-LB.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "LB (Amygdala)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of LB (Amygdala)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Amygdala-LB.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Amygdala-LB.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/E7QC-B3Y"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Posterior Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoCi-PrCu_1"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoCi-PrCu_1",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "name": "lh_PoCi-PrCu_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoCi-PrCu_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Superior Parietal and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_SP-SM_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_SP-SM_0",
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+        {
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+          "name": "lh_SP-SM_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_SP-SM_0.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the adult, neuroligin-3 knock-in mice, positive pairing condition.  \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from adult neuroligin-3 knock-in mice, positive pairing",
+      "files": [],
+      "contributors": [
+        "Cherubini, Enrico",
+        "Marchetti, Cristina",
+        "Sgritta, Martina"
+      ],
+      "kgReference": [
+        "10.25493/J43Z-KH0"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "Sylvestri, Ludovico"
+      ],
+      "description": "Whole-brain images of neuronal activation in mouse brain acquired with light-sheet microscopy. Animal models will be used to detect immediate early genes (IEGs) expression.",
+      "licenseInfo": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "hbp-00931_RestImg_Dataset",
+      "files": [],
+      "contributors": [
+        "Franceschini,Alessandra",
+        "Di Giovanna,Antonino P.",
+        "Pavone, Francesco S.",
+        "Orsini, Francesco",
+        "Costantini, Irene",
+        "Mazzamuto,Giacomo",
+        "Silvestri, Ludovico",
+        "Muellenbroich,Marie C."
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in the Anterior Thalamic nucleus using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Anterior Thalamic nucleus"
+        }
+      ],
+      "name": "Density measurements of different receptors for Anterior Thalamic nucleus",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Nucleus_ATh/ATh_fp_20180405.xlsx",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/KKTT-1TK"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 2 (PostCS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 2 (PostCS). The probability map of Area 2 (PostCS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 2 (PostCS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 2 (PostCS)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Grefkes, Christian",
+        "Zilles, Karl",
+        "Roland, Per E.",
+        "Schormann, Thorsten",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/JZP0-Q97"
+      ],
+      "publications": [
+        {
+          "name": "Human Somatosensory Area 2: Observer-Independent Cytoarchitectonic Mapping, Interindividual Variability, and Population Map",
+          "cite": "Grefkes, C., Geyer, S., Schormann, T., Roland, P., & Zilles, K. (2001). Human Somatosensory Area 2: Observer-Independent Cytoarchitectonic Mapping, Interindividual Variability, and Population Map. NeuroImage, 14(3), 617–631. ",
+          "doi": "10.1006/nimg.2001.0858"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic CM (Amygdala) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to CM (Amygdala). The probability map of CM (Amygdala) is given in file jubrain-pmap-v22c_space-mnicolin27_Amygdala-CM.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "CM (Amygdala)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of CM (Amygdala)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Amygdala-CM.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Amygdala-CM.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/X0CV-G7F"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area s32 (sACC) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area s32 (sACC). The probability map of Area s32 (sACC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-s32.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area s32 (sACC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area s32 (sACC)",
+      "files": [
+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Vogt, Brent A.",
+        "Schleicher, Axel",
+        "Hoffstaedter, Felix",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/3PBV-WH0"
+      ],
+      "publications": [
+        {
+          "name": "Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity",
+          "cite": "Palomero-Gallagher, N., Eickhoff, S. B., Hoffstaedter, F., Schleicher, A., Mohlberg, H., Vogt, B. A., … Zilles, K. (2015). Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity. NeuroImage, 115, 177–190. ",
+          "doi": "10.1016/j.neuroimage.2015.04.053"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "D'Angelo, Egidio"
+      ],
+      "description": "Study and characterization of the synaptic properties of different cerebellar neuronal types.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Recordings of excitatory postsynaptic currents from cerebellar neurons",
+      "files": [],
+      "contributors": [
+        "Locatelli, Francesca",
+        "Prestori, Francesca",
+        "Tognolina Marialuisa"
+      ],
+      "kgReference": [
+        "10.25493/F2VK-MB4"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoC-PrC_2"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PoC-PrC_2",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_PoC-PrC_2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PoC-PrC_2.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Mohlberg, Hartmut",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B.",
+        "Geyer, Stefan"
+      ],
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+        "Poupon, Cyril"
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+      "licenseInfo": [
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Caudal Middle Frontal and Postcentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+        {
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 25 (sACC) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 25 (sACC). The probability map of Area 25 (sACC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-25.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 25 (sACC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 25 (sACC)",
+      "files": [
+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-25.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Vogt, Brent A.",
+        "Schleicher, Axel",
+        "Hoffstaedter, Felix",
+        "Eickhoff, Simon B.",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/NMPJ-EU"
+      ],
+      "publications": [
+        {
+          "name": "Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity",
+          "cite": "Palomero-Gallagher, N., Eickhoff, S. B., Hoffstaedter, F., Schleicher, A., Mohlberg, H., Vogt, B. A., … Zilles, K. (2015). Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity. NeuroImage, 115, 177–190. ",
+          "doi": "10.1016/j.neuroimage.2015.04.053"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Marinelli, Silvia",
+        "Cherubini, Enrico"
+      ],
+      "description": "This dataset is temporarily under embargo. The data will become available for download after the embargo period",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Test doi: Modulation of GABAergic currents by anti-neuroligin 2 and anti-gephyrin intrabodies (scFvgephIsc cultures)",
+      "files": [],
+      "contributors": [
+        "Stendardo, Emiliana",
+        "Avvisati, Riccardo",
+        "Meringolo, Maria",
+        "Cherubini, Enrico",
+        "Malavasi, Elisa",
+        "Badiani, Aldo",
+        "Marinelli, Silvia"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Intravenous self-administration of benzydamine, a non-steroidal anti-inflammatory drug with a central cannabinoidergic mechanism of action",
+          "cite": "Avvisati, R., Meringolo, M., Stendardo, E., Malavasi, E., Marinelli, S., & Badiani, A. (2017). Intravenous self-administration of benzydamine, a non-steroidal anti-inflammatory drug with a central cannabinoidergic mechanism of action. Addiction Biology, 23(2), 610–619. ",
+          "doi": "10.1111/adb.12516"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "A general procedure to fit individual synaptic events recorded from voltage clamp experiments was tested with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. The data used here are obtained from coronal hippocampal slices (300 μm thick) from juvenile (P21-30) C57B16/J male mice and recorded from hippocampal CA1 pyramidal neurons receiving input from parvalbumin positive (PV+) and cholecystokinin positive (CCK+) interneurons. The dataset contains 2130 individual events recorded from 3 different neurons (expD1-D3). The first column of each txt file contains the time in ms and the other columns contain spontaneous inhibitory synaptic currents (sIPSCs) in pA.\nComputational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In [1] we proposed a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. A Python version is available for public use, as a Jupyter Notebook in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.\nFitting experimental data against a number of different models is a common way to do this (reviewed in [2]), and can help in the subsequent interpretation of the data. In general, experimental traces are fitted using specific approaches for specific purposes (e.g., [3], [4]). However, to the best of our knowledge, there is no easy, user-friendly, general procedure available for this purpose, especially in computational neuroscience. Our aim was thus to identify the appropriate conceptual structure of a procedure to obtain good, reliable fits of raw experimental traces of spontaneous synaptic events. This is an important step because spontaneous synaptic events have been so far exclusively analyzed using traces obtained by averaging many events. However, as can be easily imagined, each synapse in any given neuron has its own, independent, history of activation. The most likely physiological consequence is that the variables relative to the subcellular processes underlying synaptic transmission are different for each synapse. If a researcher is interested in testing a specific kinetic scheme implemented for specific biochemical pathways, the use of individual events is the most appropriate choice, since this approach would give information on the different combinations of model parameters that are consistent with the observed events. Averaging traces will lose a lot of relevant information.\nWe therefore present here the implementation of a procedure leading to the development of a unifying optimization method for individual synaptic events. Experimental data, kinetic models of synaptic transmission, and fitting parameters and their dependencies can be user defined/provided or gathered from databases. They can be used to generate optimized groups of parameters able to represent a population of synapses, either for simulation purposes or to study the functional consequences of a particular protein or subcellular synaptic transmission pathway. \n[1] Lupascu CA, Morabito A, Merenda E, et al. A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory Synapses. Frontiers in Neuroinformatics. 2016;10:23. doi:10.3389/fninf.2016.00023.  \n[2] Van Geit W., De Schutter E., Achard P. (2008). Automated neuron model optimization techniques: a review. Biol. Cybern. 99, 241–251. 10.1007/s00422-008-0257-6  \n[3] Bekkers J. M. (2003). Convolution of mini distributions for fitting evoked synaptic amplitude histograms. J. Neurosci. Methods. 130, 105–114. 10.1016/j.jneumeth.2003.09.018  \n[4] Meisl G., Kirkegaard J. B., Arosio P., Michaels T. C., Vendruscolo M., Dobson C. M., et al. . (2016). Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nat. Protoc. 11, 252–272. 10.1038/nprot.2016.010",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "sIPSCs from juvenile (P21-30) C57B16/J male mice from hippocampal CA1 pyramidal neurons receiving input from PV+ and CCK+ interneurons",
+      "files": [],
+      "contributors": [
+        "Marinelli, Silvia",
+        "Marchetti, Cristina",
+        "Cherubini, Enrico"
+      ],
+      "kgReference": [
+        "10.25493/PHA7-5KZ"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 7M (SPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 7M (SPL). The probability map of Area 7M (SPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-7M.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 7M (SPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 7M (SPL)",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Hermann, K.",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B.",
+        "Hoemke, L.",
+        "Amunts, Katrin",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/F26Z-16P"
+      ],
+      "publications": [
+        {
+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        },
+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area ifs2 (IFS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifs2 (IFS). The probability map of  Area ifs2 (IFS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifs2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifs2 (IFS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifs2 (IFS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Anterior Cingulate gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CAC-PoCi_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CAC-PoCi_0",
+      "files": [
+        {
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+          "name": "rh_CAC-PoCi_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_CAC-PoCi_0.nii",
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup> KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the neonate, NLR<sup>451C</sup> KI, positive pairing condition. \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
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+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from neonate neuroligin-3 knock-in mice, positive pairing",
+      "files": [],
+      "contributors": [
+        "Sgritta, Martina",
+        "Marchetti, Cristina",
+        "Cherubini, Enrico"
+      ],
+      "kgReference": [
+        "10.25493/PF1P-YSE"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area Ig1 (Insula) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area Ig1 (Insula). The probability map of Area Ig1 (Insula) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-Ig1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Ig1 (Insula)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area Ig1 (Insula)",
+      "files": [
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-Ig1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Kurth, F.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Hoemke, L.",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/H2H6-0SA"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex",
+          "cite": "Kurth, F., Eickhoff, S. B., Schleicher, A., Hoemke, L., Zilles, K., & Amunts, K. (2009). Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex. Cerebral Cortex, 20(6), 1448–1461. ",
+          "doi": "10.1093/cercor/bhp208"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Lateral Orbitofrontal and Rostral Middle Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_LOF-RMF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_LOF-RMF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_LOF-RMF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_LOF-RMF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Postcetnral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoC-SM_1"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoC-SM_1",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "lh_PoC-SM_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoC-SM_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Middle Frontal and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CMF-PrC_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CMF-PrC_1",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_CMF-PrC_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_CMF-PrC_1.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in the Putamen using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Putamen"
+        }
+      ],
+      "name": "Density measurements of different receptors for Putamen ",
+      "files": [
+        {
+          "byteSize": 44096,
+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        },
+        {
+          "byteSize": 16271,
+          "name": "Pu_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Nucleus_Pu/Pu_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        }
+      ],
+      "contributors": [
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/4GZ1-SHH"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in the  Mediodorsal Thalamic nucleus using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Mediodorsal Thalamic nucleus"
+        }
+      ],
+      "name": "Density measurements of different receptors for Mediodorsal Thalamic nucleus",
+      "files": [
+        {
+          "byteSize": 15539,
+          "name": "MD_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Nucleus_MD/MD_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        },
+        {
+          "byteSize": 44096,
+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
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+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Isthmus Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IC-PrCu_0"
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+      "name": "Probabilistic maps of lh_IC-PrCu_0",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_IC-PrCu_0.nii",
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 44 (IFG) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 44 (IFG). The probability map of Area 44 (IFG) is given in file jubrain-pmap-v22c_space-mnicolin27_ Area-44.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 44 (IFG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 44 (IFG)",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-44.nii",
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+        },
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Fink, Gereon R.",
+        "Uylings, Harry B.M.",
+        "Shah, Nadim J.",
+        "Schleicher, Axel",
+        "Zilles, Karl",
+        "Gurd, Jennifer M.",
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Bürgel, Uli",
+        "Eickhoff, Simon B.",
+        "Marshall, John C.",
+        "Pieperhoff, P.",
+        "Weiss, Peter H."
+      ],
+      "kgReference": [
+        "10.25493/7T39-YKE"
+      ],
+      "publications": [
+        {
+          "name": "Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined stereotaxic space—The roles of Brodmann areas 44 and 45",
+          "cite": "Amunts, K., Weiss, P. H., Mohlberg, H., Pieperhoff, P., Eickhoff, S., Gurd, J. M., … Zilles, K. (2004). Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined stereotaxic space—The roles of Brodmann areas 44 and 45. NeuroImage, 22(1), 42–56. ",
+          "doi": "10.1016/j.neuroimage.2003.12.031"
+        },
+        {
+          "name": "Broca's region revisited: Cytoarchitecture and intersubject variability",
+          "cite": "Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H. B. M., & Zilles, K. (1999). Broca’s region revisited: Cytoarchitecture and intersubject variability. The Journal of Comparative Neurology, 412(2), 319–341. ",
+          "doi": "10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO;2-7"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area FG1 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp, ar\n\nkainate (glutamate; [³H]kainate): fp, ar\n\nNMDA (glutamate; [³H]MK-801): fp, ar\n\nmGluR2/3 (glutamate; [³H] LY 341 495): ar\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp, ar\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp, ar\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp, ar\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp, ar\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp, ar\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp, ar\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp, ar\n\nα₁ (noradrenalin; [³H]prazosin): fp, ar\n\nα₂ (noradrenalin; [³H]UK-14,304): fp, ar\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp, ar\n\n5-HT₂ (serotonin; [³H]ketanserin): fp, ar\n\nD₁ (dopamine; [³H]SCH23390): fp, ar\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area FG1"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area FG1",
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+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
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+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Caspers, Julian",
+        "Friederici, Angela D.",
+        "Amunts, Katrin",
+        "Palomero-Gallagher, Nicola",
+        "Bacha-Trams, Maraike"
+      ],
+      "kgReference": [
+        "10.25493/QN6K-CHN"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        },
+        {
+          "name": "Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus",
+          "cite": "Caspers, J., Palomero-Gallagher, N., Caspers, S., Schleicher, A., Amunts, K., & Zilles, K. (2013). Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus. Brain Structure and Function, 220(1), 205–219. ",
+          "doi": "10.1007/s00429-013-0646-z"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "Localization of activated cells (expressing immedate early gene Arc) in half mouse brain, normal caging.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Arc expression in resting state",
+      "files": [],
+      "contributors": [
+        "Silvestri, Ludovico",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/MDRW-SWQ"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Clascá, Francisco"
+      ],
+      "description": "This dataset describes quantitatively the somatodendritic and axonal structure of individual thalamocortical projection neurons in the thalamic somatic and visual sensory nuclei. The cells in the dataset were labeled using state-of the art viral vectors able to drive massive levels of expression of marker proteins in a neuron after a single transfection event, leaving the rest of the brain unlabeled. This allows the unambiguous visualization /measurement of the entire axonal tree, no matter how large or complex. They are the first visualization of these cells ever reported in mice. Experiments were conducted in vivo in wildtype C57BL76 adult animals and have a low (<5%) success rate. Accurate labeling analysis is extremely labor-intensive, especially for large and widespread-axon cells. The present dataset may thus be viewed as a collection of “archetypal” reference specimens of these neuron populations. The accurate 3D data and measurement of axonal arborizations of thalamocortical neurons provide in this dataset will be re-usable by any scientist developing biologically-based models of local and long range brain circuitry.  \n**Embargo status:**\n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "3D reconstruction and measurement of individual thalamocortical projection neuron axons of somatosensory and visual thalamic nuclei",
+      "files": [],
+      "contributors": [
+        "Clascá, Francisco",
+        "Evangelio, Marian",
+        "Rubio, Mario",
+        "García-Amado, María",
+        "Porrero, César"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Subc (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Subc (Hippocampus). The probability map of Subc (Hippocampus) is given in file  jubrain-pmap-v22c_space-mnicolin27_Hippocampus-Subc.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Subc (Hippocampus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Subc (Hippocampus)",
+      "files": [
+        {
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Hippocampus-Subc.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Hippocampus-Subc.nii",
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/57EJ-01Z"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Rostral Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_RMF-SF_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_RMF-SF_1",
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+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_RMF-SF_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
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+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
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+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Modulation of GABAergic currents by anti-neuroligin 2 and anti-gephyrin intrabodies (scFvNLG2-645)",
+      "files": [],
+      "contributors": [
+        "Cherubini, Enrico",
+        "Cattaneo, Antonino",
+        "Meli, Giovanni",
+        "Giustizieri, Michela",
+        "Morabito, Annunziato",
+        "Marinelli, Silvia"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area ifs3 (IFS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifs3 (IFS). The probability map of  Area ifs3 (IFS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifs3.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifs3 (IFS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifs3 (IFS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Postcentral and Supramarginal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoC-SM_0"
+        }
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+      "name": "Probabilistic maps of rh_PoC-SM_0",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI",
+        "3D-TIFF"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area ifj2 (IFS/PreS) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area ifj2 (IFS/PreS). The probability map of  Area ifj2 (IFS/PreS) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-ifj2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area ifj2  (IFS/PreS)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area ifj2  (IFS/PreS)",
+      "files": [],
+      "contributors": [
+        "Bradler, Sabine",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Modulation of GABAergic currents by anti-neuroligin 2 and anti-gephyrin intrabodies (scFvgephIsc)",
+      "files": [],
+      "contributors": [
+        "Giustizieri, Michela",
+        "Cattaneo, Antonino",
+        "Morabito, Annunziato",
+        "Cherubini, Enrico",
+        "Meli, Giovanni",
+        "Marinelli, Silvia"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_CMF-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_CMF-SF_0",
+      "files": [
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+          "name": "rh_CMF-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_CMF-SF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area TE 1.1 (HESCHL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area TE 1.1 (HESCHL). The probability map of Area TE 1.1 (HESCHL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-TE-11.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area TE 1.1 (HESCHL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-TE-11.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-TE-11.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Schleicher, Axel",
+        "Rademacher, Jörg",
+        "Werner, C.",
+        "Schormann, Thorsten",
+        "Morosan, Patricia",
+        "Freund, H.-J.",
+        "Amunts, Katrin",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/55TY-YS8"
+      ],
+      "publications": [
+        {
+          "name": "Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex",
+          "cite": "Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.-J., & Zilles, K. (2001). Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex. NeuroImage, 13(4), 669–683. ",
+          "doi": "10.1006/nimg.2000.0714"
+        },
+        {
+          "name": "Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System",
+          "cite": "Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System. NeuroImage, 13(4), 684–701. ",
+          "doi": "10.1006/nimg.2000.0715"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area PF (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PF (IPL). The probability map of Area PF (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PF.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PF (IPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area PF (IPL)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-PF.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-PF.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Amunts, Katrin",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B.",
+        "Caspers, S.",
+        "Schleicher, Axel",
+        "Zilles, Karl",
+        "Geyer, Stefan",
+        "Mohlberg, Hartmut"
+      ],
+      "kgReference": [
+        "10.25493/H0GN-SA8"
+      ],
+      "publications": [
+        {
+          "name": "The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability",
+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
+          "doi": "10.1016/j.neuroimage.2006.06.054"
+        },
+        {
+          "name": "The human inferior parietal lobule in stereotaxic space",
+          "cite": "Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481–495. ",
+          "doi": "10.1007/s00429-008-0195-z"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti, Csv, Txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 77 subjects performing different tasks of a generic localizer paradigm: 1) left versus right hand button press, 2) horizontal versus vertical checkerboard, 3) auditory versus visual instructions, 4) computation versus simple reading, 5) motor tasks versus language and 6) math tasks. \nThe tasks were executed in a fast-event related design with acquisition duration of  307s.\nVisual stimuli were displayed in four 250-ms epochs, separated by 100ms intervals (i.e., 1.3s in total). Auditory stimuli were drawn from a recorded male voice (i.e., a total of 1.6s for motor instructions, 1.2-1.7s for sentences, and 1.2-1.3s for subtraction). The auditory or visual stimuli were shown to the participants for passive viewing or button response in event-related paradigms. Post-scan questions verified that the experimental tasks were understood and followed correctly. \nWhole-brain EPI data were acquired with the same Siemens Trio with a 32 channel head coil (TR=2400ms, TE=30ms, flip angle=60°, in-plane FOV= 19.2 * 19.2 cm, 40 slices, 3.0mm isotropic voxels). A posterior-anterior phase encoding scheme was used for all images. Standard preprocessing was performed with SPM, including slice timing, motion correction, alignment, and spatial normalization.",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "ARCHI fMRI Database: generic localizer paradigm",
+      "files": [],
+      "contributors": [
+        "Thirion, Bertrand",
+        "Pinel, Philippe",
+        "Poupon, Cyril",
+        "Grisel, Olivier",
+        "Varoquaux, Gaël"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Principal Component Regression Predicts Functional Responses across Individuals",
+          "cite": "Thirion, B., Varoquaux, G., Grisel, O., Poupon, C., & Pinel, P. (2014). Principal Component Regression Predicts Functional Responses across Individuals. Lecture Notes in Computer Science, 741–748. ",
+          "doi": "10.1007/978-3-319-10470-6_92"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Posterios Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoCi-PrCu_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PoCi-PrCu_1",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_PoCi-PrCu_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PoCi-PrCu_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PoC-PrC_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PoC-PrC_1",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_PoC-PrC_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PoC-PrC_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD).  \nAt the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI  mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the neonate, NL3 knock-out, positive pairing condition. \n\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spike time dependent plasticity (STDP) data from neonate neuroligin-3 knock-out mice, positive pairing",
+      "files": [],
+      "contributors": [
+        "Cherubini, Enrico",
+        "Sgritta, Martina",
+        "Marchetti, Cristina"
+      ],
+      "kgReference": [
+        "10.25493/BT52-9CT"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [],
+      "description": "",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Whole-cell recording and labelling of hippocampal neurons in vivo",
+      "files": [],
+      "contributors": [
+        "Freund, Tamas",
+        "Kali, Szabolcs",
+        "Varga, Viktor",
+        "Jelitai, Marta",
+        "Komlosi, Ferenc"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Jankowsky, Joanna L."
+      ],
+      "description": "Bright-field microscopy images of serial horizontal brain sections showing Pituitary homeobox 3 (Pitx3) promoter expression in a bigenic Pitx3-tetracycline-transactivator (tTA) mouse brain case (6513, adult male), using a driver-reporter construct in which the Pitx3-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data show that the Pitx3-tTA promoter is spatially restricted to the substantia nigra, ventral tegmental area, and a few other regions.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Pituitary homeobox 3 tetracycline-transactivator expression: horizontal sections (case 6513)",
+      "files": [],
+      "contributors": [
+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Yetman, Michael J.",
+        "Checinska, Martyna M.",
+        "Puchades, Maja A.",
+        "Jankowsky, Joanna L.",
+        "Leergaard, Trygve B."
+      ],
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+        "10.25493/1AH7-T1A"
+      ],
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+      "custodians": [
+        "Jankowsky, Joanna L."
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+      "description": "Bright-field microscopy images of serial coronal brain sections showing Purkinje cell protein 2 (Pcp2) promoter expression in a bigenic Pcp2-tetracycline-transactivator (tTA) mouse brain (case 1261, adult male), generated using a driver-reporter construct in which the Pcp2-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data confirm earlier reports that Pcp2-tTA promoter expression is restricted to cerebellar Purkinje cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [],
+      "name": "Purkinje cell protein 2 tetracycline-transactivator expression: coronal sections (case 1261)",
+      "files": [],
+      "contributors": [
+        "Lillehaug, Sveinung",
+        "Puchades, Maja A.",
+        "Checinska, Martyna M.",
+        "Bjaalie, Jan G.",
+        "Jankowsky, Joanna L.",
+        "Yetman, Michael J.",
+        "Leergaard, Trygve B."
+      ],
+      "kgReference": [
+        "10.25493/A2EG-VPR"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in Area Te1 using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nmGluR2/3 (glutamate; [³H] LY 341 495): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387\n\n\nThe receptor density data of this area were also published in:\n\nMorosan P, Rademacher J, Palomero-Gallagher N, and Zilles K (2005) Anatomical organization of the human auditory cortex: Cytoarchitecture and transmitter receptors. In Heil P, Scheich H, Bundinger E, and König R (Eds), The Auditory Cortex - Towards a Synthesis of Human and Animal Research. (pp. 27-50) [Mahwah, New Jersey, USA: Lawrence Erlbaum Associates](https://www.taylorfrancis.com/books/e/9781135613365/chapters/10.4324%2F9781410613066-9)",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Te1"
+        }
+      ],
+      "name": "Density measurements of different receptors for Area Te1",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
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+        }
+      ],
+      "contributors": [
+        "Morosan, Patricia",
+        "Amunts, Katrin",
+        "Bacha-Trams, Maraike",
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola",
+        "Rademacher, Jörg",
+        "Friederici, Angela D."
+      ],
+      "kgReference": [
+        "10.25493/AHX0-9PU"
+      ],
+      "publications": [
+        {
+          "name": "Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints",
+          "cite": "Zilles, K., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Friederici, A. D. (2015). Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints. Cortex, 63, 79–89. ",
+          "doi": "10.1016/j.cortex.2014.07.007"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Rostral Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_RMF-SF_1"
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Inferior Temporal and Middle Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_IT-MT_0"
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+      ],
+      "name": "Probabilistic maps of lh_IT-MT_0",
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+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Javier DeFelipe"
+      ],
+      "description": "3D reconstructions of cells in rat hippocampal CA1 region using Neurolucida software from 3D confocal stack of images.\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "hbp-00957_RatCA1_Dataset",
+      "files": [],
+      "contributors": [
+        "Miguens, Miguel",
+        "Kastanauskaite, Asta"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects that examins cognitive functions implicated in social cognition, namely mental abilities linked to the theory-of-mind or social interplay. The paradigm was designed in blocks. The blocks were in turn constituted by a set of trials, each of them containing one event. There were eight types of events, that can be described as follows: (1) watch short movies of triangles, exhibiting a putative social interaction; (2) watch short movies of triangles, displaying random movements; (3-4) interpret silently short stories 2, featuring a false-belief plot; stories were presented as visual (3) or auditory (4) stimuli; (5-6) interpret silently short stories2, featuring a cause-consequence mechanistic plot; stories were presented as visual (5) or auditory (6) stimuli; (7) listen passively to short samples of human voices; and (8) listen passively to short samples of natural sounds. The task was constituted by fifteen blocks per run. Each block included one to eight trials. Trials’ presentation within a block was pseudo-randomized for the session, but fixed for all participants. The duration of the trials ranged between six and eight seconds. A fixation cross was presented between each block between three and six seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: ARCHI social",
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+        "Pinel, Philippe",
+        "Martins, Bernadette",
+        "Doublé, Christine",
+        "Médiouni-Cloarec, Gaëlle",
+        "Joly-Testault, Véronique",
+        "Laurier, Laurence",
+        "Roger, Séverine",
+        "Becuwe-Desmidt, Séverine",
+        "Ginisty, Chantal",
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+        "Houenou, Josselin",
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+        "Amunts, Katrin",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Hoemke, L."
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+          "cite": "Zaborszky, L., Hoemke, L., Mohlberg, H., Schleicher, A., Amunts, K., & Zilles, K. (2008). Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain. NeuroImage, 42(3), 1127–1141. ",
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+        "DeFelipe, Javier",
+        "Merchan-Perez, Angel",
+        "Rodriguez, Rodrigo",
+        "Santuy, Andrea"
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+        "Poupon, Cyril"
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+        "Duclap, Delphine",
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+      "description": "This dataset contains the distinct architectonic Area PGp (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PGp (IPL). The probability map of Area PGp (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PGp.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PGp (IPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area PGp (IPL)",
+      "files": [
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Amunts, Katrin",
+        "Mohlberg, Hartmut",
+        "Caspers, S.",
+        "Scheperjans, Filip",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B.",
+        "Zilles, Karl",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/V9NJ-TBQ"
+      ],
+      "publications": [
+        {
+          "name": "The human inferior parietal lobule in stereotaxic space",
+          "cite": "Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481–495. ",
+          "doi": "10.1007/s00429-008-0195-z"
+        },
+        {
+          "name": "The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability",
+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
+          "doi": "10.1016/j.neuroimage.2006.06.054"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Fusiform and Lateral Occipital gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_Fu-LO_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_Fu-LO_1",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_Fu-LO_1.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti, Csv, Txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 77 subjects performing different tasks of a generic localizer paradigm: 1) gender judgement versus no task on face image and expression, 2) truth-worthiness versus gender judgement and 3) baseline. The tasks were executed in a (small) block design (5-7s each) with acquisition duration of 436s.\nVisual stimuli were displayed in four 250-ms epochs, separated by 100ms intervals (i.e., 1.3s in total). Auditory stimuli were drawn from a recorded male voice (i.e., a total of 1.6s for motor instructions, 1.2-1.7s for sentences, and 1.2-1.3s for subtraction). The auditory or visual stimuli were shown to the participants for passive viewing or button response in event-related paradigms. Post-scan questions verified that the experimental tasks were understood and followed correctly. \nWhole-brain EPI data were acquired with the same Siemens Trio with a 32 channel head coil (TR=2400ms, TE=30ms, flip angle=60°, in-plane FOV= 19.2 * 19.2 cm, 40 slices, 3.0mm isotropic voxels). A posterior-anterior phase encoding scheme was used for all images. Standard preprocessing was performed with SPM, including slice timing, motion correction, alignment, and spatial normalization.",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "ARCHI fMRI Database: emotional paradigm",
+      "files": [],
+      "contributors": [
+        "Thirion, Bertrand",
+        "Pinel, Philippe",
+        "Poupon, Cyril",
+        "Grisel, Olivier",
+        "Varoquaux, Gaël"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Principal Component Regression Predicts Functional Responses across Individuals",
+          "cite": "Thirion, B., Varoquaux, G., Grisel, O., Poupon, C., & Pinel, P. (2014). Principal Component Regression Predicts Functional Responses across Individuals. Lecture Notes in Computer Science, 741–748. ",
+          "doi": "10.1007/978-3-319-10470-6_92"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "xlsx"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "DeFelipe, Javier"
+      ],
+      "description": "This dataset contains the density of tyrosine hydroxylase (TH)-immunoreactive cortical interneurons in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 2 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain.",
+      "licenseInfo": [],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Brodmann’s area 17"
+        },
+        {
+          "name": "Brodmann’s area 6"
+        },
+        {
+          "name": "Brodmann’s area 32"
+        },
+        {
+          "name": "Brodmann’s area 24"
+        },
+        {
+          "name": "Brodmann’s area 21"
+        },
+        {
+          "name": "Brodmann’s area 20"
+        },
+        {
+          "name": "Brodmann’s area 12"
+        },
+        {
+          "name": "Brodmann’s area 11"
+        },
+        {
+          "name": "Brodmann’s area 46"
+        },
+        {
+          "name": "Brodmann’s area 10"
+        },
+        {
+          "name": "Brodmann’s area 9"
+        }
+      ],
+      "name": "Distribution of neurons expressing tyrosine hydroxylase in the human cerebral cortex",
+      "files": [],
+      "contributors": [
+        "Benavides-Piccione, Ruth",
+        "DeFelipe, Javier"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Distribution of neurons expressing tyrosine hydroxylase in the human cerebral cortex",
+          "cite": "Benavides-Piccione, R., & DeFelipe, J. (2007). Distribution of neurons expressing tyrosine hydroxylase in the human cerebral cortex. Journal of Anatomy, 211(2), 212–222. ",
+          "doi": "10.1111/j.1469-7580.2007.00760.x"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area OP4 (POperc) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area OP4 (POperc). The probability map of Area OP4 (POperc) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-OP4.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area OP4 (POperc)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area OP4 (POperc)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-OP4.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Schleicher, Axel"
+      ],
+      "kgReference": [
+        "10.25493/51S0-K7W"
+      ],
+      "publications": [
+        {
+          "name": "The Human Parietal Operculum. II. Stereotaxic Maps and Correlation with Functional Imaging Results",
+          "cite": "Eickhoff, S. B., Amunts, K., Mohlberg, H., & Zilles, K. (2005). The Human Parietal Operculum. II. Stereotaxic Maps and Correlation with Functional Imaging Results. Cerebral Cortex, 16(2), 268–279. ",
+          "doi": "10.1093/cercor/bhi106"
+        },
+        {
+          "name": "The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions",
+          "cite": "Eickhoff, S. B., Schleicher, A., Zilles, K., & Amunts, K. (2005). The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions. Cerebral Cortex, 16(2), 254–267. ",
+          "doi": "10.1093/cercor/bhi105"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Ch 4 (Basal Forebrain) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Ch 4 (Basal Forebrain). The probability map of Ch 4 (Basal Forebrain) is given in file jubrain-pmap-v22c_space-mnicolin27_BasalForebrain-Ch-4.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Ch 4 (Basal Forebrain)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Ch 4 (Basal Forebrain)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_BasalForebrain-Ch-4.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_BasalForebrain-Ch-4.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Zaborszky, Laszlo",
+        "Zilles, Karl",
+        "Amunts, Katrin",
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Hoemke, L."
+      ],
+      "kgReference": [
+        "10.25493/BWZ1-5MV"
+      ],
+      "publications": [
+        {
+          "name": "Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain",
+          "cite": "Zaborszky, L., Hoemke, L., Mohlberg, H., Schleicher, A., Amunts, K., & Zilles, K. (2008). Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain. NeuroImage, 42(3), 1127–1141. ",
+          "doi": "10.1016/j.neuroimage.2008.05.055"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Rostral Middle Frontal and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_RMF-SF_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_RMF-SF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 68240520,
+          "name": "rh_RMF-SF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_RMF-SF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "Weber, Bruno"
+      ],
+      "description": "The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the mouse brain. To that end previously synchrotron radiation-based X-ray microscopy images were collected. Additionally recently two-photon microscopy images were collected. And we are looking for additional alternative imaging sources. Then this data is translated into 3D mesh and volumetric models (graph).  \nThis dataset contains the model of part of cortical vasculature of the mouse brain as a graph structure (discrete mathematics).  \nThe data is a derived dataset using computational methods from the dataset “3D imaging of the vascular system of the mouse brain” ([doi: 10.25493/3248-7V](https://doi.org/10.25493/3248-7V)).",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "3D reconstruction of the vascular system of the mouse brain.",
+      "files": [],
+      "contributors": [
+        "Erlebach, Eva",
+        "Efremov, Velizar",
+        "Weber, Bruno"
+      ],
+      "kgReference": [
+        "10.25493/47H2-1HR"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc3d (Cuneus) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc3d (Cuneus). The probability map of Area hOc3d (Cuneus) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc3d.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc3d (Cuneus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc3d (Cuneus)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc3d.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc3d.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Kujovic, Milenko",
+        "Amunts, Katrin",
+        "Eickhoff, Simon B.",
+        "Rottschy, Claudia",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Malikovic, Aleksandar",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/SAMM-YKZ"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human dorsal extrastriate cortex",
+          "cite": "Kujovic, M., Zilles, K., Malikovic, A., Schleicher, A., Mohlberg, H., Rottschy, C., … Amunts, K. (2012). Cytoarchitectonic mapping of the human dorsal extrastriate cortex. Brain Structure and Function, 218(1), 157–172. ",
+          "doi": "10.1007/s00429-012-0390-9"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Waxholm Space rat brain atlas v.2.0"
+        }
+      ],
+      "custodians": [
+        "Bos, Jeroen"
+      ],
+      "description": "The dataset includes spiking data from four areas (V2m, S1bf, perirhinal cortex, and hippocampal CA1) recording simultaneously in freely moving rats during a spatial navigation task with visual discrimination and memory components. Position data of the animals during the task obtained from video tracking is also provided. A more detailed overview of the experimental design and data collection process can be found in [1].\n**Embargo status:**   \n*This dataset is temporarily under embargo. The data will become available for download after the embargo period.*",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Multi-area recordings from visual and somatosensory cortices, perirhinal cortex and hippocampal CA1",
+      "files": [],
+      "contributors": [
+        "van Mourik-Donga, Laura A.",
+        "Pennartz, Cyriel",
+        "Witter, Menno P.",
+        "Jackson, Jadin C.",
+        "Vinck, Martin",
+        "Bos, Jeroen"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Perirhinal firing patterns are sustained across large spatial segments of the task environment",
+          "cite": "Bos, J. J., Vinck, M., van Mourik-Donga, L. A., Jackson, J. C., Witter, M. P., & Pennartz, C. M. A. (2017). Perirhinal firing patterns are sustained across large spatial segments of the task environment. Nature Communications, 8, 15602. ",
+          "doi": "10.1038/ncomms15602"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Riess, Olaf"
+      ],
+      "description": "Bright-field microscopy images of serial coronal brain sections showing Ca2+/calmodulin-dependent protein kinase II (Camk2a) promoter expression in a bigenic Camk2a-tetracycline-transactivator (tTA) mouse brain (case 317.8, adult female), using a driver-reporter construct in which the Camk2a-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Odeh et al., Neuroimage 54:2603-11, 2011. Our data show that the Camk2a-tTA promoter is spatially expressed in multiple brain regions, including the cerebral cortex, several basal ganglia regions, hippocampus, tectum, and cerebellum.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Ca2+/calmodulin-dependent protein kinase II tetracycline-transactivator expression: coronal sections (case 317.8)",
+      "files": [],
+      "contributors": [
+        "Odeh, Francis",
+        "Riess, Olaf",
+        "Boy, Jana",
+        "Bjaalie, Jan G.",
+        "Schmidt, Thorsten",
+        "Leergaard, Trygve B."
+      ],
+      "kgReference": [
+        "10.25493/W97C-01R"
+      ],
+      "publications": [
+        {
+          "name": "Atlas of transgenic Tet-Off Ca2+/calmodulin-dependent protein kinase II and prion protein promoter activity in the mouse brain",
+          "cite": "Odeh, F., Leergaard, T. B., Boy, J., Schmidt, T., Riess, O., & Bjaalie, J. G. (2011). Atlas of transgenic Tet-Off Ca2+/calmodulin-dependent protein kinase II and prion protein promoter activity in the mouse brain. NeuroImage, 54(4), 2603–2611. ",
+          "doi": "10.1016/j.neuroimage.2010.11.032"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Inferior Temporal and Middle Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_IT-MT_1"
+        }
+      ],
+      "name": "Probabilistic maps of rh_IT-MT_1",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "name": "rh_IT-MT_1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_IT-MT_1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc5 (LOC) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc5 (LOC). The probability map of Area hOc5 (LOC) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc5.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc5 (LOC)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc5 (LOC)",
+      "files": [
+        {
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc5.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc5.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Eickhoff, Simon B.",
+        "Zilles, Karl",
+        "Armstrong, E.",
+        "Palomero-Gallagher, Nicola",
+        "Wilms, M.",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Amunts, Katrin",
+        "Malikovic, Aleksandar"
+      ],
+      "kgReference": [
+        "10.25493/BPG7-360"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic Analysis of the Human Extrastriate Cortex in the Region of V5/MT+: A Probabilistic, Stereotaxic Map of Area hOc5",
+          "cite": "Malikovic, A., Amunts, K., Schleicher, A., Mohlberg, H., Eickhoff, S. B., Wilms, M., … Zilles, K. (2006). Cytoarchitectonic Analysis of the Human Extrastriate Cortex in the Region of V5/MT+: A Probabilistic, Stereotaxic Map of Area hOc5. Cerebral Cortex, 17(3), 562–574. ",
+          "doi": "10.1093/cercor/bhj181"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Postcentral and Precentral gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_PoC-PrC_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_PoC-PrC_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+          "byteSize": 68240520,
+          "name": "lh_PoC-PrC_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_PoC-PrC_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects that examins functions involved in spatial cognition. The paradigm was structured in blocks and it comprised five block categories. Each block was formed by a set of trials containing an event, in which visual instructions related to one or two conditions of the same kind were displayed. These five categories of blocks were characterized as follows: (1) saccade, in which ocular movements were performed according to the displacement of a fixation across from the center toward peripheral locations in the image displayed; (2) imitation of object grasping with the right hand, in which the corresponding object was displayed on the screen; (3) mimic orientation of rhombus, displayed as image background on the screen, using the right hand; events of block categories 2 and 3 featured the same visual stimuli, in order to capture grasping-specific activity; (4) judgment on the left/right orientation of a hand displayed as visual stimulus; and (5) judgment on the palmar/dorsal direction of a hand displayed as visual stimulus. Events of block categories 4 and 5 featured the same visual stimuli. The task was constituted by forty blocks per run. The order of blocks presentation was pseudo-randomized for the session, but fixed for all participants. Each block was composed by either three or four trials. The duration of the trials ranged between 1.2 and 1.8 seconds. All blocks were inter-spaced by a fixation-cross period with a duration between four and six seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: ARCHI spatial",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+      "license": [],
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+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
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+        "10.25493/6RWD-6KM"
+      ],
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+    },
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+      "formats": [
+        "NIFTI"
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+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area PFm (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PFm (IPL). The probability map of Area PFm (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PFm.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Area PFm (IPL)"
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+      ],
+      "contributors": [
+        "Schleicher, Axel",
+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Geyer, Stefan",
+        "Caspers, S.",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/AJGE-PNH"
+      ],
+      "publications": [
+        {
+          "name": "The human inferior parietal lobule in stereotaxic space",
+          "cite": "Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481–495. ",
+          "doi": "10.1007/s00429-008-0195-z"
+        },
+        {
+          "name": "The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability",
+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
+          "doi": "10.1016/j.neuroimage.2006.06.054"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Pars Triangularis and Superior Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_Tr-SF_0"
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+      "name": "Probabilistic maps of rh_Tr-SF_0",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_Tr-SF_0.nii",
+          "contentType": "application/octet-stream"
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+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
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+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
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+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "Weber, Bruno"
+      ],
+      "description": "The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the mouse brain. To that end previously synchrotron radiation-based X-ray microscopy images were collected. Recently two-photon microscopy images were collected to that end. And we are looking for additional alternative imaging sources. Then this data is translated into 3D mesh and volumetric models (graph).  \nThis dataset contains tiff image array of part of cortical vasculature of the mouse brain.  \nThis dataset is used as an input to computational methods that results in the dataset “3D reconstruction of the vascular system of the mouse brain” ([doi: 10.25493/47H2-1HR](https://doi.org/10.25493/47H2-1HR)).",
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
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+        "Free"
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "3D imaging of the vascular system of the mouse brain.",
+      "files": [],
+      "contributors": [
+        "Weber, Bruno",
+        "Erlebach, Eva",
+        "Efremov, Velizar"
+      ],
+      "kgReference": [
+        "10.25493/3248-7V"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area TE 1.0 (HESCHL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area TE 1.0 (HESCHL). The probability map of Area TE 1.0 (HESCHL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-TE-10.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area TE 1.0 (HESCHL)"
+        }
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+      "name": "Probabilistic cytoarchitectonic map of Area TE 1.0 (HESCHL)",
+      "files": [
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-TE-10.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Morosan, Patricia",
+        "Amunts, Katrin",
+        "Werner, C.",
+        "Freund, H.-J.",
+        "Schormann, Thorsten",
+        "Rademacher, Jörg"
+      ],
+      "kgReference": [
+        "10.25493/CP2T-FYT"
+      ],
+      "publications": [
+        {
+          "name": "Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System",
+          "cite": "Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System. NeuroImage, 13(4), 684–701. ",
+          "doi": "10.1006/nimg.2000.0715"
+        },
+        {
+          "name": "Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex",
+          "cite": "Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.-J., & Zilles, K. (2001). Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex. NeuroImage, 13(4), 669–683. ",
+          "doi": "10.1006/nimg.2000.0714"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic CA2 (Hippocampus) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to CA2 (Hippocampus). The probability map of CA2 (Hippocampus) is given in file jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "CA2 (Hippocampus)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of CA2 (Hippocampus)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Hippocampus-CA2.nii",
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Shah, Nadim J.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Habel, U.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/1DJG-294"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 4a (PreCG) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 4a (PreCG). The probability map of Area 4a (PreCG) is given in file jubrain-pmap-v22c_space-mnicolin27_ Area-4a.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 4a (PreCG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 4a (PreCG)",
+      "files": [
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-4a.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-4a.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Klingberg, Torkel",
+        "Roland, Per E.",
+        "Zilles, Karl",
+        "Larsson, Jonas",
+        "Bürgel, Uli",
+        "Schormann, Thorsten",
+        "Kinomura, Shigeo",
+        "Schleicher, Axel",
+        "Ledberg, Anders",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/5GXC-Z2U"
+      ],
+      "publications": [
+        {
+          "name": "Two different areas within the primary motor cortex of man",
+          "cite": "Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bürgel, U., … Roland, P. E. (1996). Two different areas within the primary motor cortex of man. Nature, 382(6594), 805–807. ",
+          "doi": "10.1038/382805a0"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Lateral Orbitofrontal and Superior Temporal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_LOF-ST_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_LOF-ST_0",
+      "files": [
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_LOF-ST_0.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area PGa (IPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area PGa (IPL). The probability map of Area PGa (IPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-PGa.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area PGa (IPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area PGa (IPL)",
+      "files": [
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+          "byteSize": 17487360,
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-PGa.nii",
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+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
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+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Scheperjans, Filip",
+        "Geyer, Stefan",
+        "Schleicher, Axel",
+        "Caspers, S.",
+        "Eickhoff, Simon B.",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/T96P-05Y"
+      ],
+      "publications": [
+        {
+          "name": "The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability",
+          "cite": "Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. ",
+          "doi": "10.1016/j.neuroimage.2006.06.054"
+        },
+        {
+          "name": "The human inferior parietal lobule in stereotaxic space",
+          "cite": "Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481–495. ",
+          "doi": "10.1007/s00429-008-0195-z"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Pars Opercularis and Insula gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_Op-Ins_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_Op-Ins_0",
+      "files": [
+        {
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+          "name": "lh_Op-Ins_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_Op-Ins_0.nii",
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+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the left hemisphere Caudal Middle Frontal and Rostral Middle Frontal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "lh_CMF-RMF_0"
+        }
+      ],
+      "name": "Probabilistic maps of lh_CMF-RMF_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
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+          "name": "lh_CMF-RMF_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/left hemisphere/Probability_Maps/lh_CMF-RMF_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Lateral Occipital and Superior Parietal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_LO-SP_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_LO-SP_0",
+      "files": [
+        {
+          "byteSize": 156110,
+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_LO-SP_0.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Poupon, Cyril"
+      ],
+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Precentral and Superior Parietal gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "rh_PrC-SP_0"
+        }
+      ],
+      "name": "Probabilistic maps of rh_PrC-SP_0",
+      "files": [
+        {
+          "byteSize": 68240520,
+          "name": "rh_PrC-SP_0.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/right hemisphere/Probability_Maps/rh_PrC-SP_0.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
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+          "name": "short-bundles_maxprob.nii.gz",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000339/short-bundles_maxprob.nii.gz",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
+      ],
+      "kgReference": [],
+      "publications": [
+        {
+          "name": "Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography",
+          "cite": "Guevara, M., Román, C., Houenou, J., Duclap, D., Poupon, C., Mangin, J. F., & Guevara, P. (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage, 147, 703–725. ",
+          "doi": "10.1016/j.neuroimage.2016.11.066"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [],
+      "custodians": [
+        "Cherubini, Enrico"
+      ],
+      "description": "A general procedure to fit individual synaptic events recorded from voltage clamp experiments was tested with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. The data used here are obtained from coronal hippocampal slices (300 μm thick) from 3 to 4 months old Tg2576 (carrying the Amyloid Precursor Protein KM670/671NL Swedish mutation) female mice. The dataset contains 1000 individual events recorded from 10 different neurons (expE1-E10). The first column of each txt file contains the time in ms and the other columns contain spontaneous inhibitory synaptic currents (sIPSCs) in pA.\nComputational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In [1] we proposed a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. A Python version is available for public use, as a Jupyter Notebook in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.\nFitting experimental data against a number of different models is a common way to do this (reviewed in [2]), and can help in the subsequent interpretation of the data. In general, experimental traces are fitted using specific approaches for specific purposes (e.g., [3], [4]). However, to the best of our knowledge, there is no easy, user-friendly, general procedure available for this purpose, especially in computational neuroscience. Our aim was thus to identify the appropriate conceptual structure of a procedure to obtain good, reliable fits of raw experimental traces of spontaneous synaptic events. This is an important step because spontaneous synaptic events have been so far exclusively analyzed using traces obtained by averaging many events. However, as can be easily imagined, each synapse in any given neuron has its own, independent, history of activation. The most likely physiological consequence is that the variables relative to the subcellular processes underlying synaptic transmission are different for each synapse. If a researcher is interested in testing a specific kinetic scheme implemented for specific biochemical pathways, the use of individual events is the most appropriate choice, since this approach would give information on the different combinations of model parameters that are consistent with the observed events. Averaging traces will lose a lot of relevant information.\nWe therefore present here the implementation of a procedure leading to the development of a unifying optimization method for individual synaptic events. Experimental data, kinetic models of synaptic transmission, and fitting parameters and their dependencies can be user defined/provided or gathered from databases. They can be used to generate optimized groups of parameters able to represent a population of synapses, either for simulation purposes or to study the functional consequences of a particular protein or subcellular synaptic transmission pathway.\n[1] Lupascu CA, Morabito A, Merenda E, et al. A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory Synapses. Frontiers in Neuroinformatics. 2016;10:23. doi:10.3389/fninf.2016.00023.  \n[2] Van Geit W., De Schutter E., Achard P. (2008). Automated neuron model optimization techniques: a review. Biol. Cybern. 99, 241–251. 10.1007/s00422-008-0257-6  \n[3] Bekkers J. M. (2003). Convolution of mini distributions for fitting evoked synaptic amplitude histograms. J. Neurosci. Methods. 130, 105–114. 10.1016/j.jneumeth.2003.09.018  \n[4] Meisl G., Kirkegaard J. B., Arosio P., Michaels T. C., Vendruscolo M., Dobson C. M., et al. . (2016). Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nat. Protoc. 11, 252–272. 10.1038/nprot.2016.010",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Spontaneous inhibitory post-synaptic currents recorded from CA1 pyramidal neurons of adult wild type Tg2576 mice",
+      "files": [],
+      "contributors": [
+        "Cherubini, Enrico",
+        "Marinelli, Silvia",
+        "Marchetti, Cristina"
+      ],
+      "kgReference": [
+        "10.25493/CTAV-Q45"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area hOc4v (LingG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area hOc4v (LingG). The probability map of Area hOc4v (LingG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-hOc4v.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area hOc4v (LingG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area hOc4v (LingG)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-hOc4v.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-hOc4v.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Rottschy, Claudia",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Kujovic, Milenko",
+        "Mohlberg, Hartmut",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/E0YJ-ADE"
+      ],
+      "publications": [
+        {
+          "name": "Ventral visual cortex in humans: Cytoarchitectonic mapping of two extrastriate areas",
+          "cite": "Rottschy, C., Eickhoff, S. B., Schleicher, A., Mohlberg, H., Kujovic, M., Zilles, K., & Amunts, K. (2007). Ventral visual cortex in humans: Cytoarchitectonic mapping of two extrastriate areas. Human Brain Mapping, 28(10), 1045–1059. ",
+          "doi": "10.1002/hbm.20348"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V3"
+        }
+      ],
+      "custodians": [
+        "D'Angelo, Egidio"
+      ],
+      "description": "Study and characterization of the intrinsic properties of Golgi cells.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "parcellationRegion": [],
+      "name": "Recordings of spontaneous firing of cerebellar interneurons (Golgi cells)",
+      "files": [],
+      "contributors": [
+        "Tognolina, Marialuisa",
+        "D'Angelo, Egidio",
+        "Locatelli, Francesca"
+      ],
+      "kgReference": [
+        "10.25493/JQH3-0A4"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "Nifti Files, gifti files, csv files, json files"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Thirion, Bertrand"
+      ],
+      "description": "This dataset contains functional magnetic resonance images (fMRIs) of 12 subjects performing HCP Working Memory (HCP WM) task that was adapted from the classical n-back task to serve as functional localizer for evaluation of structures involved in working memory (WM). The paradigm included two categories of blocks, namely the “0-back” and “2- back” WM-task blocks. They were both equally presented within a run. A cue was always displayed at the beginning of each block, indicating its block type. Blocks were formed by sets of events, during which pictures of faces, places, tools or body parts were shown on the screen. One block was always dedicated to one specific category of pictures and the four categories were always presented during every run. The task was constituted by sixteen blocks per run, eight per n-back category. Besides, there were four pairs of blocks per visual category. The order of the blocks, regardless of their category and corresponding class of pictures, was pseudo-randomized for every run, but fixed for all participants. A fixationcross period of fifteen seconds was introduced between some blocks. All blocks contained ten trials; they were initiated by a cue during 2.5 seconds. Trials included in turn the presentation of a picture for two seconds and a very short fixation-cross period for half of a second; the total duration of one trial was thus 2.5 seconds.\n\n[https://openfmri.org/dataset/ds000244/](https://openfmri.org/dataset/ds000244/)\n\n[https://neurovault.org/collections/2138/](https://neurovault.org/collections/2138/)",
+      "licenseInfo": [
+        "Creative Commons CC0 - No Rights Reserved"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Individual Brain Charting: HCP Working Memory task",
+      "files": [
+        {
+          "byteSize": 3491751,
+          "name": "sub-02_ses-05_task-HcpWm_acq-pa_sbref.nii.gz",
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+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4213,
+          "name": "sub-02_ses-05_task-HcpWm_acq-pa_events.tsv",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-02/ses-05/func/sub-02_ses-05_task-HcpWm_acq-pa_events.tsv",
+          "contentType": "text/tab-separated-values"
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-13/ses-02/func/sub-13_ses-02_task-HcpWm_acq-ap_sbref.nii.gz",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/IBC/sub-04/ses-02/func/sub-04_ses-02_task-HcpWm_acq-pa_bold.nii.gz",
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
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+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
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+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
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+      "parcellationRegion": [],
+      "name": "Pituitary homeobox 3 tetracycline-transactivator expression: coronal sections (case 6517)",
+      "files": [],
+      "contributors": [
+        "Yetman, Michael J.",
+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Checinska, Martyna M.",
+        "Puchades, Maja A.",
+        "Jankowsky, Joanna L.",
+        "Leergaard, Trygve B."
+      ],
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+        "10.25493/DTB3-0KP"
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+      "publications": []
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+          "name": "Allen adult mouse brain reference atlas V2"
+        }
+      ],
+      "custodians": [
+        "Jankowsky, Joanna L."
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+      "description": "Bright-field microscopy images of serial coronal brain sections showing neuropsin (Nop) promoter expression in a bigenic Nop-tetracycline-transactivator (tTA) mouse brain (case 1952 adult male), using a driver-reporter construct in which the Nop-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Our data show that the Nop-tTA promoter mainly is expressed in the entorhinal cortex, but also in several other cortical regions.",
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+        "Creative Commons Attribution 4.0 International"
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+      "name": "Neuropsin tetracycline-transactivator expression: coronal sections (case 1952)",
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+        "Lillehaug, Sveinung",
+        "Bjaalie, Jan G.",
+        "Jankowsky, Joanna L.",
+        "Yetman, Michael J."
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+          "name": "Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex",
+          "cite": "Yetman, M. J., Lillehaug, S., Bjaalie, J. G., Leergaard, T. B., & Jankowsky, J. L. (2015). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex. Brain Structure and Function, 221(4), 2231–2249. ",
+          "doi": "10.1007/s00429-015-1040-9"
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+        "NIFTI"
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+        {
+          "name": "MNI Colin 27"
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+        "Amunts, Katrin"
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
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+      "parcellationRegion": [
+        {
+          "name": "Area OP3 (POperc)"
+        }
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+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B.",
+        "Mohlberg, Hartmut"
+      ],
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+        "10.25493/6PRT-2PS"
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+          "cite": "Eickhoff, S. B., Amunts, K., Mohlberg, H., & Zilles, K. (2005). The Human Parietal Operculum. II. Stereotaxic Maps and Correlation with Functional Imaging Results. Cerebral Cortex, 16(2), 268–279. ",
+          "doi": "10.1093/cercor/bhi106"
+        },
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+          "cite": "Eickhoff, S. B., Schleicher, A., Zilles, K., & Amunts, K. (2005). The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions. Cerebral Cortex, 16(2), 254–267. ",
+          "doi": "10.1093/cercor/bhi105"
+        }
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+      "custodians": [
+        "Gulyás, Attila"
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+      "description": "Understanding cortical circuits requires the characterization of their constituent neurons.\nUsing whole-cell recordings in acute hippocampal slices, followed by confocal imaging and 3D reconstruction,\na database of the morphological and physiological properties of hippocampal neurons has been constructed.\nThe database currently contains approximately 200 morphological reconstructions and 500 recordings\nfrom partially overlapping cell populations.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
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+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
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+      "name": "Morphological and physiological database of excitatory and inhibitory cell types of the mouse hippocampus",
+      "files": [],
+      "contributors": [
+        "Kohus, Zsolt",
+        "Schlingloff, Dániel",
+        "Berki, Péter",
+        "Gulyás, Attila",
+        "Kriczky, Nándor"
+      ],
+      "kgReference": [],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
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+      "custodians": [
+        "Poupon, Cyril"
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+      "description": "This data contains the probability map of a short fiber bundle connecting the right hemisphere Caudal Anterior Cingulate and Precuneus gyri, in the MNI152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fiber shape (fiber clustering), from the tractography datasets of 77 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fiber belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibers going though.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
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+        "Free"
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+        "Guevara, Miguel",
+        "Guevara, Pamela",
+        "Mangin, Jean-François",
+        "Poupon, Cyril",
+        "Duclap, Delphine",
+        "Houenou, Josselin",
+        "Román, Claudio"
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+    {
+      "formats": [
+        "NIFTI"
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+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic SF (Amygdala) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to SF (Amygdala). The probability map of SF (Amygdala) is given in file jubrain-pmap-v22c_space-mnicolin27_Amygdala-SF.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "SF (Amygdala)"
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+      "name": "Probabilistic cytoarchitectonic map of SF (Amygdala)",
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+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Habel, U.",
+        "Zilles, Karl",
+        "Schneider, F.",
+        "Shah, Nadim J.",
+        "Mohlberg, Hartmut",
+        "Pieperhoff, P.",
+        "Kindler, M.",
+        "Kedo, O.",
+        "Amunts, Katrin"
+      ],
+      "kgReference": [
+        "10.25493/BRNZ-SXW"
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+      "publications": [
+        {
+          "name": "Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps",
+          "cite": "Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., … Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5-6), 343–352. ",
+          "doi": "10.1007/s00429-005-0025-5"
+        }
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+    {
+      "formats": [
+        "3D TIFF"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "This dataset contains the image of spatial distribution of GABA receptor in human cortex using serial two photons microscopy. The individual 3D tiles acquired with the two photon fluorescence microscope were fused together to produce the final image.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Visual cortex area 17"
+        }
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+      "name": "Maps of GABA receptors in human cortex",
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+          "name": "gaba.tiff",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000772/gaba.tiff",
+          "contentType": "image/tiff"
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+      "contributors": [
+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/EXHA-95S"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 3a (PostCG) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 3a (PostCG). The probability map of Area 3a (PostCG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-3a.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 3a (PostCG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 3a (PostCG)",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-3a.nii",
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+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
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+      ],
+      "contributors": [
+        "Mohlberg, Hartmut",
+        "Geyer, Stefan",
+        "Schleicher, Axel",
+        "Schormann, Thorsten",
+        "Zilles, Karl"
+      ],
+      "kgReference": [
+        "10.25493/R19N-B8E"
+      ],
+      "publications": [
+        {
+          "name": "Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex",
+          "cite": "Geyer, S., Schleicher, A., & Zilles, K. (1999). Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex. NeuroImage, 10(1), 63–83. ",
+          "doi": "10.1006/nimg.1999.0440"
+        },
+        {
+          "name": "Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex",
+          "cite": "Geyer, S., Schormann, T., Mohlberg, H., & Zilles, K. (2000). Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex. NeuroImage, 11(6), 684–696. ",
+          "doi": "10.1006/nimg.2000.0548"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area Ig2 (Insula) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area Ig2 (Insula). The probability map of Area Ig2 (Insula) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-Ig2.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area Ig2 (Insula)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area Ig2 (Insula)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-Ig2.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-Ig2.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Kurth, F.",
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Hoemke, L.",
+        "Schleicher, Axel",
+        "Eickhoff, Simon B."
+      ],
+      "kgReference": [
+        "10.25493/N7C7-BHW"
+      ],
+      "publications": [
+        {
+          "name": "Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex",
+          "cite": "Kurth, F., Eickhoff, S. B., Schleicher, A., Hoemke, L., Zilles, K., & Amunts, K. (2009). Cytoarchitecture and Probabilistic Maps of the Human Posterior Insular Cortex. Cerebral Cortex, 20(6), 1448–1461. ",
+          "doi": "10.1093/cercor/bhp208"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area OP1 (POperc) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area OP1 (POperc). The probability map of Area OP1 (POperc) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-OP1.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area OP1 (POperc)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area OP1 (POperc)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-OP1.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-OP1.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Amunts, Katrin",
+        "Zilles, Karl",
+        "Mohlberg, Hartmut",
+        "Eickhoff, Simon B.",
+        "Schleicher, Axel"
+      ],
+      "kgReference": [
+        "10.25493/HVH9-KBR"
+      ],
+      "publications": [
+        {
+          "name": "The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions",
+          "cite": "Eickhoff, S. B., Schleicher, A., Zilles, K., & Amunts, K. (2005). The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions. Cerebral Cortex, 16(2), 254–267. ",
+          "doi": "10.1093/cercor/bhi105"
+        },
+        {
+          "name": "The Human Parietal Operculum. II. Stereotaxic Maps and Correlation with Functional Imaging Results",
+          "cite": "Eickhoff, S. B., Amunts, K., Mohlberg, H., & Zilles, K. (2005). The Human Parietal Operculum. II. Stereotaxic Maps and Correlation with Functional Imaging Results. Cerebral Cortex, 16(2), 268–279. ",
+          "doi": "10.1093/cercor/bhi106"
+        }
+      ]
+    },
+    {
+      "formats": [],
+      "referenceSpaces": [
+        {
+          "name": "Allen adult mouse brain reference atlas V2"
+        }
+      ],
+      "custodians": [
+        "Leergaard, Trygve B."
+      ],
+      "description": "Tabular overview (csv format) of brain-wide reporter expression mapped in five commonly used tetracycline-transactivator (tTA) driver lines: neuropsin (Nop); L7/Purkinje cell protein 2 (Pcp2); Pituitary homeobox 3 (Pitx3); cellular prion protein (Prnp), and Ca2+/calmodulin-dependent protein kinase IIa (Camk2a).  This analysis is derived from microscopic brain images taken from 12 driver-reporter constructs (DOIs for source data sets are specified in data descriptor file listed below; hbp-00170\\_Comparative\\_DataDescriptor\\_v1p1.txt) in which the different promoters regulate the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Labelling was observed in microscopic images that were spatially registered to the Allen Mouse Common Coordinate Framework (v2; mouse.brain-map.org). The density of labeling was assessed by two independent researchers using a semi-quantitative grading system from 0 – 4, introduced in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Here grade 0 represents absence of labeled cells (less than 1 per 0.01 mm2), grade 1 - low density (few cells, possible to count), grade 2 - medium density (several cells that can be individually discerned, but not readily counted), grade 3 - high density (many labeled cells with large degree of overlap), and grade 4 - very high density (where individual cells cannot be discerned). For each promoter-tTA line one representative case was semi-quantitatively scored and results verified in the other cases. Scores did not vary more than 1 grade between cases or researchers in any regions. The highest numbers were reported. If the density of labeling was found to vary substantially within a region, the highest observed score was recorded.",
+      "licenseInfo": [
+        "Creative Commons Attribution 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [],
+      "name": "Comparative overview of brain-wide tetracycline-transactivator expression",
+      "files": [],
+      "contributors": [
+        "Puchades, Maja A.",
+        "Bjaalie, Jan G.",
+        "Lillehaug, Sveinung",
+        "Leergaard, Trygve B.",
+        "Jankowsky, Joanna L.",
+        "Kleven, Heidi",
+        "Checinska, Martyna M.",
+        "Yetman, Michael J."
+      ],
+      "kgReference": [
+        "10.25493/ARKS-R7H"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "3D-TIFF"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Pavone, Francesco S."
+      ],
+      "description": "This dataset contains images of astrocytes in the human cortex obtained using serial two-photon microscopy. The individual 3D tiles acquired with the two photon fluorescence microscope were fused together to produce the final image.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Visual cortex area 17"
+        }
+      ],
+      "name": "Map of astrocytes in human brain cortex",
+      "files": [
+        {
+          "byteSize": 2399916287,
+          "name": "mosaic2.tiff",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/hbp-data-000326/mosaic2.tiff",
+          "contentType": "image/tiff"
+        }
+      ],
+      "contributors": [
+        "Costantini, Irene",
+        "Pavone, Francesco S."
+      ],
+      "kgReference": [
+        "10.25493/6RNR-BDX"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "xlsx, tif, txt"
+      ],
+      "referenceSpaces": [],
+      "custodians": [
+        "Zilles, Karl",
+        "Palomero-Gallagher, Nicola"
+      ],
+      "description": "This dataset contains the densities (in fmol/mg protein) of 16 receptors for classical neurotransmitters in the Globus Pallidus using quantitative in vitro autoradiography. The receptor density measurements can be provided in three ways: (fp) as density fingerprints (average across samples; mean density and standard deviation for each of the 16 receptors), (pr) as laminar density profiles (exemplary data from one sample; average course of the density from the pial surface to the border between layer VI and the white matter for each receptor), and (ar) as color-coded autoradiographs (exemplary data from one sample; laminar density distribution patterns for each receptor labeling). \nThis dataset contains the following receptor density measurements based on the labeling of these receptor binding sites: \n\nAMPA (glutamate; labelled with [³H]AMPA): fp\n\nkainate (glutamate; [³H]kainate): fp\n\nNMDA (glutamate; [³H]MK-801): fp\n\nGABA<sub>A</sub> (GABA; [³H]muscimol): fp\n\nGABA<sub>B</sub> (GABA; [³H] CGP54626): fp\n\nGABA<sub>A</sub> associated benzodiazepine binding sites (BZ; [³H]flumazenil): fp\n\nmuscarinic M₁ (acetylcholine; [³H]pirenzepine): fp\n\nmuscarinic M₂ (acetylcholine; [³H]oxotremorine-M): fp\n\nmuscarinic M₃ (acetylcholine; [³H]4-DAMP): fp\n\nnicotinic α₄β₂ (acetylcholine; [³H]epibatidine): fp\n\nα₁ (noradrenalin; [³H]prazosin): fp\n\nα₂ (noradrenalin; [³H]UK-14,304): fp\n\n5-HT₁<sub>A</sub> (serotonin; [³H]8-OH-DPAT): fp\n\n5-HT₂ (serotonin; [³H]ketanserin): fp\n\nD₁ (dopamine; [³H]SCH23390): fp\n\nWhich sample was used for which receptor density measurement is stated in metadata files accompanying the main data repository. For methodological details, see Zilles et al. (2002), and in Palomero-Gallagher and Zilles (2018).\n\nZilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press.\n\nPalomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Globus Pallidus"
+        }
+      ],
+      "name": "Density measurements of different receptors for Globus Pallidus",
+      "files": [
+        {
+          "byteSize": 15948,
+          "name": "GP_fp_20180405.xlsx",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/data_derived/Nucleus_GP/GP_fp_20180405.xlsx",
+          "contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        },
+        {
+          "byteSize": 44096,
+          "name": "metadata.zip",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/Receptors/metadata.zip",
+          "contentType": "application/zip"
+        }
+      ],
+      "contributors": [
+        "Palomero-Gallagher, Nicola"
+      ],
+      "kgReference": [
+        "10.25493/TPRG-5VH"
+      ],
+      "publications": []
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 4p (PreCG) in the MNI Colin 27 reference. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference where each voxel is assigned with the probability to belong to Area 4p (PreCG). The probability map of Area 4p (PreCG) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-4p.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 4p (PreCG)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 4p (PreCG)",
+      "files": [
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-4p.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-4p.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Klingberg, Torkel",
+        "Roland, Per E.",
+        "Zilles, Karl",
+        "Larsson, Jonas",
+        "Bürgel, Uli",
+        "Schormann, Thorsten",
+        "Kinomura, Shigeo",
+        "Schleicher, Axel",
+        "Ledberg, Anders",
+        "Geyer, Stefan"
+      ],
+      "kgReference": [
+        "10.25493/ECYA-0D1"
+      ],
+      "publications": [
+        {
+          "name": "Two different areas within the primary motor cortex of man",
+          "cite": "Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bürgel, U., … Roland, P. E. (1996). Two different areas within the primary motor cortex of man. Nature, 382(6594), 805–807. ",
+          "doi": "10.1038/382805a0"
+        }
+      ]
+    },
+    {
+      "formats": [
+        "NIFTI"
+      ],
+      "referenceSpaces": [
+        {
+          "name": "MNI Colin 27"
+        }
+      ],
+      "custodians": [
+        "Amunts, Katrin"
+      ],
+      "description": "This dataset contains the distinct architectonic Area 7A (SPL) in the MNI Colin 27 reference brain. As part of the JuBrain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 reference brain where each voxel is assigned with the probability to belong to Area 7A (SPL). The probability map of Area 7A (SPL) is given in file jubrain-pmap-v22c_space-mnicolin27_Area-7A.nii. The JuBrain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets.",
+      "licenseInfo": [
+        "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"
+      ],
+      "embargoStatus": [
+        "Free"
+      ],
+      "license": [],
+      "parcellationRegion": [
+        {
+          "name": "Area 7A (SPL)"
+        }
+      ],
+      "name": "Probabilistic cytoarchitectonic map of Area 7A (SPL)",
+      "files": [
+        {
+          "byteSize": 17487360,
+          "name": "jubrain-pmap-v22c_space-mnicolin27_Area-7A.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-pmap-v22c_space-mnicolin27_Area-7A.nii",
+          "contentType": "application/octet-stream"
+        },
+        {
+          "byteSize": 4372104,
+          "name": "jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "absolutePath": "https://object.cscs.ch/v1/AUTH_227176556f3c4bb38df9feea4b91200c/JuBrain/data-derived/jubrain-pmaps-v22c/jubrain-max-pmap-v22c_space-mnicolin27.nii",
+          "contentType": "application/octet-stream"
+        }
+      ],
+      "contributors": [
+        "Zilles, Karl",
+        "Schleicher, Axel",
+        "Hermann, K.",
+        "Scheperjans, Filip",
+        "Eickhoff, Simon B.",
+        "Mohlberg, Hartmut",
+        "Amunts, Katrin",
+        "Hoemke, L."
+      ],
+      "kgReference": [
+        "10.25493/MQ4V-YRR"
+      ],
+      "publications": [
+        {
+          "name": "Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2007). Observer-Independent Cytoarchitectonic Mapping of the Human Superior Parietal Cortex. Cerebral Cortex, 18(4), 846–867. ",
+          "doi": "10.1093/cercor/bhm116"
+        },
+        {
+          "name": "Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex",
+          "cite": "Scheperjans, F., Eickhoff, S. B., Hoemke, L., Mohlberg, H., Hermann, K., Amunts, K., & Zilles, K. (2008). Probabilistic Maps, Morphometry, and Variability of Cytoarchitectonic Areas in the Human Superior Parietal Cortex. Cerebral Cortex, 18(9), 2141–2157. ",
+          "doi": "10.1093/cercor/bhm241"
+        }
+      ]
+    }
+  ],
+  "total": 347,
+  "size": 450,
+  "start": 0
+}
\ No newline at end of file
diff --git a/src/res/ext/camillaWaxholmPointsAggregatedData.json b/src/res/ext/camillaWaxholmPointsAggregatedData.json
index ad504a85726ec3f6392162c3d6ec9d1fbaa724c3..46f83655c43787c882055e600b699a5d767baa5c 100644
--- a/src/res/ext/camillaWaxholmPointsAggregatedData.json
+++ b/src/res/ext/camillaWaxholmPointsAggregatedData.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/src/res/ext/colin.json b/src/res/ext/colin.json
index f5c73b84f8bd5087b5602eca7ff741b7091276b5..10e5be246a4fac2872aae200b85815b5e31ffecc 100644
--- a/src/res/ext/colin.json
+++ b/src/res/ext/colin.json
@@ -1 +1 @@
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diff --git a/src/res/ext/dwmAggregatedData.json b/src/res/ext/dwmAggregatedData.json
index 23837b49e563cf38a7f35aefaba769c7bacaae04..822fd62a5859442542ccc7803580a7f56c40c88e 100644
--- a/src/res/ext/dwmAggregatedData.json
+++ b/src/res/ext/dwmAggregatedData.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/src/res/ext/pmapsAggregatedData.json b/src/res/ext/pmapsAggregatedData.json
index 668f4415b4bb522cd33014c9d82a62d741280be5..23f486e60c3daaf991bdb549f82a2466fc257b0e 100644
--- a/src/res/ext/pmapsAggregatedData.json
+++ b/src/res/ext/pmapsAggregatedData.json
@@ -1 +1,2739 @@
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+    "uuid": "d7f376b1-6a4a-4b60-8d4a-0b33b2caea93"
+  },
+  {
+    "type": "Probabilistic cytoarchitectonic map",
+    "name": "Probabilistic cytoarchitectonic map of Dorsal Dentate Nucleus (Cerebellum)",
+    "regionName": [
+      {
+        "regionName": "Dorsal Dentate Nucleus (Cerebellum)",
+        "relationship": "equals"
+      }
+    ],
+    "files": [
+      {
+        "filename": "Cytoarchitectonic Probabilistic Map for Dorsal Dentate Nucleus (Cerebellum)",
+        "name": "Cytoarchitectonic Probabilistic Map for Dorsal Dentate Nucleus (Cerebellum)",
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+        "url": "https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Ndentd.nii",
+        "properties": {}
+      }
+    ],
+    "targetParcellation": "JuBrain Cytoarchitectonic Atlas",
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+        }
+      ],
+      "associatedRegion": "Dorsal Dentate Nucleus (Cerebellum)",
+      "associatedParcellation": "JuBrain Cytoarchitectonic Atlas",
+      "datasetInfo": "MNIColin27_PMap.json"
+    },
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+    "uuid": "c769c77f-bf7f-4dcc-8dea-17a8490c7de7"
+  },
+  {
+    "type": "Probabilistic cytoarchitectonic map",
+    "name": "Probabilistic cytoarchitectonic map of Ventral Dentate Nucleus (Cerebellum)",
+    "regionName": [
+      {
+        "regionName": "Ventral Dentate Nucleus (Cerebellum)",
+        "relationship": "equals"
+      }
+    ],
+    "files": [
+      {
+        "filename": "Cytoarchitectonic Probabilistic Map for Ventral Dentate Nucleus (Cerebellum)",
+        "name": "Cytoarchitectonic Probabilistic Map for Ventral Dentate Nucleus (Cerebellum)",
+        "mimetype": "application/nifti",
+        "url": "https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Ndentv.nii",
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+      }
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+        }
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+    "uuid": "58741d45-6d01-4263-9797-e5cd515e650f"
+  },
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+    "type": "Probabilistic cytoarchitectonic map",
+    "name": "Probabilistic cytoarchitectonic map of Fastigial Nucleus (Cerebellum)",
+    "regionName": [
+      {
+        "regionName": "Fastigial Nucleus (Cerebellum)",
+        "relationship": "equals"
+      }
+    ],
+    "files": [
+      {
+        "filename": "Cytoarchitectonic Probabilistic Map for Fastigial Nucleus (Cerebellum)",
+        "name": "Cytoarchitectonic Probabilistic Map for Fastigial Nucleus (Cerebellum)",
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+        "url": "https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Nfast.nii",
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+    ],
+    "targetParcellation": "JuBrain Cytoarchitectonic Atlas",
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+        }
+      ],
+      "associatedRegion": "Fastigial Nucleus (Cerebellum)",
+      "associatedParcellation": "JuBrain Cytoarchitectonic Atlas",
+      "datasetInfo": "MNIColin27_PMap.json"
+    },
+    "kgID": "Dataset/abdf1ae00cd05d8e53f167870ca1f97b",
+    "uuid": "01226dce-dd8a-484f-a683-f9cfc0a73504"
+  },
+  {
+    "type": "Probabilistic cytoarchitectonic map",
+    "name": "Probabilistic cytoarchitectonic map of Interposed Nucleus (Cerebellum)",
+    "regionName": [
+      {
+        "regionName": "emboliform nucleus, globose nucleus",
+        "relationship": "equals"
+      }
+    ],
+    "files": [
+      {
+        "filename": "Cytoarchitectonic Probabilistic Map for emboliform nucleus, globose nucleus",
+        "name": "Cytoarchitectonic Probabilistic Map for emboliform nucleus, globose nucleus",
+        "mimetype": "application/nifti",
+        "url": "https://neuroglancer.humanbrainproject.org/precomputed/JuBrain/v2.2c/PMaps/Cerebellum_Ninterp.nii",
+        "properties": {}
+      }
+    ],
+    "targetParcellation": "JuBrain Cytoarchitectonic Atlas",
+    "properties": {
+      "name": "Interposed Nucleus (Cerebellum)",
+      "description": "",
+      "publications": [
+        {
+          "doi": "https://doi.org/10.3389/fnana.2015.00054",
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+        }
+      ],
+      "associatedRegion": "Interposed Nucleus (Cerebellum)",
+      "associatedParcellation": "JuBrain Cytoarchitectonic Atlas",
+      "datasetInfo": "MNIColin27_PMap.json"
+    },
+    "kgID": "Dataset/c99a7efffacca3b364c05a7a9345839a",
+    "uuid": "33dcfd4a-09ba-493b-8025-3e7d63eea456"
+  }
+]
\ No newline at end of file
diff --git a/src/res/ext/test.json b/src/res/ext/test.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d9e02858246fb04e3872496b5f22426692d2cba
--- /dev/null
+++ b/src/res/ext/test.json
@@ -0,0 +1,14 @@
+{
+  "name": "test",
+  "type": "template",
+  "species": "Human",
+  "useTheme": "dark",
+  "ngId": "greyscale",
+  "nehubaConfigURL": "res/json/testNehubaConfig.json",
+  "parcellations": [
+  ],
+  "properties": {
+    "name": "test",
+    "description": "test description"
+  }
+}
\ No newline at end of file
diff --git a/src/res/ext/testNehubaConfig.json b/src/res/ext/testNehubaConfig.json
new file mode 100644
index 0000000000000000000000000000000000000000..2a42671d7a955b6300ccd372f1b496d702063cbd
--- /dev/null
+++ b/src/res/ext/testNehubaConfig.json
@@ -0,0 +1,66 @@
+{
+  "globals": {
+    "hideNullImageValues": true,
+    "useNehubaLayout": true,
+    "useNehubaMeshLayer": true,
+    "useCustomSegmentColors": true
+  },
+  "zoomWithoutCtrl": true,
+  "hideNeuroglancerUI": true,
+  "rightClickWithCtrl": true,
+  "rotateAtViewCentre": true,
+  "zoomAtViewCentre": true,
+  "enableMeshLoadingControl": true,
+  "layout": {
+    "useNehubaPerspective": {
+      "fixedZoomPerspectiveSlices": {
+        "sliceViewportWidth": 300,
+        "sliceViewportHeight": 300,
+        "sliceZoom": 724698.1843689409,
+        "sliceViewportSizeMultiplier": 2
+      },
+      "centerToOrigin": true,
+      "mesh": {
+        "removeBasedOnNavigation": true,
+        "flipRemovedOctant": true,
+        "surfaceParcellation": true
+      },
+      "removePerspectiveSlicesBackground": {
+        "mode": "=="
+      },
+      "waitForMesh": true,
+      "drawSubstrates": {
+        "color": [
+          0.5,
+          0.5,
+          1,
+          0.2
+        ]
+      },
+      "drawZoomLevels": {
+        "cutOff": 150000
+      },
+      "restrictZoomLevel": {
+        "minZoom": 2500000,
+        "maxZoom": 3500000
+      }
+    }
+  },
+  "dataset": {
+    "imageBackground": [
+      0,
+      0,
+      0,
+      1
+    ],
+    "initialNgState": {
+      "showDefaultAnnotations": false,
+      "layers": {
+        "greyscale": {
+          
+          "source": "dvid://http://dvid-dev.humanbrainproject.org:/5b666a3cc19344f29025c65e829f98f2/grayscale"
+        }
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/src/res/ext/waxholmRatV2_0.json b/src/res/ext/waxholmRatV2_0.json
index e5fc1c61c35773dc4ec6aa46a0277114fb9d04d4..c4cf6171da1c2200dc096d378f59dea0ac2e1e49 100644
--- a/src/res/ext/waxholmRatV2_0.json
+++ b/src/res/ext/waxholmRatV2_0.json
@@ -1 +1 @@
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diff --git a/src/res/images/receptor/hOc1_bm_kainate.jpg b/src/res/images/receptor/hOc1_bm_kainate.jpg
new file mode 100644
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diff --git a/src/res/images/receptor/hOc1_bm_mGluR2_3.jpg b/src/res/images/receptor/hOc1_bm_mGluR2_3.jpg
new file mode 100644
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diff --git a/src/services/auth.service.ts b/src/services/auth.service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..20a8f38d1fe204320dff0061d34d24497866783a
--- /dev/null
+++ b/src/services/auth.service.ts
@@ -0,0 +1,52 @@
+import { Injectable } from "@angular/core";
+
+const IV_REDIRECT_TOKEN = `IV_REDIRECT_TOKEN`
+
+@Injectable({
+  providedIn: 'root'
+})
+
+export class AuthService{
+  public user: User | null
+
+  public logoutHref: String = 'logout'
+
+  /**
+   * TODO build it dynamically, or at least possible to configure via env var
+   */
+  public loginMethods : AuthMethod[] = [{
+    name: 'HBP OIDC',
+    href: 'hbp-oidc/auth'
+  }]
+
+  constructor() {
+    fetch('user')
+      .then(res => res.json())
+      .then(user => this.user = user)
+      .catch(e => {
+        if (!PRODUCTION)
+          console.log(`auth failed`, e)
+      })
+  }
+
+  authSaveState() {
+    window.localStorage.setItem(IV_REDIRECT_TOKEN, window.location.href)
+  }
+
+  authReloadState() {
+
+    const redirect = window.localStorage.getItem(IV_REDIRECT_TOKEN)
+    window.localStorage.removeItem(IV_REDIRECT_TOKEN)
+    if (redirect) window.location.href = redirect
+  }
+}
+
+export interface User {
+  name: String
+  id: String
+}
+
+export interface AuthMethod{
+  href: String
+  name: String
+}
diff --git a/src/services/state/dataStore.store.ts b/src/services/state/dataStore.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d5064be7807bc280dd3f9c879d57b37a62503f28
--- /dev/null
+++ b/src/services/state/dataStore.store.ts
@@ -0,0 +1,141 @@
+import { Action } from '@ngrx/store'
+
+export function dataStore(state:any,action:DatasetAction){
+  switch (action.type){
+    case FETCHED_DATAENTRIES: {
+      return Object.assign({},state,{
+        fetchedDataEntries : action.fetchedDataEntries
+      })
+    }
+    case FETCHED_SPATIAL_DATA :{
+      return Object.assign({},state,{
+        fetchedSpatialData : action.fetchedDataEntries
+      })
+    }
+    case FETCHED_METADATA : {
+      return Object.assign({},state,{
+        fetchedMetadataMap : action.fetchedMetadataMap
+      })
+    }
+    default:
+      return state
+  }
+}
+
+export interface DatasetAction extends Action{
+  fetchedDataEntries : DataEntry[]
+  fetchedSpatialData : DataEntry[]
+  fetchedMetadataMap : Map<string,Map<string,{properties:Property}>>
+}
+
+export const FETCHED_DATAENTRIES = 'FETCHED_DATAENTRIES'
+export const FETCHED_METADATA = 'FETCHED_METADATA'
+export const FETCHED_SPATIAL_DATA = `FETCHED_SPATIAL_DATA`
+
+export interface Activity{
+  methods: string[]
+  preparation: string[]
+  protocols: string[  ]
+}
+
+export interface DataEntry{
+  activity: Activity[]
+  name: string
+  description: string
+  license: string[]
+  licenseInfo: string[]
+  parcellationRegion: ParcellationRegion[]
+  formats: string[]
+  custodians: string[]
+  contributors: string[]
+  referenceSpaces: ReferenceSpace[]
+  files : File[]
+  publications: Publication[]
+  embargoStatus: string[]
+
+  methods: string[]
+  protocols: string[]
+
+  preview?: boolean
+
+  /**
+   * TODO typo, should be kgReferences
+   */
+  kgReference: string[]
+}
+
+export interface ParcellationRegion {
+  id?: string
+  name: string
+}
+
+export interface ReferenceSpace {
+  name: string
+}
+
+export interface Publication{
+  name: string
+  doi : string
+  cite : string
+}
+
+export interface Property{
+  description : string
+  publications : Publication[]
+}
+
+export interface Landmark{
+  type : string //e.g. sEEG recording site, etc
+  name : string
+  templateSpace : string // possibily inherited from LandmarkBundle (?)
+  geometry : PointLandmarkGeometry | PlaneLandmarkGeometry | OtherLandmarkGeometry
+  properties : Property
+  files : File[]
+}
+
+export interface DataStateInterface{
+  fetchedDataEntries : DataEntry[]
+
+  /**
+   * Map that maps parcellation name to a Map, which maps datasetname to Property Object
+   */
+  fetchedMetadataMap : Map<string,Map<string,{properties:Property}>>
+}
+
+export interface PointLandmarkGeometry extends LandmarkGeometry{
+  position : [number, number, number]
+}
+
+export interface PlaneLandmarkGeometry extends LandmarkGeometry{
+  // corners have to be CW or CCW (no zigzag)
+  corners : [[number, number, number],[number, number, number],[number, number, number],[number, number, number]]
+}
+
+export interface OtherLandmarkGeometry extends LandmarkGeometry{
+  vertices: [number, number, number][]
+  meshIdx: [number,number,number][]
+}
+
+interface LandmarkGeometry{
+  type : 'point' | 'plane'
+  space? : 'voxel' | 'real'
+}
+
+export interface File{
+  name: string
+  absolutePath: string
+  byteSize: number
+  contentType: string,
+}
+
+export interface ViewerPreviewFile{
+  name: string
+  filename: string
+  mimetype: string
+  url?: string
+  data?: any
+}
+
+export interface FileSupplementData{
+  data: any
+}
\ No newline at end of file
diff --git a/src/services/state/ngViewerState.store.ts b/src/services/state/ngViewerState.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d1dd8b27a79daa1b2a73ad56df959cac71f87636
--- /dev/null
+++ b/src/services/state/ngViewerState.store.ts
@@ -0,0 +1,87 @@
+import { Action } from '@ngrx/store'
+
+export interface NgViewerStateInterface{
+  layers : NgLayerInterface[]
+  forceShowSegment : boolean | null
+  nehubaReady: boolean
+}
+
+export interface NgViewerAction extends Action{
+  layer : NgLayerInterface
+  forceShowSegment : boolean
+  nehubaReady: boolean
+}
+
+const defaultState:NgViewerStateInterface = {layers:[], forceShowSegment:null, nehubaReady: false}
+
+export function ngViewerState(prevState:NgViewerStateInterface = defaultState, action:NgViewerAction):NgViewerStateInterface{
+  switch(action.type){
+    case ADD_NG_LAYER:
+      return Object.assign({}, prevState, {
+        /* this configration hides the layer if a non mixable layer already present */
+        layers : action.layer.constructor === Array 
+          ? prevState.layers.concat(action.layer)
+          : prevState.layers.concat(
+              Object.assign({}, action.layer, 
+                action.layer.mixability === 'nonmixable' && prevState.layers.findIndex(l => l.mixability === 'nonmixable') >= 0
+                  ? {visible: false}
+                  : {}))
+        
+        /* this configuration does not the addition of multiple non mixable layers */
+        // layers : action.layer.mixability === 'nonmixable' && prevState.layers.findIndex(l => l.mixability === 'nonmixable') >= 0
+        //   ? prevState.layers
+        //   : prevState.layers.concat(action.layer)
+
+        /* this configuration allows the addition of multiple non mixables */
+        // layers : prevState.layers.map(l => mapLayer(l, action.layer)).concat(action.layer)
+      })
+    case REMOVE_NG_LAYER:
+      return Object.assign({}, prevState, {
+        layers : prevState.layers.filter(l => l.name !== action.layer.name)
+      } as NgViewerStateInterface)
+    case SHOW_NG_LAYER:
+      return Object.assign({}, prevState, {
+        layers : prevState.layers.map(l => l.name === action.layer.name
+          ? Object.assign({}, l, {
+              visible : true
+            } as NgLayerInterface)
+          : l)
+      })
+    case HIDE_NG_LAYER:
+      return Object.assign({}, prevState, {
+        layers : prevState.layers.map(l => l.name === action.layer.name
+          ? Object.assign({}, l, {
+              visible : false
+            } as NgLayerInterface)
+          : l)
+        })
+    case FORCE_SHOW_SEGMENT:
+      return Object.assign({}, prevState, {
+        forceShowSegment : action.forceShowSegment
+      }) as NgViewerStateInterface
+    case NEHUBA_READY: 
+      const { nehubaReady } = action
+      return {
+        ...prevState,
+        nehubaReady
+      }
+    default:
+      return prevState
+  }
+}
+
+export const ADD_NG_LAYER = 'ADD_NG_LAYER'
+export const REMOVE_NG_LAYER = 'REMOVE_NG_LAYER'
+export const SHOW_NG_LAYER = 'SHOW_NG_LAYER'
+export const HIDE_NG_LAYER = 'HIDE_NG_LAYER'
+export const FORCE_SHOW_SEGMENT = `FORCE_SHOW_SEGMENT`
+export const NEHUBA_READY = `NEHUBA_READY`
+
+interface NgLayerInterface{
+  name : string
+  source : string
+  mixability : string // base | mixable | nonmixable
+  visible : boolean
+  shader? : string
+  transform? : any
+}
\ No newline at end of file
diff --git a/src/services/state/pluginState.store.ts b/src/services/state/pluginState.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..93d6b1e9678128b624fbe146c26afb08f494306b
--- /dev/null
+++ b/src/services/state/pluginState.store.ts
@@ -0,0 +1,30 @@
+import { Action } from '@ngrx/store'
+
+
+export interface PluginInitManifestInterface{
+  initManifests : Map<string,string|null>
+}
+
+export interface PluginInitManifestActionInterface extends Action{
+  manifest: {
+    name : string,
+    initManifestUrl : string | null
+  }
+}
+
+export const ACTION_TYPES = {
+  SET_INIT_PLUGIN: `SET_INIT_PLUGIN`
+}
+
+export function pluginState(prevState:PluginInitManifestInterface = {initManifests : new Map()}, action:PluginInitManifestActionInterface):PluginInitManifestInterface{
+  switch(action.type){
+    case ACTION_TYPES.SET_INIT_PLUGIN:
+      const newMap = new Map(prevState.initManifests )
+      return {
+        ...prevState,
+        initManifests: newMap.set(action.manifest.name, action.manifest.initManifestUrl)
+      }
+    default:
+      return prevState
+  }
+}
diff --git a/src/services/state/spatialSearchState.store.ts b/src/services/state/spatialSearchState.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..cdc34bf99f82e1a6c4d72f842114a0c2f5e841ec
--- /dev/null
+++ b/src/services/state/spatialSearchState.store.ts
@@ -0,0 +1,35 @@
+import { Action } from '@ngrx/store'
+
+export function spatialSearchState(state:SpatialDataStateInterface = initSpatialDataState, action:SpatialDataEntries){
+  switch (action.type){
+    case SPATIAL_GOTO_PAGE:
+      return Object.assign({},state,{
+        spatialSearchPagination : action.pageNo
+      })
+    case UPDATE_SPATIAL_DATA:
+      return Object.assign({},state,{
+        spatialSearchTotalResults : action.totalResults
+      })
+    default :
+      return state
+  }
+}
+
+export interface SpatialDataStateInterface{
+  spatialSearchPagination : number
+  spatialSearchTotalResults : number
+}
+
+const initSpatialDataState : SpatialDataStateInterface = {
+  spatialSearchPagination : 0, 
+  spatialSearchTotalResults : 0
+}
+
+export interface SpatialDataEntries extends Action{
+  pageNo? : number
+  totalResults? : number
+  visible? : boolean
+}
+
+export const SPATIAL_GOTO_PAGE = `SPATIAL_GOTO_PAGE`
+export const UPDATE_SPATIAL_DATA = `UPDATE_SPATIAL_DATA`
diff --git a/src/services/state/uiState.store.ts b/src/services/state/uiState.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..fadd5b87fcc506e504ff011a429675648d85c303
--- /dev/null
+++ b/src/services/state/uiState.store.ts
@@ -0,0 +1,118 @@
+import { Action } from '@ngrx/store'
+
+const agreedCookieKey = 'agreed-cokies'
+const aggredKgTosKey = 'agreed-kg-tos'
+
+const defaultState : UIStateInterface = {
+  mouseOverSegments: [],
+  mouseOverSegment: null,
+  mouseOverLandmark: null,
+  focusedSidePanel: null,
+  sidePanelOpen: false,
+
+  /**
+   * replace with server side logic (?)
+   */
+  agreedCookies: localStorage.getItem(agreedCookieKey) === 'agreed',
+  agreedKgTos: localStorage.getItem(aggredKgTosKey) === 'agreed'
+}
+
+export function uiState(state:UIStateInterface = defaultState,action:UIAction){
+  switch(action.type){
+    case MOUSE_OVER_SEGMENTS:
+      const { segments } = action
+      return {
+        ...state,
+        mouseOverSegments: segments
+      }
+    case MOUSE_OVER_SEGMENT:
+      return {
+        ...state,
+        mouseOverSegment : action.segment
+      }
+    case MOUSE_OVER_LANDMARK:
+      return {
+        ...state,
+        mouseOverLandmark : action.landmark
+      }
+    /**
+     * TODO deprecated
+     * remove ASAP
+     */
+    case TOGGLE_SIDE_PANEL:
+      return {
+        ...state,
+        sidePanelOpen: !state.sidePanelOpen
+      }
+    case OPEN_SIDE_PANEL:
+      return {
+        ...state,
+        sidePanelOpen: true
+      }
+    case CLOSE_SIDE_PANEL:
+      return {
+        ...state,
+        sidePanelOpen: false
+      }
+    case AGREE_COOKIE:
+      /**
+       * TODO replace with server side logic
+       */
+      localStorage.setItem(agreedCookieKey, 'agreed')
+      return {
+        ...state,
+        agreedCookies: true
+      }
+    case AGREE_KG_TOS:
+      /**
+       * TODO replace with server side logic
+       */
+      localStorage.setItem(aggredKgTosKey, 'agreed')
+      return {
+        ...state,
+        agreedKgTos: true
+      }
+    default:
+      return state
+  }
+}
+
+export interface UIStateInterface{
+  mouseOverSegments: {
+    layer: {
+      name: string
+    }
+    segment: any | null
+  }[]
+  sidePanelOpen : boolean
+  mouseOverSegment : any | number
+  mouseOverLandmark : any 
+  focusedSidePanel : string | null
+
+  agreedCookies: boolean
+  agreedKgTos: boolean
+}
+
+export interface UIAction extends Action{
+  segment: any | number
+  landmark: any
+  focusedSidePanel? : string
+  segments?:{
+    layer: {
+      name: string
+    }
+    segment: any | null
+  }[]
+}
+
+export const MOUSE_OVER_SEGMENT = `MOUSE_OVER_SEGMENT`
+export const MOUSE_OVER_SEGMENTS = `MOUSE_OVER_SEGMENTS`
+export const MOUSE_OVER_LANDMARK = `MOUSE_OVER_LANDMARK`
+
+export const TOGGLE_SIDE_PANEL = 'TOGGLE_SIDE_PANEL'
+export const CLOSE_SIDE_PANEL = `CLOSE_SIDE_PANEL`
+export const OPEN_SIDE_PANEL = `OPEN_SIDE_PANEL`
+
+export const AGREE_COOKIE = `AGREE_COOKIE`
+export const AGREE_KG_TOS = `AGREE_KG_TOS`
+export const SHOW_KG_TOS = `SHOW_KG_TOS`
\ No newline at end of file
diff --git a/src/services/state/viewerConfig.store.ts b/src/services/state/viewerConfig.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..eb634fee9dbeda27a7e5915258048bd43b93c885
--- /dev/null
+++ b/src/services/state/viewerConfig.store.ts
@@ -0,0 +1,54 @@
+import { Action } from "@ngrx/store";
+
+export interface ViewerConfiguration{
+  gpuLimit: number
+  animation: boolean
+}
+
+interface ViewerConfigurationAction extends Action{
+  config: Partial<ViewerConfiguration>,
+  payload: any
+}
+
+export const CONFIG_CONSTANTS = {
+  /**
+   * byets
+   */
+  gpuLimitMin: 1e8, 
+  gpuLimitMax: 1e9,
+  defaultGpuLimit: 1e9,
+  defaultAnimation: true
+}
+
+export const ACTION_TYPES = {
+  UPDATE_CONFIG: `UPDATE_CONFIG`,
+  CHANGE_GPU_LIMIT: `CHANGE_GPU_LIMIT`
+}
+
+const lsGpuLimit = localStorage.getItem('iv-gpulimit')
+const gpuLimit = lsGpuLimit && !isNaN(Number(lsGpuLimit))
+  ? Number(lsGpuLimit)
+  : CONFIG_CONSTANTS.defaultGpuLimit
+
+export function viewerConfigState(prevState:ViewerConfiguration = {animation: CONFIG_CONSTANTS.defaultAnimation, gpuLimit}, action:ViewerConfigurationAction) {
+  switch (action.type) {
+    case ACTION_TYPES.UPDATE_CONFIG:
+      return {
+        ...prevState,
+        ...action.config
+      }
+    case ACTION_TYPES.CHANGE_GPU_LIMIT:
+      const newGpuLimit = Math.min(
+        CONFIG_CONSTANTS.gpuLimitMax,
+        Math.max(
+          (prevState.gpuLimit || CONFIG_CONSTANTS.defaultGpuLimit) + action.payload.delta,
+          CONFIG_CONSTANTS.gpuLimitMin
+        ))
+      return {
+        ...prevState,
+        gpuLimit: newGpuLimit
+      }
+    default:
+      return prevState
+  }
+}
\ No newline at end of file
diff --git a/src/services/state/viewerState.store.ts b/src/services/state/viewerState.store.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e694c1fe0b8e3650715cdde3a8cb967cb1aa66a0
--- /dev/null
+++ b/src/services/state/viewerState.store.ts
@@ -0,0 +1,171 @@
+import { Action } from '@ngrx/store'
+import { UserLandmark } from 'src/atlasViewer/atlasViewer.apiService.service';
+import { getNgIdLabelIndexFromId, generateLabelIndexId, recursiveFindRegionWithLabelIndexId, propagateNgId } from '../stateStore.service';
+
+export interface ViewerStateInterface{
+  fetchedTemplates : any[]
+
+  templateSelected : any | null
+  parcellationSelected : any | null
+  regionsSelected : any[]
+
+  landmarksSelected : any[]
+  userLandmarks : UserLandmark[]
+
+  navigation : any | null
+  dedicatedView : string[]
+}
+
+export interface AtlasAction extends Action{
+  fetchedTemplate? : any[]
+
+  selectTemplate? : any
+  selectParcellation? : any
+  selectRegions?: any[]
+  selectRegionIds: string[]
+  deselectRegions? : any[]
+  dedicatedView? : string
+
+  landmarks : UserLandmark[]
+  deselectLandmarks : UserLandmark[]
+
+  navigation? : any
+}
+
+export function viewerState(
+  state:Partial<ViewerStateInterface> = {
+    landmarksSelected : [],
+    fetchedTemplates : []
+  },
+  action:AtlasAction
+){
+  switch(action.type){
+    /**
+     * TODO may be obsolete. test when nifti become available
+     */
+    case LOAD_DEDICATED_LAYER:
+      const dedicatedView = state.dedicatedView
+        ? state.dedicatedView.concat(action.dedicatedView)
+        : [action.dedicatedView]
+      return {
+        ...state,
+        dedicatedView 
+      }
+    case UNLOAD_DEDICATED_LAYER:
+      return {
+        ...state,
+        dedicatedView : state.dedicatedView
+          ? state.dedicatedView.filter(dv => dv !== action.dedicatedView)
+          : []
+      }
+    case NEWVIEWER:
+      const { selectParcellation } = action
+      const parcellation = propagateNgId( selectParcellation )
+      return {
+        ...state,
+        templateSelected : action.selectTemplate,
+        parcellationSelected : parcellation,
+        regionsSelected : [],
+        landmarksSelected : [],
+        navigation : {},
+        dedicatedView : null
+      }
+    case FETCHED_TEMPLATE : {
+      return {
+        ...state,
+        fetchedTemplates: state.fetchedTemplates.concat(action.fetchedTemplate)
+      }
+    }
+    case CHANGE_NAVIGATION : {
+      return {
+        ...state,
+        navigation : action.navigation
+      }
+    }
+    case SELECT_PARCELLATION : {
+      const { selectParcellation } = action
+      const parcellation = propagateNgId( selectParcellation )
+      return {
+        ...state,
+        parcellationSelected: parcellation,
+        regionsSelected: []
+      }
+    }
+    case SELECT_REGIONS:
+      const { selectRegions } = action
+      return {
+        ...state,
+        regionsSelected: selectRegions
+      }
+    case SELECT_REGIONS_WITH_ID : {
+      const { parcellationSelected } = state
+      const { selectRegionIds } = action
+      const { ngId: defaultNgId } = parcellationSelected
+
+      /**
+       * for backwards compatibility.
+       * older versions of atlas viewer may only have labelIndex as region identifier
+       */
+      const regionsSelected = selectRegionIds
+        .map(labelIndexId => getNgIdLabelIndexFromId({ labelIndexId }))
+        .map(({ ngId, labelIndex }) => {
+          return {
+            labelIndexId: generateLabelIndexId({
+              ngId: ngId || defaultNgId,
+              labelIndex 
+            })
+          }
+        })
+        .map(({ labelIndexId }) => {
+          return recursiveFindRegionWithLabelIndexId({ 
+            regions: parcellationSelected.regions,
+            labelIndexId,
+            inheritedNgId: defaultNgId
+          })
+        })
+        .filter(v => {
+          if (!v) console.log(`SELECT_REGIONS_WITH_ID, some ids cannot be parsed intto label index`)
+          return !!v
+        })
+      return {
+        ...state,
+        regionsSelected
+      }
+    }
+    case DESELECT_LANDMARKS : {
+      return {
+        ...state,
+        landmarksSelected : state.landmarksSelected.filter(lm => action.deselectLandmarks.findIndex(dLm => dLm.name === lm.name) < 0)
+      }
+    }
+    case SELECT_LANDMARKS : {
+      return {
+        ...state,
+        landmarksSelected : action.landmarks
+      }
+    }
+    case USER_LANDMARKS : {
+      return {
+        ...state,
+        userLandmarks: action.landmarks
+      } 
+    }
+    default :
+      return state
+  }
+}
+
+export const LOAD_DEDICATED_LAYER = 'LOAD_DEDICATED_LAYER'
+export const UNLOAD_DEDICATED_LAYER = 'UNLOAD_DEDICATED_LAYER'
+
+export const NEWVIEWER = 'NEWVIEWER'
+
+export const FETCHED_TEMPLATE = 'FETCHED_TEMPLATE'
+export const CHANGE_NAVIGATION = 'CHANGE_NAVIGATION'
+
+export const SELECT_PARCELLATION = `SELECT_PARCELLATION`
+export const SELECT_REGIONS = `SELECT_REGIONS`
+export const SELECT_REGIONS_WITH_ID = `SELECT_REGIONS_WITH_ID`
+export const SELECT_LANDMARKS = `SELECT_LANDMARKS`
+export const DESELECT_LANDMARKS = `DESELECT_LANDMARKS`
+export const USER_LANDMARKS = `USER_LANDMARKS`
diff --git a/src/services/stateStore.service.ts b/src/services/stateStore.service.ts
index 7ea4f72507c4898f9e2bf7f88f9a41508c042e96..5bcd42ed9acd371ec4f5bc1ce223308ee44bdc9f 100644
--- a/src/services/stateStore.service.ts
+++ b/src/services/stateStore.service.ts
@@ -1,388 +1,87 @@
-import { Action } from '@ngrx/store'
 import { filter } from 'rxjs/operators';
-import { UserLandmark } from '../atlasViewer/atlasViewer.apiService.service';
 
-export const NEWVIEWER = 'NEWVIEWER'
+export { viewerConfigState } from './state/viewerConfig.store'
+export { pluginState } from './state/pluginState.store'
+export { NgViewerAction, NgViewerStateInterface, ngViewerState, ADD_NG_LAYER, FORCE_SHOW_SEGMENT, HIDE_NG_LAYER, REMOVE_NG_LAYER, SHOW_NG_LAYER } from './state/ngViewerState.store'
+export { CHANGE_NAVIGATION, AtlasAction, DESELECT_LANDMARKS, FETCHED_TEMPLATE, NEWVIEWER, SELECT_LANDMARKS, SELECT_PARCELLATION, SELECT_REGIONS, USER_LANDMARKS, ViewerStateInterface, viewerState } from './state/viewerState.store'
+export { DataEntry, ParcellationRegion, DataStateInterface, DatasetAction, FETCHED_DATAENTRIES, FETCHED_METADATA, FETCHED_SPATIAL_DATA, Landmark, OtherLandmarkGeometry, PlaneLandmarkGeometry, PointLandmarkGeometry, Property, Publication, ReferenceSpace, dataStore, File, FileSupplementData } from './state/dataStore.store'
+export { CLOSE_SIDE_PANEL, MOUSE_OVER_LANDMARK, MOUSE_OVER_SEGMENT, OPEN_SIDE_PANEL, TOGGLE_SIDE_PANEL, UIAction, UIStateInterface, uiState } from './state/uiState.store'
+export { SPATIAL_GOTO_PAGE, SpatialDataEntries, SpatialDataStateInterface, UPDATE_SPATIAL_DATA, spatialSearchState } from './state/spatialSearchState.store'
 
-export const FETCHED_TEMPLATE = 'FETCHED_TEMPLATE'
-export const SELECT_PARCELLATION = `SELECT_PARCELLATION`
-export const SELECT_REGIONS = `SELECT_REGIONS`
-export const DESELECT_REGIONS = `DESELECT_REGIONS`
-export const SELECT_LANDMARKS = `SELECT_LANDMARKS`
-export const DESELECT_LANDMARKS = `DESELECT_LANDMARKS`
-export const USER_LANDMARKS = `USER_LANDMARKS`
-
-export const CHANGE_NAVIGATION = 'CHANGE_NAVIGATION'
-
-export const FETCHED_DATAENTRIES = 'FETCHED_DATAENTRIES'
-export const FETCHED_METADATA = 'FETCHED_METADATA'
-export const FETCHED_SPATIAL_DATA = `FETCHED_SPATIAL_DATA`
-
-export const LOAD_DEDICATED_LAYER = 'LOAD_DEDICATED_LAYER'
-export const UNLOAD_DEDICATED_LAYER = 'UNLOAD_DEDICATED_LAYER'
-
-export const SPATIAL_GOTO_PAGE = `SPATIAL_GOTO_PAGE`
-export const UPDATE_SPATIAL_DATA = `UPDATE_SPATIAL_DATA`
-export const UPDATE_SPATIAL_DATA_VISIBLE = `UPDATE_SPATIAL_DATA_VISIBLE `
-
-export const TOGGLE_SIDE_PANEL = 'TOGGLE_SIDE_PANEL'
-export const CLOSE_SIDE_PANEL = `CLOSE_SIDE_PANEL`
-export const OPEN_SIDE_PANEL = `OPEN_SIDE_PANEL`
-
-export const MOUSE_OVER_SEGMENT = `MOUSE_OVER_SEGMENT`
-export const MOUSE_OVER_LANDMARK = `MOUSE_OVER_LANDMARK`
-
-export const SET_INIT_PLUGIN = `SET_INIT_PLUGIN`
-export const FETCHED_PLUGIN_MANIFESTS = `FETCHED_PLUGIN_MANIFESTS`
-export const LAUNCH_PLUGIN = `LAUNCH_PLUGIN`
-
-export interface PluginInitManifestInterface{
-  initManifests : Map<string,string|null>
-}
-
-export interface PluginInitManifestActionInterface extends Action{
-  manifest: {
-    name : string,
-    initManifestUrl : string | null
-  }
-}
-
-export interface ViewerStateInterface{
-  fetchedTemplates : any[]
-
-  templateSelected : any | null
-  parcellationSelected : any | null
-  regionsSelected : any[]
-
-  landmarksSelected : any[]
-  userLandmarks : UserLandmark[]
-
-  navigation : any | null
-  dedicatedView : string[]
-}
-
-export interface AtlasAction extends Action{
-  fetchedTemplate? : any[]
-
-  selectTemplate? : any
-  selectParcellation? : any
-  selectRegions? : any[]
-  deselectRegions? : any[]
-  dedicatedView? : string
-
-  landmarks : UserLandmark[]
-  deselectLandmarks : UserLandmark[]
-
-  navigation? : any
-}
-
-export interface NewViewerAction extends Action{
-  selectTemplate : any,
-  selectParcellation :any
-}
-
-export interface DatasetAction extends Action{
-  fetchedDataEntries : DataEntry[]
-  fetchedSpatialData : DataEntry[]
-  fetchedMetadataMap : Map<string,Map<string,{properties:Property}>>
-}
-
-export interface DataStateInterface{
-  fetchedDataEntries : DataEntry[]
-
-  /**
-   * Map that maps parcellation name to a Map, which maps datasetname to Property Object
-   */
-  fetchedMetadataMap : Map<string,Map<string,{properties:Property}>>
-}
-
-interface NgLayerInterface{
-  name : string
-  source : string
-  mixability : string // base | mixable | nonmixable
-  visible : boolean
-  shader? : string
-  transform? : any
-}
-
-export const ADD_NG_LAYER = 'ADD_NG_LAYER'
-export const REMOVE_NG_LAYER = 'REMOVE_NG_LAYER'
-export const SHOW_NG_LAYER = 'SHOW_NG_LAYER'
-export const HIDE_NG_LAYER = 'HIDE_NG_LAYER'
-export const FORCE_SHOW_SEGMENT = `FORCE_SHOW_SEGMENT`
-
-export interface NgViewerStateInterface{
-  layers : NgLayerInterface[]
-  forceShowSegment : boolean | null
-}
-
-export interface NgViewerAction extends Action{
-  layer : NgLayerInterface
-  forceShowSegment : boolean
-}
-
-const mapLayer = (existingLayer:NgLayerInterface, incomingLayer:NgLayerInterface):NgLayerInterface => {
-  return incomingLayer.mixability === 'base'
-    ? existingLayer
-    : incomingLayer.mixability === 'mixable'
-      ? existingLayer.mixability === 'nonmixable'
-        ? Object.assign({}, existingLayer, {
-            visible : false
-          } as NgLayerInterface)
-        : existingLayer
-      /* incomingLayer.mixability === 'nonmixable' */
-      : existingLayer.mixability === 'base'
-        ? existingLayer
-        : Object.assign({}, existingLayer, {
-            visible : false
-          } as NgLayerInterface)
-}
-
-export function pluginState(prevState:PluginInitManifestInterface = {initManifests : new Map()}, action:PluginInitManifestActionInterface):PluginInitManifestInterface{
-  switch(action.type){
-    case SET_INIT_PLUGIN:
-      const newMap = new Map(prevState.initManifests)
-      return Object.assign({}, prevState, {
-        initManifests : newMap.set(action.manifest.name, action.manifest.initManifestUrl)
-      } as PluginInitManifestInterface)
-    default:
-      return prevState
-  }
-}
-
-export function ngViewerState(prevState:NgViewerStateInterface = {layers:[], forceShowSegment:null}, action:NgViewerAction):NgViewerStateInterface{
-  switch(action.type){
-    case ADD_NG_LAYER:
-      return Object.assign({}, prevState, {
-        /* this configration hides the layer if a non mixable layer already present */
-        layers : action.layer.constructor === Array 
-          ? prevState.layers.concat(action.layer)
-          : prevState.layers.concat(
-              Object.assign({}, action.layer, 
-                action.layer.mixability === 'nonmixable' && prevState.layers.findIndex(l => l.mixability === 'nonmixable') >= 0
-                  ? {visible: false}
-                  : {}))
-        
-        /* this configuration does not the addition of multiple non mixable layers */
-        // layers : action.layer.mixability === 'nonmixable' && prevState.layers.findIndex(l => l.mixability === 'nonmixable') >= 0
-        //   ? prevState.layers
-        //   : prevState.layers.concat(action.layer)
-
-        /* this configuration allows the addition of multiple non mixables */
-        // layers : prevState.layers.map(l => mapLayer(l, action.layer)).concat(action.layer)
-      })
-    case REMOVE_NG_LAYER:
-      return Object.assign({}, prevState, {
-        layers : prevState.layers.filter(l => l.name !== action.layer.name)
-      } as NgViewerStateInterface)
-    case SHOW_NG_LAYER:
-      return Object.assign({}, prevState, {
-        layers : prevState.layers.map(l => l.name === action.layer.name
-          ? Object.assign({}, l, {
-              visible : true
-            } as NgLayerInterface)
-          : l)
-      })
-    case HIDE_NG_LAYER:
-      return Object.assign({}, prevState, {
-        layers : prevState.layers.map(l => l.name === action.layer.name
-          ? Object.assign({}, l, {
-              visible : false
-            } as NgLayerInterface)
-          : l)
-        })
-    case FORCE_SHOW_SEGMENT:
-      return Object.assign({}, prevState, {
-        forceShowSegment : action.forceShowSegment
-      }) as NgViewerStateInterface
-    default:
-      return prevState
-  }
+export function safeFilter(key:string){
+  return filter((state:any)=>
+    (typeof state !== 'undefined' && state !== null) &&
+    typeof state[key] !== 'undefined' && state[key] !== null) 
 }
 
-export function uiState(state:UIStateInterface = {mouseOverSegment:null, mouseOverLandmark : null, focusedSidePanel:null, sidePanelOpen: false},action:UIAction){
-  switch(action.type){
-    case MOUSE_OVER_SEGMENT:
-      return Object.assign({},state,{
-        mouseOverSegment : action.segment
-      })
-    case MOUSE_OVER_LANDMARK:
-      return Object.assign({}, state, {
-        mouseOverLandmark : action.landmark
-      })
-    case TOGGLE_SIDE_PANEL:
-      return Object.assign({}, state, {
-        focusedSidePanel : typeof action.focusedSidePanel  === 'undefined' || state.focusedSidePanel === action.focusedSidePanel
-          ? null
-          : action.focusedSidePanel, 
-        sidePanelOpen : !(typeof action.focusedSidePanel  === 'undefined' || state.focusedSidePanel === action.focusedSidePanel)
-      } as Partial<UIStateInterface>)
-    case OPEN_SIDE_PANEL :
-      return Object.assign({},state,{
-        sidePanelOpen : true
-      })
-    case CLOSE_SIDE_PANEL :
-      return Object.assign({},state,{
-        sidePanelOpen : false
-      })
-    default :
-      return state
+export function extractLabelIdx(region:any):number[]{
+  if(!region.children || region.children.constructor !== Array){
+    return isNaN(region.labelIndex) || region.labelIndex === null
+      ? []
+      : [Number(region.labelIndex)]
   }
+  return region.children.reduce((acc,item)=>{
+    return acc.concat(extractLabelIdx(item))
+  },[]).concat( region.labelIndex ? Number(region.labelIndex) : [] )
 }
 
-export function viewerState(
-  state:Partial<ViewerStateInterface> = {
-    landmarksSelected : [],
-    fetchedTemplates : []
-  },
-  action:AtlasAction
-){
-  switch(action.type){
-    case LOAD_DEDICATED_LAYER:
-      const dedicatedView = state.dedicatedView
-        ? state.dedicatedView.concat(action.dedicatedView)
-        : [action.dedicatedView]
-      return Object.assign({},state,{
-        dedicatedView 
-      })
-    case UNLOAD_DEDICATED_LAYER:
-      return Object.assign({},state,{
-        dedicatedView : state.dedicatedView
-          ? state.dedicatedView.filter(dv => dv !== action.dedicatedView)
-          : []
-      })
-    case NEWVIEWER:
-      return Object.assign({},state,{
-        templateSelected : action.selectTemplate,
-        parcellationSelected : action.selectParcellation,
-        regionsSelected : [],
-        landmarksSelected : [],
-        navigation : {},
-        dedicatedView : null
-      })
-    case FETCHED_TEMPLATE : {
-      return Object.assign({}, state, {
-        fetchedTemplates: state.fetchedTemplates.concat(action.fetchedTemplate)
-      })
-    }
-    case CHANGE_NAVIGATION : {
-      return Object.assign({},state,{navigation : action.navigation})
-    }
-    case SELECT_PARCELLATION : {
-      return Object.assign({},state,{
-        parcellationSelected : action.selectParcellation,
-        regionsSelected : []
-      })
-    }
-    case DESELECT_REGIONS : {
-      return Object.assign({}, state, {
-        regionsSelected : state.regionsSelected.filter(re => action.deselectRegions.findIndex(dRe => dRe.name === re.name) < 0)
-      })
-    }
-    case SELECT_REGIONS : {
-      return Object.assign({},state,{
-        regionsSelected : action.selectRegions.map(region=>Object.assign({},region,{
-          labelIndex : Number(region.labelIndex)
+const inheritNgId = (region:any) => {
+  const {ngId = 'root', children = []} = region
+  return {
+    ngId,
+    ...region,
+    ...(children && children.map
+      ? {
+        children: children.map(c => inheritNgId({
+          ngId,
+          ...c
         }))
-      })
-    }
-    case DESELECT_LANDMARKS : {
-      return Object.assign({}, state, {
-        landmarksSelected : state.landmarksSelected.filter(lm => action.deselectLandmarks.findIndex(dLm => dLm.name === lm.name) < 0)
-      })
-    }
-    case SELECT_LANDMARKS : {
-      return Object.assign({}, state, {
-        landmarksSelected : action.landmarks
-      })
-    }
-    case USER_LANDMARKS : {
-      return Object.assign({}, state, {
-        userLandmarks : action.landmarks
-      })
-    }
-    default :
-      return state
+      }
+      : {})
   }
 }
 
-export function dataStore(state:any,action:DatasetAction){
-  switch (action.type){
-    case FETCHED_DATAENTRIES: {
-      return Object.assign({},state,{
-        fetchedDataEntries : action.fetchedDataEntries
-      })
-    }
-    case FETCHED_SPATIAL_DATA :{
-      return Object.assign({},state,{
-        fetchedSpatialData : action.fetchedDataEntries
-      })
+export function getMultiNgIdsRegionsLabelIndexMap(parcellation: any = {}):Map<string,Map<number, any>>{
+  const map:Map<string,Map<number, any>> = new Map()
+  const { ngId = 'root'} = parcellation
+
+  const processRegion = (region:any) => {
+    const { ngId } = region
+    const existingMap = map.get(ngId)
+    const labelIndex = Number(region.labelIndex)
+    if (labelIndex) {
+      if (!existingMap) {
+        const newMap = new Map()
+        newMap.set(labelIndex, region)
+        map.set(ngId, newMap)
+      } else {
+        existingMap.set(labelIndex, region)
+      }
     }
-    case FETCHED_METADATA : {
-      return Object.assign({},state,{
-        fetchedMetadataMap : action.fetchedMetadataMap
+
+    if (region.children && region.children.forEach) {
+      region.children.forEach(child => {
+        processRegion({
+          ngId,
+          ...child
+        })
       })
     }
-    default:
-      return state
   }
-}
-
-export interface SpatialDataEntries extends Action
-{
-  pageNo? : number
-  totalResults? : number
-  visible? : boolean
-}
-
-export interface SpatialDataStateInterface{
-  spatialSearchPagination : number
-  spatialSearchTotalResults : number
-  spatialDataVisible : boolean
-}
-
-const initSpatialDataState : SpatialDataStateInterface = {
-  spatialDataVisible : true,
-  spatialSearchPagination : 0, 
-  spatialSearchTotalResults : 0
-}
 
-export function spatialSearchState(state:SpatialDataStateInterface = initSpatialDataState, action:SpatialDataEntries){
-  switch (action.type){
-    case SPATIAL_GOTO_PAGE:
-      return Object.assign({},state,{
-        spatialSearchPagination : action.pageNo
-      })
-    case UPDATE_SPATIAL_DATA:
-      return Object.assign({},state,{
-        spatialSearchTotalResults : action.totalResults
-      })
-    case UPDATE_SPATIAL_DATA_VISIBLE:
-      return Object.assign({},state,{
-        spatialDataVisible : action.visible
-      })
-    default :
-      return state
+  if (parcellation && parcellation.regions && parcellation.regions.forEach) {
+    parcellation.regions.forEach(r => processRegion({
+      ngId,
+      ...r
+    }))
   }
-}
-
-export function safeFilter(key:string){
-  return filter((state:any)=>
-    (typeof state !== 'undefined' && state !== null) &&
-    typeof state[key] !== 'undefined' && state[key] !== null) 
-}
 
-export function extractLabelIdx(region:any):number[]{
-  if(!region.children || region.children.constructor !== Array){
-    return isNaN(region.labelIndex) || region.labelIndex === null
-      ? []
-      : [Number(region.labelIndex)]
-  }
-  return region.children.reduce((acc,item)=>{
-    return acc.concat(extractLabelIdx(item))
-  },[]).concat( region.labelIndex ? Number(region.labelIndex) : [] )
+  return map
 }
 
+/**
+ * labelIndexMap maps label index to region
+ */
 export function getLabelIndexMap(regions:any[]):Map<number,any>{
   const returnMap = new Map()
 
@@ -396,92 +95,79 @@ export function getLabelIndexMap(regions:any[]):Map<number,any>{
 
   reduceRegions(regions)
   return returnMap
-} 
-
-export interface DedicatedViewState{
-  dedicatedView : string | null
-}
-
-export interface DedicatedViewAction extends Action{
-  dedicatedView : string | null
-}
-
-export interface DataEntry{
-  type : string
-  name : string
-  kgID? : string
-  regionName : {
-    regionName : string,
-    relationship : string
-  }[]
-
-  properties : Property
-
-  files : File[]
-}
-
-export interface File{
-  filename : string
-  name : string
-  mimetype : string
-  url? : string
-  data? : any
-  kgID? : string
-  targetParcellation : string
-  properties : any
-}
-
-export interface Property{
-  description : string
-  publications : Publication[]
 }
 
-export interface Publication{
-  doi : string
-  citation : string
+export function getNgIds(regions: any[]): string[]{
+  return regions && regions.map
+    ? regions
+        .map(r => [r.ngId, ...getNgIds(r.children)])
+        .reduce((acc, item) => acc.concat(item), [])
+        .filter(ngId => !!ngId)
+    : []
 }
 
-export interface UIStateInterface{
-  sidePanelOpen : boolean
-  mouseOverSegment : any | number
-  mouseOverLandmark : any 
-  focusedSidePanel : string | null
-}
-
-export interface UIAction extends Action{
-  segment : any | number
-  landmark : any
-  focusedSidePanel? : string
-}
-
-export interface Landmark{
-  type : string //e.g. sEEG recording site, etc
-  name : string
-  templateSpace : string // possibily inherited from LandmarkBundle (?)
-  geometry : PointLandmarkGeometry | PlaneLandmarkGeometry | OtherLandmarkGeometry
-  properties : Property
-  files : File[]
-}
-
-export interface LandmarkGeometry{
-  type : 'point' | 'plane'
-  space? : 'voxel' | 'real'
-}
-
-export interface PointLandmarkGeometry extends LandmarkGeometry{
-  position : [number, number, number]
+export interface DedicatedViewState{
+  dedicatedView : string | null
 }
 
-export interface PlaneLandmarkGeometry extends LandmarkGeometry{
-  // corners have to be CW or CCW (no zigzag)
-  corners : [[number, number, number],[number, number, number],[number, number, number],[number, number, number]]
+export function isDefined(obj){
+  return typeof obj !== 'undefined' && obj !== null
 }
 
-export interface OtherLandmarkGeometry extends LandmarkGeometry{
-  vertices: [number, number, number][]
-  meshIdx: [number,number,number][]
-}
+export function generateLabelIndexId({ ngId, labelIndex }) {
+  return `${ngId}#${labelIndex}`
+}
+
+export function getNgIdLabelIndexFromId({ labelIndexId } = {labelIndexId: ''}) {
+  const _ = labelIndexId && labelIndexId.split && labelIndexId.split('#') || []
+  const ngId = _.length > 1
+    ? _[0]
+    : null
+  const labelIndex = _.length > 1
+    ? Number(_[1])
+    : _.length === 0
+      ? null
+      : Number(_[0])
+  return { ngId, labelIndex }
+}
+
+const recursiveFlatten = (region, {ngId}) => {
+  return [{
+    ngId,
+    ...region
+  }].concat(
+    ...((region.children && region.children.map && region.children.map(c => recursiveFlatten(c, { ngId : region.ngId || ngId })) )|| [])
+  )
+}
+
+export function recursiveFindRegionWithLabelIndexId({ regions, labelIndexId, inheritedNgId = 'root' }: {regions: any[], labelIndexId: string, inheritedNgId:string}) {
+  const { ngId, labelIndex } = getNgIdLabelIndexFromId({ labelIndexId })
+  const fr1 = regions.map(r => recursiveFlatten(r,{ ngId: inheritedNgId }))
+  const fr2 = fr1.reduce((acc, curr) => acc.concat(...curr), [])
+  const found = fr2.find(r => r.ngId === ngId && Number(r.labelIndex) === Number(labelIndex))
+  if (found) return found
+  debugger
+  return null
+}
+
+export function propagateNgId(parcellation) {
+  const recursivePropagateNgId = (region, {ngId}) => {
+    return {
+      ngId,
+      ...region,
+      ...( region.children && region.children.map
+        ? {
+          children: region.children.map(c => recursivePropagateNgId(c, { ngId: region.ngId || ngId }))
+        }
+        : {} )
+    }
+  }
+  const regions = parcellation.regions && parcellation.regions.map
+    ? parcellation.regions.map(r => recursivePropagateNgId(r, { ngId: parcellation.ngId }))
+    : []
 
-export function isDefined(obj){
-  return typeof obj !== 'undefined' && obj !== null
+  return {
+    ...parcellation,
+    regions
+  }
 }
\ No newline at end of file
diff --git a/src/ui/banner/banner.component.ts b/src/ui/banner/banner.component.ts
deleted file mode 100644
index c572d500e0b14b020fe9a429bee2438d045486f5..0000000000000000000000000000000000000000
--- a/src/ui/banner/banner.component.ts
+++ /dev/null
@@ -1,292 +0,0 @@
-import { Component, OnDestroy, ChangeDetectionStrategy, HostListener, ViewChild, ElementRef, OnInit } from "@angular/core";
-import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, safeFilter, SELECT_PARCELLATION, extractLabelIdx, SELECT_REGIONS, NEWVIEWER, getLabelIndexMap, isDefined, CHANGE_NAVIGATION } from "../../services/stateStore.service";
-import { Observable, Subscription, merge, Subject } from "rxjs";
-import { map, filter, debounceTime, buffer, distinctUntilChanged } from "rxjs/operators";
-import { FilterNameBySearch } from "../../util/pipes/filterNameBySearch.pipe";
-import { regionAnimation } from "./regionPopover.animation";
-import { AtlasViewerConstantsServices } from "../../atlasViewer/atlasViewer.constantService.service"
-
-@Component({
-  selector: 'atlas-banner',
-  templateUrl: './banner.template.html',
-  styleUrls: [
-    `./banner.style.css`
-  ],
-  animations: [
-    regionAnimation
-  ],
-  changeDetection: ChangeDetectionStrategy.OnPush
-})
-
-export class AtlasBanner implements OnDestroy, OnInit {
-
-  public loadedTemplates$: Observable<any[]>
-  public newViewer$: Observable<any>
-  public selectedParcellation$: Observable<any>
-  public selectedRegions$: Observable<any[]>
-
-  private regionsLabelIndexMap: Map<number, any> = new Map()
-
-  public selectedTemplate: any
-  public selectedParcellation: any
-  public selectedRegions: any[] = []
-  private navigation: { position: [number, number, number] } = { position: [0, 0, 0] }
-
-  private subscriptions: Subscription[] = []
-
-  @ViewChild('templateCitationAnchor', { read: ElementRef }) templateCitationAnchor: ElementRef
-  @ViewChild('parcellationCitationAnchor', { read: ElementRef }) parcellationCitationAnchor: ElementRef
-
-  searchTerm: string = ''
-
-  constructor(
-    private store: Store<ViewerStateInterface>,
-    private constantService: AtlasViewerConstantsServices
-  ) {
-    this.loadedTemplates$ = this.store.pipe(
-      select('viewerState'),
-      safeFilter('fetchedTemplates'),
-      map(state => state.fetchedTemplates))
-
-    this.newViewer$ = this.store.pipe(
-      select('viewerState'),
-      filter(state => isDefined(state) && isDefined(state.templateSelected)),
-      distinctUntilChanged((o, n) => o.templateSelected.name === n.templateSelected.name)
-    )
-
-    this.selectedParcellation$ = this.store.pipe(
-      select('viewerState'),
-      safeFilter('parcellationSelected'),
-      map(state => state.parcellationSelected),
-      distinctUntilChanged((o, n) => o === n)
-    )
-
-    this.selectedRegions$ = merge(
-      this.store.pipe(
-        select('viewerState'),
-        filter(state => isDefined(state) && isDefined(state.regionsSelected)),
-        map(state => state.regionsSelected)
-      )
-    )
-  }
-
-  ngOnInit() {
-    this.subscriptions.push(
-      this.newViewer$.subscribe((state) => {
-        this.selectedTemplate = state.templateSelected
-        const selectedParcellation = state.parcellationSelected ? state.parcellationSelected : this.selectedTemplate.parcellations[0]
-        this.handleParcellationChange(selectedParcellation)
-      })
-    )
-
-    this.subscriptions.push(
-      this.newViewer$.pipe(
-        debounceTime(250)
-      ).subscribe(() => {
-        if (this.templateCitationAnchor)
-          this.templateCitationAnchor.nativeElement.click()
-      })
-    )
-
-    this.subscriptions.push(
-      this.selectedParcellation$.subscribe((this.handleParcellationChange).bind(this))
-    )
-
-    this.subscriptions.push(
-      this.selectedParcellation$.pipe(
-        debounceTime(250)
-      ).subscribe(() => {
-        if (this.parcellationCitationAnchor)
-          this.parcellationCitationAnchor.nativeElement.click()
-      })
-    )
-
-    this.subscriptions.push(
-      this.selectedRegions$.subscribe((ev) => {
-        this.selectedRegions = ev
-      })
-    )
-
-    this.subscriptions.push(
-      this.handleRegionTreeClickSubject.pipe(
-        buffer(
-          this.handleRegionTreeClickSubject.pipe(
-            debounceTime(200)
-          )
-        )
-      ).subscribe(arr => arr.length > 1 ? this.doubleClick(arr[0]) : this.singleClick(arr[0]))
-    )
-
-    this.subscriptions.push(
-      this.store.pipe(
-        select('viewerState'),
-        safeFilter('navigation'),
-        map(obj => obj.navigation)
-      ).subscribe((navigation: any) => this.navigation = navigation)
-    )
-  }
-
-  ngOnDestroy() {
-    this.subscriptions.forEach(s => s.unsubscribe())
-  }
-
-  handleParcellationChange(parcellation) {
-    this.selectedParcellation = parcellation
-    this.regionsLabelIndexMap = getLabelIndexMap(parcellation.regions)
-  }
-
-  selectTemplate(template: any) {
-    if (this.selectedTemplate === template) {
-      return
-    }
-
-    this.store.dispatch({
-      type: NEWVIEWER,
-      selectTemplate: template,
-      selectParcellation: template.parcellations[0]
-    })
-  }
-
-  selectParcellation(parcellation: any) {
-    if (this.selectedParcellation === parcellation) {
-      return
-    }
-    this.store.dispatch({
-      type: SELECT_PARCELLATION,
-      selectParcellation: parcellation
-    })
-  }
-
-  /* double click navigate to the interested area */
-  private doubleClick(obj: any) {
-    if (!(obj && obj.inputItem && (obj.inputItem.position || obj.inputItem.POIs))) {
-      return
-    }
-
-    const newPos = obj.inputItem.position
-      ? obj.inputItem.position
-      : obj.inputItem.POIs && obj.inputItem.POIs.constructor === Array && obj.inputItem.POIs.length > 0
-        ? obj.inputItem.POIs[0]
-        : null
-
-    this.store.dispatch({
-      type: CHANGE_NAVIGATION,
-      navigation: {
-        position: newPos,
-        animation: {
-          /* empty object is enough to be truthy */
-        }
-      },
-    })
-  }
-
-  /* single click selects/deselects region(s) */
-  private singleClick(obj: any) {
-    const region = obj.inputItem
-    const selectedSet = new Set(extractLabelIdx(region))
-    const intersection = new Set([...this.selectedRegions.map(r => r.labelIndex)].filter(v => selectedSet.has(v)))
-    this.store.dispatch({
-      type: SELECT_REGIONS,
-      selectRegions: intersection.size > 0 ?
-        this.selectedRegions.filter(v => !intersection.has(v.labelIndex)) :
-        this.selectedRegions.concat([...selectedSet].map(idx => this.regionsLabelIndexMap.get(idx)))
-    })
-  }
-
-  private handleRegionTreeClickSubject: Subject<any> = new Subject()
-
-  /* NB need to bind two way data binding like this. Or else, on searchInput blur, the flat tree will be rebuilt,
-    resulting in first click to be ignored */
-  changeSearchTerm(event: any) {
-    if (event.target.value === this.searchTerm)
-      return
-    this.searchTerm = event.target.value
-  }
-
-  @ViewChild('searchRegionPopover', { read: ElementRef }) inputRegionPopover: ElementRef
-  public showRegionTree: boolean
-
-  @HostListener('document:click', ['$event'])
-  closeRegion(event: MouseEvent) {
-
-    /* region popover may not always be rendered */
-    if (!this.inputRegionPopover)
-      return
-
-    /* FF < 62 does not implement event.srcElement so use event.originalTarget to polyfill for FF */
-
-    const contains = event.srcElement
-      ? this.inputRegionPopover.nativeElement.contains(event.srcElement)
-      : this.inputRegionPopover.nativeElement.contains((event as any).originalTarget)
-    if (contains)
-      this.showRegionTree = true
-    else
-      this.showRegionTree = false
-  }
-
-  handleClickRegion(obj: any) {
-    obj.event.stopPropagation()
-    this.handleRegionTreeClickSubject.next(obj)
-  }
-
-  displayActiveTemplate(template: any) {
-    return `<small>Template</small> <small class = "mute-text">${template ? '(' + template.name + ')' : ''}</small> <span class = "caret"></span>`
-  }
-
-  displayActiveParcellation(parcellation: any) {
-    return `<small>Parcellation</small> <small class = "mute-text">${parcellation ? '(' + parcellation.name + ')' : ''}</small> <span class = "caret"></span>`
-  }
-
-  private insertHighlight(name: string, searchTerm: string): string {
-    const regex = new RegExp(searchTerm, 'gi')
-    return searchTerm === '' ?
-      name :
-      name.replace(regex, (s) => `<span class = "highlight">${s}</span>`)
-  }
-
-  displayTreeNode(item: any) {
-    return typeof item.labelIndex !== 'undefined' && this.selectedRegions.findIndex(re => re.labelIndex === Number(item.labelIndex)) >= 0 ?
-      `<span class = "regionSelected">${this.insertHighlight(item.name, this.searchTerm)}</span>` :
-      `<span class = "regionNotSelected">${this.insertHighlight(item.name, this.searchTerm)}</span>`
-  }
-
-  getChildren(item: any) {
-    return item.children
-  }
-
-  filterTreeBySearch(node: any): boolean {
-    return this.filterNameBySearchPipe.transform([node.name], this.searchTerm)
-  }
-
-  clearRegions(event: Event) {
-    event.stopPropagation()
-    event.preventDefault()
-    this.store.dispatch({
-      type: SELECT_REGIONS,
-      selectRegions: []
-    })
-  }
-
-  filterNameBySearchPipe = new FilterNameBySearch()
-
-  get aggregatedRegionTree() {
-    return {
-      name: this.selectedParcellation.name,
-      children: this.selectedParcellation.regions
-    }
-  }
-
-  citationExists(obj: any) {
-    return obj && obj.properties && obj.properties.publications && obj.properties.publications.length > 0
-  }
-
-  get toastDuration() {
-    return this.constantService.citationToastDuration
-  }
-
-  get isMobile(){
-    return this.constantService.mobile
-  }
-}
-
diff --git a/src/ui/banner/banner.template.html b/src/ui/banner/banner.template.html
deleted file mode 100644
index 74a7d4ad517c0741af03eca5a9493e6615754dce..0000000000000000000000000000000000000000
--- a/src/ui/banner/banner.template.html
+++ /dev/null
@@ -1,86 +0,0 @@
-<dropdown-component
-  (itemSelected) = "selectTemplate($event)"
-  [activeDisplay] = "displayActiveTemplate"
-  [selectedItem] = "selectedTemplate"
-  [inputArray] = "loadedTemplates$ | async | filterNull">
-
-</dropdown-component>
-
-<i 
-  *ngIf = "citationExists(selectedTemplate) && !isMobile" 
-  [toastLength] = "toastDuration"
-  [showToast] = "citation"
-  class="glyphicon glyphicon-info-sign"
-  #templateCitationAnchor>
-
-  <ng-template #citation>
-    <h4>
-      Citations for {{ selectedTemplate ? selectedTemplate.name : '' }} 
-    </h4>
-    <citations-component
-      [properties] = "selectedTemplate.properties">
-    </citations-component>
-  </ng-template>
-
-</i>
-
-<dropdown-component
-  *ngIf = "selectedTemplate"
-  (itemSelected) = "selectParcellation($event)"
-  [activeDisplay] = "displayActiveParcellation"
-  [selectedItem] = "selectedParcellation"
-  [inputArray] = "selectedTemplate.parcellations">
-</dropdown-component>
-
-<i 
-  *ngIf = "citationExists(selectedParcellation) && !isMobile" 
-  [toastLength] = "toastDuration"
-  [showToast] = "citation"
-  class="glyphicon glyphicon-info-sign"
-  #parcellationCitationAnchor>
-  <ng-template #citation>
-    <h4>
-      Citations for {{ selectedParcellation ? selectedParcellation.name : '' }} 
-    </h4>
-    <citations-component
-      [properties] = "selectedParcellation.properties">
-    </citations-component>
-  </ng-template>
-</i>
-
-<div 
-  *ngIf = "selectedTemplate"
-  #searchRegionPopover
-  searchRegionPopover>
-
-  <input 
-    (keydown.esc) = "showRegionTree = false; $event.target.blur(); searchTerm = ''"
-    (focus) = "showRegionTree = true"
-    [value] = "searchTerm"
-    (input) = "changeSearchTerm($event)"
-    class = "form-control" 
-    type="text" 
-    placeholder="Regions"/>
-    
-  <div 
-    [@showState]
-    *ngIf = "showRegionTree" 
-    hideScrollbarContainer>
-
-    <div treeContainer #treeContainer>
-      <div treeHeader>
-        <span>{{ selectedRegions.length }} {{ selectedRegions.length > 1 ? 'regions' : 'region' }} selected</span> 
-        <span (click) = "clearRegions($event)" *ngIf = "selectedRegions.length > 0" class = "btn btn-link">clear all</span>
-      </div>
-      
-      <flat-tree-component 
-        [flatTreeViewPort] = "treeContainer"
-        (treeNodeClick) = "handleClickRegion($event)"
-        [inputItem] = "aggregatedRegionTree"
-        [renderNode] = "displayTreeNode.bind(this)"
-        [searchFilter] = "filterTreeBySearch.bind(this)">
-        
-      </flat-tree-component>
-    </div>
-  </div>
-</div>
\ No newline at end of file
diff --git a/src/ui/banner/regionPopover.animation.ts b/src/ui/banner/regionPopover.animation.ts
deleted file mode 100644
index ecf95a13432c984855db4350e4795a1b095b4783..0000000000000000000000000000000000000000
--- a/src/ui/banner/regionPopover.animation.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import { trigger, state, style, transition, animate } from "@angular/animations";
-
-
-export const regionAnimation = trigger('showState',[
-  state('*',
-    style({
-      opacity : '1.0'
-    })
-  ),
-  state('void',
-    style({
-      opacity : '0.0',
-      'pointer-events':'none'
-    })
-  ),
-  transition('* => void', [
-    animate('230ms ease-in')
-  ]),
-  transition('void => *',[
-    style({
-      opacity : '0.0',
-    }),
-    animate('420ms ease-in', style({
-      opacity : '1.0'
-    }))
-  ])
-])
\ No newline at end of file
diff --git a/src/ui/btnShadow.style.css b/src/ui/btnShadow.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..3d4bb9c2dd3ef38fcaa74e5deb5406132ffbde03
--- /dev/null
+++ b/src/ui/btnShadow.style.css
@@ -0,0 +1,23 @@
+.btnWrapper
+{
+  display:flex;
+  align-items: center;
+  justify-content: center;
+}
+
+
+.btnWrapper > .btn
+{
+  width: 2.5em;
+  height: 2.5em;
+
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.btnWrapper.btnWrapper-lg > .btn
+{
+  width: 5rem;
+  height: 5rem;
+}
\ No newline at end of file
diff --git a/src/ui/config/config.component.ts b/src/ui/config/config.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e0104405a1b4bc8efda4e8d5b8e18af4598b7b06
--- /dev/null
+++ b/src/ui/config/config.component.ts
@@ -0,0 +1,75 @@
+import { Component, OnInit, OnDestroy } from '@angular/core'
+import { Store, select } from '@ngrx/store';
+import { ViewerConfiguration, ACTION_TYPES } from 'src/services/state/viewerConfig.store'
+import { Observable, Subject, Subscription } from 'rxjs';
+import { map, distinctUntilChanged, debounce, debounceTime } from 'rxjs/operators';
+
+@Component({
+  selector: 'config-component',
+  templateUrl: './config.template.html',
+  styleUrls: [
+    './config.style.css'
+  ]
+})
+
+export class ConfigComponent implements OnInit, OnDestroy{
+
+  /**
+   * in MB
+   */
+  public gpuLimit$: Observable<number>
+  public keydown$: Subject<Event> = new Subject()
+  private subscriptions: Subscription[] = []
+
+  public gpuMin : number = 100
+  public gpuMax : number = 1000
+  
+  constructor(private store: Store<ViewerConfiguration>) {
+    this.gpuLimit$ = this.store.pipe(
+      select('viewerConfigState'),
+      map((config:ViewerConfiguration) => config.gpuLimit),
+      distinctUntilChanged(),
+      map(v => v / 1e6)
+    )
+  }
+
+  ngOnInit(){
+    this.subscriptions.push(
+      this.keydown$.pipe(
+        debounceTime(250)
+      ).subscribe(ev => {
+        /**
+         * maybe greak in FF. ev.srcElement is IE non standard property
+         */
+        const val = (<HTMLInputElement>ev.srcElement).value
+        const numVal = val && Number(val)
+        if (isNaN(numVal) || numVal < this.gpuMin || numVal > this.gpuMax )
+          return
+        this.setGpuPreset({
+          value: numVal
+        })
+      })
+    )
+  }
+
+  ngOnDestroy(){
+    this.subscriptions.forEach(s => s.unsubscribe())
+  }
+
+  public wheelEvent(ev:WheelEvent) {
+    const delta = ev.deltaY * -1e5
+    this.store.dispatch({
+      type: ACTION_TYPES.CHANGE_GPU_LIMIT,
+      payload: { delta }
+    })
+  }
+
+  public setGpuPreset({value}: {value: number}) {
+    this.store.dispatch({
+      type: ACTION_TYPES.UPDATE_CONFIG,
+      config: {
+        gpuLimit: value * 1e6
+      }
+    })
+  }
+}
\ No newline at end of file
diff --git a/src/ui/config/config.style.css b/src/ui/config/config.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/ui/config/config.template.html b/src/ui/config/config.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..18f84d0b297f9ef71422960695f032c567faafb1
--- /dev/null
+++ b/src/ui/config/config.template.html
@@ -0,0 +1,32 @@
+<div class="input-group">
+  <span class="input-group-prepend">
+    <span class="input-group-text">
+      GPU Limit
+    </span>
+  </span>
+  <input
+    (wheel)="wheelEvent($event)"
+    type="number"
+    [min]="100"
+    [max]="1000"
+    [step]="0.1"
+    class="form-control"
+    (input)="keydown$.next($event)"
+    [value]="gpuLimit$ | async ">
+
+  <div class="input-group-append">
+
+    <div (click)="setGpuPreset({ value: 100 })" class="btn btn-outline-secondary">
+        100
+      </div>
+      <div (click)="setGpuPreset({ value: 500 })" class="btn btn-outline-secondary">
+        500
+      </div>
+      <div (click)="setGpuPreset({ value: 1000 })" class="btn btn-outline-secondary">
+        1000
+      </div>
+      <span class="input-group-text">
+        MB
+      </span>
+    </div>
+  </div>
\ No newline at end of file
diff --git a/src/ui/cookieAgreement/cookieAgreement.component.ts b/src/ui/cookieAgreement/cookieAgreement.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e52f59c5be1878198d2c3321c10daa19486fbeef
--- /dev/null
+++ b/src/ui/cookieAgreement/cookieAgreement.component.ts
@@ -0,0 +1,13 @@
+import { Component, ChangeDetectionStrategy } from '@angular/core'
+
+@Component({
+  selector: 'cookie-agreement',
+  templateUrl: './cookieAgreement.template.html',
+  styleUrls: [
+    './cookieAgreement.style.css'
+  ],
+  changeDetection: ChangeDetectionStrategy.OnPush
+})
+export class CookieAgreement {
+  public showMore:boolean = false
+}
\ No newline at end of file
diff --git a/src/ui/cookieAgreement/cookieAgreement.style.css b/src/ui/cookieAgreement/cookieAgreement.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/ui/cookieAgreement/cookieAgreement.template.html b/src/ui/cookieAgreement/cookieAgreement.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..316ca3d43c8ea1a97f94e10bfb87e02b0a98560f
--- /dev/null
+++ b/src/ui/cookieAgreement/cookieAgreement.template.html
@@ -0,0 +1,110 @@
+<div>
+  <p>Interactive Viewer uses ‘cookies’ (text files placed on your computer) to verify login details,
+    remember user choices and preferences, and in some instances determine site permissions. Cookies also provide,
+    in anonymous form, the number of visitors accessing the HBP Public Website, features users access during website
+    visits, and the general location of the user based on IP address.</p>
+  <p>Interactive Viewer uses at least the following cookies:</p>
+  <p><strong>connect.sid</strong> : verify login details</p>
+  <p>To find out more about cookies, including how to determine the cookies that have been set on
+    your computer and how to manage
+    or delete them, visit <a href="www.aboutcookies.org">www.aboutcookies.org </a> .</p>
+  <p>To opt-out of being tracked by Google Analytics across all websites, visit
+    <a href="http://tools.google.com/dlpage/gaoptout">http://tools.google.com/dlpage/gaoptout</a> .</p>
+
+  <button
+    class="btn btn-outline-info btn-block mb-2"
+    (click)="showMore = !showMore">
+    Show {{showMore? "less" : "more"}}
+  </button>
+
+  <div *ngIf="showMore">
+    <small>
+
+      <div>
+        <strong>Data controller(s):</strong>
+        <span>
+          Interactive Viewer is the data controller for your login information.
+        </span>
+      </div>
+
+      <div>
+        <strong>List of partners responsible:</strong>
+        <span>
+          FZJ-INM1 <a href="mailto:inm1-bda@fz-juelich.de">inm1-bda@fz-juelich.de</a>
+        </span>
+      </div>
+
+      <div>
+        <strong>HBP Data Protection Officer (HBP DPO):</strong>
+        <span>
+          You can find out about HBP Data Protection policies here: 
+          <a href="https://www.humanbrainproject.eu/en/social-ethical-reflective/ethics-support/data-protection/">
+            https://www.humanbrainproject.eu/en/social-ethical-reflective/ethics-support/data-protection/
+          </a>
+          and you can contact HBP Data Protection Officer at
+          <a href="mailto:data.protection@humanbrainproject.eu">
+            data.protection@humanbrainproject.eu
+          </a>
+        </span>
+      </div>
+
+      <div>
+        <strong>Legal basis for data processing:</strong>
+        <span>
+          consent
+        </span>
+      </div>
+
+      <div>
+        <strong>General categories of personal data collected:</strong>
+        <span>
+          Name, ORC ID, JWT ID Token
+        </span>
+      </div>
+
+      <div>
+        <strong>Data shared within the HBP Consortium:</strong>
+        <span>
+          JWT ID Token is shared with ChunMa (Chunk Master), enabling upload and retrieval of user uploaded data.
+        </span>
+      </div>
+
+      <div>
+        <strong>Data Shared with Third Parties:</strong>
+        <span>none</span>
+      </div>
+
+      <div>
+        <strong>Transfer of any personal data to third countries:</strong>
+        <span>
+          Data are stored in HBP infrastructure, and will not be transferred to Third countries.
+        </span>
+      </div>
+
+      <div>
+        <strong>Retention periods:</strong>
+        <div>
+          24 hours
+        </div>
+      </div>
+
+      <div>
+        <strong>Lodging a complaint:</strong>
+        <span>
+          The HBP DPO and HBP partners will make every reasonable effort to address your data protection concerns.
+          However, you have a right to lodge a complaint with a data protection authority. Contact information for the
+          European Data Protection Board and EU DPAs are available here:
+          <ul>
+            <li><a
+                href="http://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id=612080">http://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id=612080</a>
+            </li>
+            <li data-v-fc2c56ac=""><a data-v-fc2c56ac=""
+                href="https://edpb.europa.eu/about-edpb/board/members_en">https://edpb.europa.eu/about-edpb/board/members_en</a>
+            </li>
+          </ul>
+        </span>
+      </div>
+
+    </small>
+  </div>
+</div>
diff --git a/src/ui/databrowser/databrowser.component.ts b/src/ui/databrowser/databrowser.component.ts
deleted file mode 100644
index 003a019511c6addb57fb61d34bca3adcfe891ab8..0000000000000000000000000000000000000000
--- a/src/ui/databrowser/databrowser.component.ts
+++ /dev/null
@@ -1,308 +0,0 @@
-import { Component, OnDestroy, ComponentFactoryResolver, ComponentFactory, OnInit, Injector } from "@angular/core";
-import { Store, select } from "@ngrx/store";
-import { DataStateInterface, Property, safeFilter, DataEntry, File, SELECT_REGIONS, getLabelIndexMap, isDefined, SPATIAL_GOTO_PAGE, CHANGE_NAVIGATION, UPDATE_SPATIAL_DATA_VISIBLE, DESELECT_REGIONS, DESELECT_LANDMARKS, SELECT_LANDMARKS } from "../../services/stateStore.service";
-import { map, filter, distinctUntilChanged } from "rxjs/operators";
-import { HasPathProperty } from "../../util/pipes/pathToNestedChildren.pipe";
-import { TreeComponent } from "../../components/tree/tree.component";
-import { Observable, Subscription, combineLatest } from "rxjs";
-import { FileViewer } from "../fileviewer/fileviewer.component";
-import { WidgetServices } from "../../atlasViewer/widgetUnit/widgetService.service";
-import { AtlasViewerConstantsServices } from "../../atlasViewer/atlasViewer.constantService.service";
-
-@Component({
-  selector : 'data-browser',
-  templateUrl : './databrowser.template.html',
-  styleUrls : [
-    `./databrowser.style.css`
-  ]
-})
-
-export class DataBrowserUI implements OnDestroy,OnInit{
-
-  private fileViewerComponentFactory : ComponentFactory<FileViewer>
-
-  hitsPerPage : number = 15
-  currentPage :  number = 0
-
-  metadataMap : Map<string,Map<string,{properties:Property}>>
-  dataEntries : DataEntry[] = []
-  spatialDataEntries : DataEntry[] = []
-  spatialPagination : number = 0
-  spatialTotalNo : number = 0
-  hideDataTypes : Set<string> = new Set()
-
-  private _spatialDataVisible : boolean = false
-
-  dedicatedViewString : string | null
-
-  private regionsLabelIndexMap : Map<number,any> = new Map()
-
-  public selectedRegions$ : Observable<any[]>
-  public selectedLandmarks$ : Observable<any[]>
-  public selectedPOI$ : Observable<any[]>
-
-  private metadataMap$ : Observable<any>
-  private fetchedDataEntries$ : Observable<any>
-  private selectParcellation$ : Observable<any>
-  private dedicatedViewString$ : Observable<string|null>
-  private spatialDataEntries$ : Observable<any[]>
-  private spatialPagination$ : Observable<{spatialSearchPagination:number,spatialSearchTotalResults:number}>
-
-  private subscriptions : Subscription[] = []
-
-  constructor(
-    private cfr : ComponentFactoryResolver,
-    private store : Store<DataStateInterface>,
-    private constantService : AtlasViewerConstantsServices,
-    private injector : Injector,
-    private widgetServices : WidgetServices
-  ){
-    
-    this.fileViewerComponentFactory = this.cfr.resolveComponentFactory(FileViewer)
-
-    this.selectedRegions$ = this.store.pipe(
-      select('viewerState'),
-      safeFilter('regionsSelected'),
-      map(state=>state.regionsSelected)
-    )
-
-    this.selectedLandmarks$ = this.store.pipe(
-      select('viewerState'),
-      safeFilter('landmarksSelected'),
-      map(state => state.landmarksSelected)
-    )
-
-    this.selectedPOI$ = combineLatest(
-      this.selectedRegions$,
-      this.selectedLandmarks$
-    ).pipe(
-      map(results => [...results[0], ...results[1]])
-    )
-    
-
-    this.metadataMap$ = this.store.pipe(
-      select('dataStore'),
-      safeFilter('fetchedMetadataMap'),
-      map(v=>v.fetchedMetadataMap)
-    )
-    
-    this.fetchedDataEntries$ = this.store.pipe(
-      select('dataStore'),
-      safeFilter('fetchedDataEntries'),
-      map(v=>v.fetchedDataEntries)
-    )
-
-    this.selectParcellation$ = this.store.pipe(
-      select('viewerState'),
-      safeFilter('parcellationSelected'),
-      map(state=>state.parcellationSelected),
-      distinctUntilChanged((p1,p2)=>p1.name === p2.name)
-    )
-      
-
-    this.dedicatedViewString$ = this.store.pipe(
-      select('viewerState'),
-      filter(state=> typeof state !== 'undefined' && state !== null),
-      map(state=>state.dedicatedView)
-    )
-
-    this.spatialDataEntries$ = this.store.pipe(
-      select('dataStore'),
-      safeFilter('fetchedSpatialData'),
-      map(state=>state.fetchedSpatialData)
-    )
-
-    this.spatialPagination$ = store.pipe(
-      select('spatialSearchState'),
-      filter(state=> isDefined(state) &&
-        isDefined(state.spatialSearchPagination) &&
-        isDefined(state.spatialSearchTotalResults)
-      ),
-      distinctUntilChanged((s1,s2)=>
-        s1.spatialSearchPagination === s2.spatialSearchPagination &&
-        s1.spatialSearchTotalResults === s2.spatialSearchTotalResults &&
-        s1.spatialDataVisible === s2.spatialDataVisible),
-    )
-  }
-
-  ngOnInit(){
-
-    this.subscriptions.push(this.metadataMap$.subscribe(map=>(this.metadataMap = map)))
-
-    this.subscriptions.push(this.fetchedDataEntries$.subscribe(arr=>(this.dataEntries = arr)))
-
-    this.subscriptions.push(this.selectParcellation$.subscribe(parcellation=>
-        this.handleParcellationSelection(parcellation.regions)))
-
-    this.subscriptions.push(
-      this.dedicatedViewString$.subscribe(dvs=>this.dedicatedViewString = dvs))
-
-    this.subscriptions.push(
-      this.spatialDataEntries$.subscribe(this.handleSpatialDataEntries.bind(this)))
-
-    this.subscriptions.push(
-      this.spatialPagination$.subscribe(this.handleSpatialPaginationChange.bind(this))
-    )
-  }
-
-  ngOnDestroy(){
-    this.subscriptions.forEach(s=>s.unsubscribe())
-  }
-
-  /* spatial search functionalities */
-
-  spatialPaginationChange(pagenum:number){
-    this.store.dispatch({
-      type : SPATIAL_GOTO_PAGE,
-      pageNo : pagenum
-    })
-  }
-
-  get spatialPaginationHitsPerPage(){
-    return this.constantService.spatialResultsPerPage
-  }
-
-  toggleSpatialDataVisible(){
-    this.store.dispatch({
-      type : UPDATE_SPATIAL_DATA_VISIBLE,
-      visible : !this._spatialDataVisible
-    })
-  }
-
-  get spatialDataVisible(){
-    return this._spatialDataVisible
-  }
-
-  // TODO deprecated? rethink how to implement displaying of spatial landmarks
-  handleSpatialPaginationChange(state){
-    // if(isDefined (state.spatialSearchPagination) )
-    //   this.spatialPagination = state.spatialSearchPagination
-
-    if(isDefined(state.spatialSearchTotalResults))
-      this.spatialTotalNo = state.spatialSearchTotalResults
-      
-    if(isDefined(state.spatialDataVisible))
-      this._spatialDataVisible = state.spatialDataVisible
-
-    // if(this._spatialDataVisible === false)
-    //   return
-    
-    // if(this.spatialPagination === this.spatialSearchObj.pageNo)
-    //   return
-
-    // console.log('pagination change')
-    // this.spatialSearchObj.pageNo = this.spatialPagination
-    // this.atlasviewerDataService.spatialSearch(this.spatialSearchObj)
-  }
-
-  handleSpatialDataEntries(datas){
-    this.spatialDataEntries = datas
-  }
-
-  /* non-spatial data functionalities */
-
-  regions : any[] = []
-
-  handleParcellationSelection(regions:any[]){
-    this.regionsLabelIndexMap = getLabelIndexMap(regions)
-    this.regions = Array.from(this.regionsLabelIndexMap.values())
-  }
-
-  paginationChange(pageNum:number){
-    this.currentPage = pageNum
-  }
-
-  serchResultFilesIsArray(files:any){
-    return typeof files !== 'undefined' && 
-      files !== null &&
-      files.constructor === Array
-  }
-
-  renderNode(file:HasPathProperty&File){
-    return `${file.name ? file.name : file.path}`
-  }
-
-  dataWindowRegistry: Set<string> = new Set()
-
-  handleFlatTreeNodeClick(payload:{dataset:DataEntry, file:File}){
-    const { dataset, file } = payload
-    const { properties, kgID } = dataset
-    if(file.mimetype){
-
-      // TODO use KG id in future
-      if(this.dataWindowRegistry.has(file.name)){
-        /* already open, will not open again */
-        return
-      }
-      /* not yet open, add the name to registry */
-      this.dataWindowRegistry.add(file.name)
-
-      const component = this.fileViewerComponentFactory.create(this.injector)
-      component.instance.searchResultFile = Object.assign({}, file, { datasetProperties : properties }, kgID ? { kgID } : {})
-      const compref = this.widgetServices.addNewWidget(component,{title:file.name,exitable:true,state:'floating'})
-
-      /* on destroy, removes name from registry */
-      compref.onDestroy(() => this.dataWindowRegistry.delete(file.name))
-    }else{
-      /** no mime type  */
-    }
-  }
-
-  clearAllPOIs(){
-    this.store.dispatch({
-      type : SELECT_REGIONS,
-      selectRegions : []
-    })
-    this.store.dispatch({
-      type : SELECT_LANDMARKS,
-      landmarks : []
-    })
-  }
-
-  typeVisible(type:string){
-    return !this.hideDataTypes.has(type)
-  }
-
-  toggleTypeVisibility(type:string){
-    this.hideDataTypes.has(type) ?
-      this.hideDataTypes.delete(type) :
-      this.hideDataTypes.add(type)
-
-    /* Somehow necessary, or else Angular will not mark for change */
-    this.hideDataTypes = new Set(
-      [...this.hideDataTypes]
-    )
-  }
-
-  gothere(event:MouseEvent,position:any){
-    event.stopPropagation()
-    event.preventDefault()
-
-    this.store.dispatch({
-      type : CHANGE_NAVIGATION,
-      navigation : {
-        position : position,
-        animation : {
-          
-        }
-      }
-    })
-  }
-
-  removePOI(event:MouseEvent, region:any){
-    event.stopPropagation()
-    event.preventDefault()
-
-    if(region.spatialLandmark){
-      this.store.dispatch({
-        type : DESELECT_LANDMARKS,
-        deselectLandmarks : [region]
-      })
-    }else{
-      this.store.dispatch({
-        type : DESELECT_REGIONS,
-        deselectRegions : [region]
-      })
-    }
-  }
-}
diff --git a/src/ui/databrowser/databrowser.template.html b/src/ui/databrowser/databrowser.template.html
deleted file mode 100644
index 9a5460f8191ab97f83314fb340924c05cc929359..0000000000000000000000000000000000000000
--- a/src/ui/databrowser/databrowser.template.html
+++ /dev/null
@@ -1,145 +0,0 @@
-<!-- Header -->
-<div *ngIf = "selectedPOI$ | async" databrowserheader>
-  <span>
-    {{ !(selectedPOI$ | async) ? '' : (((selectedPOI$ | async).length === 0 ? 'No' : (selectedPOI$ | async).length) + ' selected region' + ((selectedPOI$ | async).length > 1 ? 's' : '')) + ' of interest'  }} 
-  </span>
-  <small
-    *ngIf = "(selectedPOI$ | async).length > 0"
-    class = "btn btn-link"
-    (click) = "clearAllPOIs()">
-    clear all
-  </small>
-</div>
-
-<!-- Filter -->
-<div filterBlock>
-  <div>
-    Filter
-  </div>
-  <div
-    *ngFor = "let type of dataEntries | getUniqueProperty : 'type' "
-    class = "clickable"
-    (click)="toggleTypeVisibility(type)">
-    <small>
-      <i
-        *ngIf = "typeVisible(type)" 
-        class = "glyphicon glyphicon-check">
-      </i>
-      <i
-        *ngIf = "!typeVisible(type)" 
-        class = "glyphicon glyphicon-unchecked">
-      </i>
-    </small>
-    <small>
-      {{ type }}
-    </small>
-  </div>
-  <div 
-    class = "clickable" 
-    (click) = "toggleSpatialDataVisible()">
-    <small>
-      <i *ngIf = "spatialDataVisible" class = "glyphicon glyphicon-check"></i>
-      <i *ngIf = "!spatialDataVisible" class = "glyphicon glyphicon-unchecked"></i>
-    </small>
-    <small>
-      Spatial Search
-    </small>
-  </div>
-</div>
-
-<!-- Data -->
-<div *ngIf = "spatialDataVisible && false">
-  <panel-component [collapseBody] = "true" [bodyCollapsable] = "true">
-    <div heading>
-      <span>
-        Spatial Search Data
-      </span>
-      <span class = "mute-text">
-        ({{ spatialTotalNo }})
-      </span>
-    </div>
-    <div body>
-      <div *ngFor = "let data of spatialDataEntries" spatialSearchCell>
-        <div *ngIf = "data.name">
-          {{ data.name }}
-        </div>
-        <div *ngIf = "data.properties">
-          {{ data.properties.description }}
-        </div>
-      </div>
-
-      <pagination-component
-        *ngIf = "spatialDataEntries.length > 0"
-        [hitsPerPage]="spatialPaginationHitsPerPage"
-        [total] = "spatialTotalNo"
-        [currentPage]="spatialPagination"
-        (paginationChange)="spatialPaginationChange($event)">
-
-      </pagination-component>
-    </div>
-  </panel-component>
-</div>
-<div 
-  *ngIf = "(selectedPOI$ | async) && (selectedPOI$ | async).length > 0; else noSelectedRegion"
-  regionsContainer>
-
-  <div
-    *ngFor = "let data of (selectedRegions$ | async | sortDataEntriesToRegion : dataEntries).concat( selectedLandmarks$ | async | spatialLandmarksToDataBrowserItemPipe ) | searchResultPagination  : currentPage : hitsPerPage  "
-    regionContainer>
-    <panel-component 
-      [collapseBody] = "true"
-      [bodyCollapsable] = "true">
-      <div [ngClass] = "(data.searchResults | filterDataEntriesByType : hideDataTypes).length === 0 ? 'noResultClass' : ''" 
-        displayflex
-        heading>
-        <div maintext>
-          <span>
-            {{ data.region ? data.region.name : 'Not associated with any region' }}
-          </span>
-          <span class = "mute-text">
-            ({{ (data.searchResults | filterDataEntriesByType : hideDataTypes).length }})
-          </span>
-        </div>
-        <div propertyicons>
-          <i *ngFor = "let poi of data.region.POIs"
-            (click) = "gothere($event,poi)" 
-            class = "glyphicon glyphicon-screenshot" >
-          </i>
-          <i *ngIf = "data.region && data.region.position"
-            (click) = "gothere($event,data.region.position)" 
-            class = "glyphicon glyphicon-screenshot" >
-          </i>
-          <i (click) = "removePOI($event,data.region)"
-            class = "glyphicon glyphicon-remove-sign">
-          </i>
-        </div>
-      </div>
-      <div body>
-
-        <dataset-viewer
-          (launchFileViewer) = "handleFlatTreeNodeClick($event)"
-          [dataset] = "searchResult"
-          *ngFor = "let searchResult of data.searchResults">
-
-        </dataset-viewer>
-      </div>
-    </panel-component>
-  </div>
-
-  <!-- pagination -->
-  <pagination-component
-    *ngIf = "(selectedPOI$ | async).length > 0 && (selectedPOI$ | async).length > hitsPerPage"
-    [hitsPerPage]="hitsPerPage"
-    [total] = "(selectedPOI$ | async).length"
-    [currentPage]="currentPage"
-    (paginationChange)="paginationChange($event)">
-
-  </pagination-component>
-
-</div>
-
-<ng-template #noSelectedRegion>
-  <div noSelectedRegion>
-    Select a region / spatial landmark to get started
-  </div>
-</ng-template>
diff --git a/src/ui/databrowserModule/databrowser.module.ts b/src/ui/databrowserModule/databrowser.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1238aa758a99074e33dfdfaccd0daf488e82cbc7
--- /dev/null
+++ b/src/ui/databrowserModule/databrowser.module.ts
@@ -0,0 +1,131 @@
+import { NgModule } from "@angular/core";
+import { CommonModule } from "@angular/common";
+import { DataBrowser } from "./databrowser/databrowser.component";
+import { DatasetViewerComponent } from "./datasetViewer/datasetViewer.component";
+import { ComponentsModule } from "src/components/components.module";
+import { ModalityPicker } from "./modalityPicker/modalityPicker.component";
+import { FormsModule } from "@angular/forms";
+import { PathToNestedChildren } from "./util/pathToNestedChildren.pipe";
+import { CopyPropertyPipe } from "./util/copyProperty.pipe";
+import { FilterDataEntriesbyMethods } from "./util/filterDataEntriesByMethods.pipe";
+import { FilterDataEntriesByRegion } from "./util/filterDataEntriesByRegion.pipe";
+import { TooltipModule } from "ngx-bootstrap/tooltip";
+import { PreviewComponent } from "./preview/preview.component";
+import { FileViewer } from "./fileviewer/fileviewer.component";
+import { RadarChart } from "./fileviewer/radar/radar.chart.component";
+import { ChartsModule } from "ng2-charts";
+import { LineChart } from "./fileviewer/line/line.chart.component";
+import { DedicatedViewer } from "./fileviewer/dedicated/dedicated.component";
+import { Chart } from 'chart.js'
+import { AtlasViewerConstantsServices } from "src/atlasViewer/atlasViewer.constantService.service";
+import { PopoverModule } from "ngx-bootstrap/popover";
+import { UtilModule } from "src/util/util.module";
+import { AggregateArrayIntoRootPipe } from "./util/aggregateArrayIntoRoot.pipe";
+
+@NgModule({
+  imports:[
+    ChartsModule,
+    CommonModule,
+    ComponentsModule,
+    FormsModule,
+    UtilModule,
+    TooltipModule.forRoot(),
+    PopoverModule.forRoot()
+  ],
+  declarations: [
+    DataBrowser,
+    DatasetViewerComponent,
+    ModalityPicker,
+    PreviewComponent,
+    FileViewer,
+    RadarChart,
+    LineChart,
+    DedicatedViewer,
+
+    /**
+     * pipes
+     */
+    PathToNestedChildren,
+    CopyPropertyPipe,
+    FilterDataEntriesbyMethods,
+    FilterDataEntriesByRegion,
+    AggregateArrayIntoRootPipe
+  ],
+  exports:[
+    DataBrowser
+  ],
+  entryComponents:[
+    DataBrowser
+  ]
+  /**
+   * shouldn't need bootstrap, so no need for browser module
+   */
+})
+
+export class DatabrowserModule{
+  constructor(
+    constantsService:AtlasViewerConstantsServices
+  ){
+    /**
+     * Because there is no easy way to display standard deviation natively, use a plugin 
+     * */
+    Chart.pluginService.register({
+
+      /* patching background color fill, so saved images do not look completely white */
+      beforeDraw: (chart) => {
+        const ctx = chart.ctx as CanvasRenderingContext2D;
+        ctx.fillStyle = constantsService.darktheme ?
+          `rgba(50,50,50,0.8)` :
+          `rgba(255,255,255,0.8)`
+
+        if (chart.canvas) ctx.fillRect(0, 0, chart.canvas.width, chart.canvas.height)
+
+      },
+
+      /* patching standard deviation for polar (potentially also line/bar etc) graph */
+      afterInit: (chart) => {
+        if (chart.config.options && chart.config.options.tooltips) {
+
+          chart.config.options.tooltips.callbacks = {
+            label: function (tooltipItem, data) {
+              let sdValue
+              if (data.datasets && typeof tooltipItem.datasetIndex != 'undefined' && data.datasets[tooltipItem.datasetIndex].label) {
+                const sdLabel = data.datasets[tooltipItem.datasetIndex].label + '_sd'
+                const sd = data.datasets.find(dataset => typeof dataset.label != 'undefined' && dataset.label == sdLabel)
+                if (sd && sd.data && typeof tooltipItem.index != 'undefined' && typeof tooltipItem.yLabel != 'undefined') sdValue = Number(sd.data[tooltipItem.index]) - Number(tooltipItem.yLabel)
+              }
+              return `${tooltipItem.yLabel} ${sdValue ? '(' + sdValue + ')' : ''}`
+            }
+          }
+        }
+        if (chart.data.datasets) {
+          chart.data.datasets = chart.data.datasets
+            .map(dataset => {
+              if (dataset.label && /\_sd$/.test(dataset.label)) {
+                const originalDS = chart.data.datasets!.find(baseDS => typeof baseDS.label !== 'undefined' && (baseDS.label == dataset.label!.replace(/_sd$/, '')))
+                if (originalDS) {
+                  return Object.assign({}, dataset, {
+                    data: (originalDS.data as number[]).map((datapoint, idx) => (Number(datapoint) + Number((dataset.data as number[])[idx]))),
+                    ... constantsService.chartSdStyle
+                  })
+                } else {
+                  return dataset
+                }
+              } else if (dataset.label) {
+                const sdDS = chart.data.datasets!.find(sdDS => typeof sdDS.label !== 'undefined' && (sdDS.label == dataset.label + '_sd'))
+                if (sdDS) {
+                  return Object.assign({}, dataset, {
+                    ...constantsService.chartBaseStyle
+                  })
+                } else {
+                  return dataset
+                }
+              } else {
+                return dataset
+              }
+            })
+        }
+      }
+    })
+  }
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/databrowser.service.ts b/src/ui/databrowserModule/databrowser.service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..5baa496dc446a07906ffb41a99358a134d98a357
--- /dev/null
+++ b/src/ui/databrowserModule/databrowser.service.ts
@@ -0,0 +1,354 @@
+import { Injectable, OnDestroy } from "@angular/core";
+import { Subscription, Observable, combineLatest, BehaviorSubject, fromEvent, from, of } from "rxjs";
+import { ViewerConfiguration } from "src/services/state/viewerConfig.store";
+import { select, Store } from "@ngrx/store";
+import { AtlasViewerConstantsServices } from "src/atlasViewer/atlasViewer.constantService.service";
+import { ADD_NG_LAYER, REMOVE_NG_LAYER, DataEntry, safeFilter, FETCHED_DATAENTRIES, FETCHED_SPATIAL_DATA, UPDATE_SPATIAL_DATA } from "src/services/stateStore.service";
+import { map, distinctUntilChanged, debounceTime, filter, tap, switchMap, catchError } from "rxjs/operators";
+import { AtlasWorkerService } from "src/atlasViewer/atlasViewer.workerService.service";
+import { FilterDataEntriesByRegion } from "./util/filterDataEntriesByRegion.pipe";
+import { NO_METHODS } from "./util/filterDataEntriesByMethods.pipe";
+import { ComponentRef } from "@angular/core/src/render3";
+import { DataBrowser } from "./databrowser/databrowser.component";
+import { WidgetUnit } from "src/atlasViewer/widgetUnit/widgetUnit.component";
+import { SHOW_KG_TOS } from "src/services/state/uiState.store";
+import { regionFlattener } from "src/util/regionFlattener";
+
+const noMethodDisplayName = 'No methods described'
+
+/**
+ * param for .toFixed method
+ * 6: nm
+ * 3: um
+ * 0: mm
+ */
+const SPATIAL_SEARCH_PRECISION = 6
+/**
+ * in ms
+ */
+const SPATIAL_SEARCH_DEBOUNCE = 500
+
+export function temporaryFilterDataentryName(name: string):string{
+  return /autoradiography/.test(name)
+    ? 'autoradiography'
+    : NO_METHODS === name
+      ? noMethodDisplayName
+      : name
+}
+
+function generateToken() {
+  return Date.now().toString()
+}
+
+@Injectable({
+  providedIn: 'root'
+})
+export class DatabrowserService implements OnDestroy{
+
+  public darktheme: boolean = false
+
+  public instantiatedWidgetUnits: WidgetUnit[] = []
+  public queryData: (arg:{regions: any[], template:any, parcellation: any}) => void = (arg) => {
+    const { dataBrowser, widgetUnit } = this.createDatabrowser(arg)
+    this.instantiatedWidgetUnits.push(widgetUnit.instance)
+    widgetUnit.onDestroy(() => {
+      this.instantiatedWidgetUnits = this.instantiatedWidgetUnits.filter(db => db !== widgetUnit.instance)
+    })
+  }
+  public createDatabrowser:  (arg:{regions:any[], template:any, parcellation:any}) => {dataBrowser: ComponentRef<DataBrowser>, widgetUnit:ComponentRef<WidgetUnit>}
+  public getDataByRegion: ({regions, parcellation, template}:{regions:any[], parcellation:any, template: any}) => Promise<DataEntry[]> = ({regions, parcellation, template}) => new Promise((resolve, reject) => {
+    this.lowLevelQuery(template.name, parcellation.name)
+      .then(de => this.filterDEByRegion.transform(de, regions, parcellation.regions.map(regionFlattener).reduce((acc, item) => acc.concat(item), [])))
+      .then(resolve)
+      .catch(reject)
+  })
+
+  private filterDEByRegion: FilterDataEntriesByRegion = new FilterDataEntriesByRegion()
+  private dataentries: DataEntry[] = []
+  private fetchDataStatus$: Observable<any>
+
+  private subscriptions: Subscription[] = []
+  public fetchDataObservable$: Observable<any>
+  public manualFetchDataset$: BehaviorSubject<null> = new BehaviorSubject(null)
+
+  private fetchSpatialData$: Observable<any>
+  public spatialDatasets$: Observable<any>
+
+  constructor(
+    private workerService: AtlasWorkerService,
+    private constantService: AtlasViewerConstantsServices,
+    private store: Store<ViewerConfiguration>
+  ){
+
+    this.subscriptions.push(
+      this.store.pipe(
+        select('ngViewerState')
+      ).subscribe(layersInterface => 
+        this.ngLayers = new Set(layersInterface.layers.map(l => l.source.replace(/^nifti\:\/\//, ''))))
+    )
+
+    this.subscriptions.push(
+      store.pipe(
+        select('dataStore'),
+        safeFilter('fetchedDataEntries'),
+        map(v => v.fetchedDataEntries)
+      ).subscribe(de => {
+        this.dataentries = de
+      })
+    )
+
+  this.fetchSpatialData$ = combineLatest(
+    this.store.pipe(
+      select('viewerState'),
+      select('navigation')
+    ),
+    this.store.pipe(
+      select('viewerState'),
+      select('templateSelected')
+    )
+  ).pipe(
+    debounceTime(SPATIAL_SEARCH_DEBOUNCE)
+  )
+
+  this.spatialDatasets$ = this.fetchSpatialData$.pipe(
+    filter(([navigation, templateSelected]) => !!navigation && !!navigation.position && !!templateSelected && !!templateSelected.name),
+    switchMap(([navigation, templateSelected]) => {
+
+        /**
+       * templateSelected and templateSelected.name must be defined for spatial search
+       */
+      if (!templateSelected || !templateSelected.name)
+        return from(Promise.reject('templateSelected must not be empty'))
+      const encodedTemplateName = encodeURI(templateSelected.name)
+
+      // in mm
+      const center = navigation.position.map(n=>n/1e6)
+      const searchWidth = this.constantService.spatialWidth / 4 * navigation.zoom / 1e6
+      const pt1 = center.map(v => (v - searchWidth).toFixed(SPATIAL_SEARCH_PRECISION))
+      const pt2 = center.map(v => (v + searchWidth).toFixed(SPATIAL_SEARCH_PRECISION))
+      
+      return from(fetch(`${this.constantService.backendUrl}datasets/spatialSearch/templateName/${encodedTemplateName}/bbox/${pt1.join('_')}__${pt2.join("_")}`)
+        .then(res => res.json()))
+    }),
+    catchError((err) => (console.log(err), of([])))
+  )
+
+  this.fetchDataObservable$ = combineLatest(
+      this.store.pipe(
+        select('viewerState'),
+        safeFilter('templateSelected'),
+        tap(({templateSelected}) => this.darktheme = templateSelected.useTheme === 'dark'),
+        map(({templateSelected})=>(templateSelected.name)),
+        distinctUntilChanged()
+      ),
+      this.store.pipe(
+        select('viewerState'),
+        safeFilter('parcellationSelected'),
+        map(({parcellationSelected})=>(parcellationSelected.name)),
+        distinctUntilChanged()
+      ),
+      this.manualFetchDataset$
+    )
+
+    this.fetchDataStatus$ = combineLatest(
+      this.fetchDataObservable$
+    )
+
+    this.subscriptions.push(
+      this.spatialDatasets$.subscribe(arr => {
+        this.store.dispatch({
+          type: FETCHED_SPATIAL_DATA,
+          fetchedDataEntries: arr
+        })
+        this.store.dispatch({
+          type : UPDATE_SPATIAL_DATA,
+          totalResults : arr.length
+        })
+      })
+    )
+
+    this.subscriptions.push(
+      this.fetchDataObservable$.pipe(
+        debounceTime(16)
+      ).subscribe((param : [string, string, null] ) => this.fetchData(param[0], param[1]))
+    )
+
+    this.subscriptions.push(
+      fromEvent(this.workerService.worker, 'message').pipe(
+        filter((message:MessageEvent) => message && message.data && message.data.type === 'RETURN_REBUILT_REGION_SELECTION_TREE'),
+        map(message => message.data),
+      ).subscribe((payload:any) => {
+        /**
+         * rebuiltSelectedRegion contains super region that are 
+         * selected as a result of all of its children that are selectted
+         */
+        const { rebuiltSelectedRegions, rebuiltSomeSelectedRegions } = payload
+        /**
+         * apply filter and populate databrowser instances
+         */
+      })
+    )
+  }
+
+  ngOnDestroy(){
+    this.subscriptions.forEach(s => s.unsubscribe())
+  }
+
+  public fetchPreviewData(datasetName: string){
+    const encodedDatasetName = encodeURI(datasetName)
+    return new Promise((resolve, reject) => {
+      fetch(`${this.constantService.backendUrl}datasets/preview/${encodedDatasetName}`)
+        .then(res => res.json())
+        .then(resolve)
+        .catch(reject)
+    })
+  }
+
+  public ngLayers : Set<string> = new Set()
+  public showNewNgLayer({ url }):void{
+
+    const layer = {
+      name : url,
+      source : `nifti://${url}`,
+      mixability : 'nonmixable',
+      shader : this.constantService.getActiveColorMapFragmentMain()
+    }
+    this.store.dispatch({
+      type: ADD_NG_LAYER,
+      layer
+    })
+  }
+
+  private dispatchData(arr:DataEntry[]){
+    this.store.dispatch({
+      type : FETCHED_DATAENTRIES,
+      fetchedDataEntries : arr
+    })
+  }
+
+  public fetchedFlag: boolean = false
+  public fetchError: string
+  public fetchingFlag: boolean = false
+  private mostRecentFetchToken: any
+
+  private lowLevelQuery(templateName: string, parcellationName: string){
+    const encodedTemplateName = encodeURI(templateName)
+    const encodedParcellationName = encodeURI(parcellationName)
+    return Promise.all([
+      fetch(`${this.constantService.backendUrl}datasets/templateName/${encodedTemplateName}`)
+        .then(res => res.json()),
+      fetch(`${this.constantService.backendUrl}datasets/parcellationName/${encodedParcellationName}`)
+        .then(res => res.json())
+    ])
+      .then(arr => [...arr[0], ...arr[1]])
+      /**
+       * remove duplicates
+       */
+      .then(arr => arr.reduce((acc, item) => {
+        const newMap = new Map(acc)
+        return newMap.set(item.name, item)
+      }, new Map()))
+      .then(map => Array.from(map.values() as DataEntry[]))
+  }
+
+  private fetchData(templateName: string, parcellationName: string){
+    this.dispatchData([])
+
+    const requestToken = generateToken()
+    this.mostRecentFetchToken = requestToken
+    this.fetchingFlag = true
+    
+    this.lowLevelQuery(templateName, parcellationName)
+      .then(array => {
+        if (this.mostRecentFetchToken === requestToken) {
+          this.dispatchData(array)
+          this.mostRecentFetchToken = null
+          this.fetchedFlag = true
+          this.fetchingFlag = false
+          this.fetchError = null
+        }
+      })
+      .catch(e => {
+        if (this.mostRecentFetchToken === requestToken) {
+          this.fetchingFlag = false
+          this.mostRecentFetchToken = null
+          this.fetchError = 'Fetching dataset error.'
+          console.warn('Error fetching dataset', e)
+          /**
+           * TODO
+           * retry?
+           */
+        }
+      })
+  }
+
+  removeNgLayer({ url }) {
+    this.store.dispatch({
+      type : REMOVE_NG_LAYER,
+      layer : {
+        name : url
+      }
+    })
+  }
+
+  rebuildRegionTree(selectedRegions, regions){
+    this.workerService.worker.postMessage({
+      type: 'BUILD_REGION_SELECTION_TREE',
+      selectedRegions,
+      regions
+    })
+  }
+
+  public dbComponentInit(db:DataBrowser){
+    this.store.dispatch({
+      type: SHOW_KG_TOS
+    })
+  }
+
+  public getModalityFromDE = getModalityFromDE
+
+  public getBackgroundColorStyleFromRegion(region:any = null){
+    return region && region.rgb
+      ? `rgb(${region.rgb.join(',')})`
+      : `white`
+  }
+}
+
+
+export function reduceDataentry(accumulator:{name:string, occurance:number}[], dataentry: DataEntry) {
+  const methods = dataentry.methods
+    .reduce((acc, item) => acc.concat(
+      item.length > 0
+        ? item
+        : NO_METHODS ), [])
+    .map(temporaryFilterDataentryName)
+
+  const newDE = Array.from(new Set(methods))
+    .filter(m => !accumulator.some(a => a.name === m))
+
+  return newDE.map(name => {
+    return {
+      name,
+      occurance: 1
+    }
+  }).concat(accumulator.map(({name, occurance, ...rest}) => {
+    return {
+      ...rest,
+      name,
+      occurance: methods.some(m => m === name)
+        ? occurance + 1
+        : occurance
+    }
+  }))
+}
+
+export function getModalityFromDE(dataentries:DataEntry[]):CountedDataModality[] {
+  return dataentries.reduce((acc, de) => reduceDataentry(acc, de), [])
+}
+
+
+export interface CountedDataModality{
+  name: string
+  occurance: number
+  visible: boolean
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/databrowser/databrowser.component.ts b/src/ui/databrowserModule/databrowser/databrowser.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e9ca1b1e011606c13cf5c31b1dea9c5e9e0af942
--- /dev/null
+++ b/src/ui/databrowserModule/databrowser/databrowser.component.ts
@@ -0,0 +1,165 @@
+import { Component, OnDestroy, OnInit, ViewChild, Input } from "@angular/core";
+import { DataEntry } from "src/services/stateStore.service";
+import { Subscription, merge } from "rxjs";
+import { DatabrowserService, CountedDataModality } from "../databrowser.service";
+import { ModalityPicker } from "../modalityPicker/modalityPicker.component";
+
+@Component({
+  selector : 'data-browser',
+  templateUrl : './databrowser.template.html',
+  styleUrls : [
+    `./databrowser.style.css`
+  ]
+})
+
+export class DataBrowser implements OnDestroy,OnInit{
+
+  @Input()
+  public regions: any[] = []
+
+  @Input()
+  public template: any
+  
+  @Input()
+  public parcellation: any
+
+  public dataentries: DataEntry[] = []
+
+  public currentPage: number = 0
+  public hitsPerPage: number = 5
+
+  public fetchingFlag: boolean = false
+  public fetchError: boolean = false
+  /**
+   * TODO filter types
+   */
+  private subscriptions : Subscription[] = []
+  public countedDataM: CountedDataModality[] = []
+  public visibleCountedDataM: CountedDataModality[] = []
+
+  @ViewChild(ModalityPicker)
+  modalityPicker: ModalityPicker
+
+  get darktheme(){
+    return this.dbService.darktheme
+  }
+
+  /**
+   * TODO
+   * viewport
+   * user defined filter
+   * etc
+   */
+  public gemoetryFilter: any
+
+  constructor(
+    private dbService: DatabrowserService
+  ){
+
+  }
+
+  ngOnInit(){
+    this.dbService.dbComponentInit(this)
+    this.regions = this.regions.map(r => {
+      /**
+       * TODO to be replaced with properly region UUIDs from KG
+       */
+      return {
+        id: `${this.parcellation.name}/${r.name}`,
+        ...r
+      }
+    })
+    const { regions, parcellation, template } = this
+    this.fetchingFlag = true
+    this.dbService.getDataByRegion({ regions, parcellation, template })
+      .then(de => {
+        this.dataentries = de
+        this.fetchingFlag = false
+        return de
+      })
+      .then(this.dbService.getModalityFromDE)
+      .then(modalities => {
+        this.countedDataM = modalities
+      })
+      .catch(e => {
+        console.error(e)
+        this.fetchingFlag = false
+        this.fetchError = true
+      })
+
+    this.subscriptions.push(
+      merge(
+        // this.dbService.selectedRegions$,
+        this.dbService.fetchDataObservable$
+      ).subscribe(() => {
+        this.resetCurrentPage()
+        /**
+         * Only reset modality picker
+         * resetting all creates infinite loop
+         */
+        this.clearAll()
+      })
+    )
+    
+    /**
+     * TODO fix
+     */
+    // this.subscriptions.push(
+    //   this.filterApplied$.subscribe(() => this.currentPage = 0)
+    // )
+  }
+
+  ngOnDestroy(){
+    this.subscriptions.forEach(s=>s.unsubscribe())
+  }
+
+  clearAll(){
+    this.countedDataM = this.countedDataM.map(cdm => {
+      return {
+        ...cdm,
+        visible: false
+      }
+    })
+    this.visibleCountedDataM = []
+    this.resetCurrentPage()
+  }
+
+  handleModalityFilterEvent(modalityFilter:CountedDataModality[]){
+    this.countedDataM = modalityFilter
+    this.visibleCountedDataM = modalityFilter.filter(dm => dm.visible)
+    this.resetCurrentPage()
+  }
+
+  retryFetchData(event: MouseEvent){
+    event.preventDefault()
+    this.dbService.manualFetchDataset$.next(null)
+  }
+
+  public showParcellationList: boolean = false
+  
+  public filePreviewName: string
+  onShowPreviewDataset(payload: {datasetName:string, event:MouseEvent}){
+    const { datasetName, event } = payload
+    this.filePreviewName = datasetName
+  }
+
+  /**
+   * when filter changes, it is necessary to set current page to 0,
+   * or one may overflow and see no dataset
+   */
+  resetCurrentPage(){
+    this.currentPage = 0
+  }
+
+  resetFilters(event?:MouseEvent){
+    this.clearAll()
+  }
+
+  getBackgroundColorStyleFromRegion(region:any) {
+    return this.dbService.getBackgroundColorStyleFromRegion(region)
+  }
+}
+
+export interface DataEntryFilter{
+  filter: (dataentries:DataEntry[]) => DataEntry[]
+}
diff --git a/src/ui/databrowser/databrowser.style.css b/src/ui/databrowserModule/databrowser/databrowser.style.css
similarity index 56%
rename from src/ui/databrowser/databrowser.style.css
rename to src/ui/databrowserModule/databrowser/databrowser.style.css
index 64e569802b2976ce09edb48878e094b9eaa4cbc3..03c84492265b2bd08506129d0c16e782c60788e7 100644
--- a/src/ui/databrowser/databrowser.style.css
+++ b/src/ui/databrowserModule/databrowser/databrowser.style.css
@@ -63,18 +63,13 @@ div[databrowserheader]
   white-space: nowrap;
 }
 
-div[filterBlock]
-{
-  padding : 0.5em 1em;
-}
-
 div[filterBlock] > *
 {
   white-space: nowrap;
   overflow: hidden;
 }
 
-div.clickable:hover
+.clickable:hover
 {
   color:#dbb556;
   cursor:default;
@@ -119,9 +114,6 @@ div[noSelectedRegion]
 dataset-viewer
 {
   display: block;
-  margin: 1em;
-  padding: 1em;
-  background-color: rgba(128, 128, 128, 0.2);
 }
 
 div[regionsContainer]
@@ -133,4 +125,100 @@ div[regionsContainer]
 div[regionContainer]
 {
   margin-top:0.5em;
+}
+
+modality-picker
+{
+  font-size: 90%;
+  display:inline-block;
+}
+
+.filterWrapper
+{
+  white-space:normal;
+  max-height: 15em;
+  overflow-y:auto;
+  overflow-x:hidden;
+}
+
+.spinnerAnimationCircleContainer
+{
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+.spinnerAnimationCircleContainer > .spinnerAnimationCircle
+{
+  font-size: 300%;
+}
+
+.parcellationSelectionWrapper
+{
+  width: 100%;
+  display:flex;
+  flex-direction: row;
+  align-items: flex-start;
+}
+.parcellationSelectionWrapper > *:first-child
+{
+  flex: 1 0 0;
+}
+.parcellationSelectionWrapper > *:last-child
+{
+  flex: 0 0 0;
+}
+
+.toggleParcellationBtn
+{
+  margin-left: -2.5em;
+  z-index: 1;
+}
+
+radio-list
+{
+  display: block;
+}
+
+/* datawrapper */
+:host .dataEntryWrapper
+{
+  white-space: nowrap;
+  overflow: hidden;
+  width:200%;
+  padding: 0;
+  transition: transform 190ms ease;
+}
+
+.dataEntryWrapper > *
+{
+  vertical-align: top;
+  white-space: initial;
+  width: 50%;
+  display:inline-block;
+  padding: 0.5em 1em;
+}
+
+.filePreviewContainer
+{
+  max-height: 100%;
+  overflow:auto;
+}
+
+.overflow-container
+{
+  overflow:hidden;
+  width: 100%;
+  height: 100%;
+  margin: 0;
+  padding: 0;
+}
+
+.dot
+{
+  width: 0.5rem;
+  height: 0.5rem;
+  display: inline-block;
+  border-radius: 50%;
+  background-color:white;
 }
\ No newline at end of file
diff --git a/src/ui/databrowserModule/databrowser/databrowser.template.html b/src/ui/databrowserModule/databrowser/databrowser.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..515a94d8331c6235c7ef253d927c7d159ba597ea
--- /dev/null
+++ b/src/ui/databrowserModule/databrowser/databrowser.template.html
@@ -0,0 +1,146 @@
+<div class="overflow-container">
+
+  <div
+    [ngStyle]="filePreviewName ? {'transform': 'translateX(-50%)'} : {}"
+    class="dataEntryWrapper">
+
+    <!-- main window -->
+    <div
+      class="t-a-ease-500"
+      [style.height]="filePreviewName ? '0px' : 'auto'">
+
+      <!-- description -->
+      <readmore-component>
+        <div class="p-2 mh-20em overflow-auto">
+          Datasets relevant to
+          <span
+            *ngFor="let region of regions"
+            class="badge badge-secondary mr-1 mw-100">
+            <span [ngStyle]="{backgroundColor:getBackgroundColorStyleFromRegion(region)}" class="dot">
+              
+            </span>
+            <span class="d-inline-block mw-100 overflow-hidden text-truncate">
+              {{ region.name }}
+            </span>
+          </span>
+        </div>
+      </readmore-component>
+      
+      <!-- modality picker -->
+      <div>
+        <span
+          placement="right"
+          container="body"
+          [popover]="countedDataM.length > 0 ? modalityPicker : null"
+          [outsideClick]="true"
+          [containerClass]="darktheme ? 'darktheme' : ''"
+          class="clickable btn-sm btn btn-secondary btn-block">
+          Filter by Modality <small *ngIf="visibleCountedDataM.length as visibleDMLength">({{ visibleDMLength }})</small>
+        </span>
+      </div>
+    
+      <!-- datasets container -->
+      <div
+        *ngIf="fetchingFlag; else fetched"
+        class="spinnerAnimationCircleContainer">
+        <div class="spinnerAnimationCircle"></div>
+        <div>Fetching datasets...</div>
+      </div>
+
+    </div>
+
+    <!-- file previewer -->
+    <div
+      class="filePreview">
+      <div class="filePreviewContainer">
+        <div (click)="filePreviewName=null" class="rounded-circle btn btn-sm btn-outline-secondary">
+          <i class="fas fa-arrow-left"></i>
+        </div>
+        <preview-component
+          *ngIf="filePreviewName"
+          [datasetName]="filePreviewName">
+
+        </preview-component>
+          
+      </div>
+    </div>
+  </div>
+</div>
+
+<ng-template #modalityPicker>
+  <div class="filterWrapper">
+    <modality-picker
+      (click)="$event.stopPropagation();"
+      class="mw-100"
+      [countedDataM]="countedDataM"
+      (modalityFilterEmitter)="handleModalityFilterEvent($event)">
+
+    </modality-picker>
+  </div>
+</ng-template>
+
+<ng-template #fetched>
+  <div class="ml-2 mr-2 alert alert-danger" *ngIf="fetchError; else showData">
+    <i class="fas fa-exclamation-triangle"></i> Error fetching data. <a href="#" (click)="retryFetchData($event)" class="btn btn-link text-info">retry</a>
+  </div>
+</ng-template>
+
+<ng-template #showData>
+  <!-- datawrapper -->
+  <div *ngIf="dataentries | filterDataEntriesByMethods : visibleCountedDataM as filteredDataEntry"
+>
+
+    <!-- dataentries -->
+    <div class="dataEntry">
+      <div>
+        <i *ngIf="dataentries.length > 0">
+          {{ dataentries.length }} total results. 
+          <span
+            *ngIf="visibleCountedDataM.length > 0 "> 
+            {{ filteredDataEntry.length }} 
+            filtered results.
+            <a
+              href="#"
+              class="btn btn-sm btn-link"
+              (click)="resetFilters($event)">reset filters
+            </a>
+          </span>
+        </i>
+        <i *ngIf="dataentries.length === 0">
+          No results to show.
+        </i>
+      </div>
+      <div *ngIf="dataentries.length > 0">
+        <dataset-viewer
+          class="mt-1"
+          *ngFor="let dataset of filteredDataEntry | searchResultPagination : currentPage : hitsPerPage"
+          (showPreviewDataset)="onShowPreviewDataset($event)"
+          [dataset]="dataset">
+          <div regionTagsContainer>
+
+            <!-- TODO may want to separate the region badge into a separate component -->
+            <span
+              *ngFor="let region of dataset.parcellationRegion"
+              class="badge badge-secondary mr-1 mw-100">
+              <span [ngStyle]="{backgroundColor:getBackgroundColorStyleFromRegion(region)}" class="dot">
+                
+              </span>
+              <span class="d-inline-block mw-100 overflow-hidden text-truncate">
+                {{ region.name }}
+              </span>
+            </span>
+          </div>
+        </dataset-viewer>
+      </div>
+      
+      <pagination-component
+        (paginationChange)="currentPage = $event"
+        [hitsPerPage]="hitsPerPage"
+        [total]="filteredDataEntry.length"
+        [currentPage]="currentPage">
+      </pagination-component>
+    </div>
+
+  </div>
+</ng-template>
+
diff --git a/src/ui/databrowserModule/datasetViewer/datasetViewer.component.ts b/src/ui/databrowserModule/datasetViewer/datasetViewer.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..7be8d563234f7e854b1bda067e07a1a758471718
--- /dev/null
+++ b/src/ui/databrowserModule/datasetViewer/datasetViewer.component.ts
@@ -0,0 +1,42 @@
+import { Component, Input, Output, EventEmitter, ViewChild, ElementRef } from "@angular/core";
+import { DataEntry } from "src/services/stateStore.service";
+
+@Component({
+  selector : 'dataset-viewer',
+  templateUrl : './datasetViewer.template.html',
+  styleUrls : ['./datasetViewer.style.css']
+})
+
+export class DatasetViewerComponent{
+  @Input() dataset : DataEntry
+  
+  @Output() showPreviewDataset: EventEmitter<{datasetName:string, event:MouseEvent}> = new EventEmitter()
+  @ViewChild('kgrRef', {read:ElementRef}) kgrRef: ElementRef
+
+  previewDataset(event:MouseEvent){
+    if (!this.dataset.preview) return
+    this.showPreviewDataset.emit({
+      event,
+      datasetName: this.dataset.name
+    })
+    event.stopPropagation()
+  }
+
+  clickMainCard(event:MouseEvent) {
+    if (this.kgrRef) this.kgrRef.nativeElement.click()
+  }
+
+  get methods(): string[]{
+    return this.dataset.activity.reduce((acc, act) => {
+      return acc.concat(act.methods)
+    }, [])
+  }
+
+  get hasKgRef(): boolean{
+    return this.kgReference.length > 0
+  }
+
+  get kgReference(): string[] {
+    return this.dataset.kgReference.map(ref => `https://doi.org/${ref}`)
+  }
+}
\ No newline at end of file
diff --git a/src/ui/datasetViewer/datasetViewer.style.css b/src/ui/databrowserModule/datasetViewer/datasetViewer.style.css
similarity index 50%
rename from src/ui/datasetViewer/datasetViewer.style.css
rename to src/ui/databrowserModule/datasetViewer/datasetViewer.style.css
index f95c2e97aa13bcc73d1324e153874bd4cdb2a063..17836b855bb96876f0781e7a30e4974aca789d72 100644
--- a/src/ui/datasetViewer/datasetViewer.style.css
+++ b/src/ui/databrowserModule/datasetViewer/datasetViewer.style.css
@@ -7,4 +7,20 @@ flat-tree-component
 :host-context([darktheme="true"]) hr
 {
   border-color: rgba(0, 0, 0, 0.5);
+}
+
+.dataset-pill
+{
+  font-size:80%;
+}
+
+.ds-container
+{
+
+  background-color: rgba(128, 128, 128, 0.2);
+}
+
+.preview-container
+{
+  flex: 0 0 2em;
 }
\ No newline at end of file
diff --git a/src/ui/databrowserModule/datasetViewer/datasetViewer.template.html b/src/ui/databrowserModule/datasetViewer/datasetViewer.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..c1248259e4ac13b17b3395b63ef41d1d3396b277
--- /dev/null
+++ b/src/ui/databrowserModule/datasetViewer/datasetViewer.template.html
@@ -0,0 +1,37 @@
+<div class="d-flex">
+  <div
+    *ngIf="dataset; else defaultDisplay"
+    (click)="clickMainCard($event)"
+    [ngClass]="{'muted': !hasKgRef}"
+    class="p-2 ds-container main-container"
+    [hoverable]="{translateY:-2,disable:!hasKgRef}">
+    <ng-content select="[regionTagsContainer]">
+
+    </ng-content>
+    <div title>
+      {{ dataset.name }}
+    </div>
+
+    <a
+      *ngFor="let kgr of kgReference"
+      class="btn btn-sm btn-link"
+      target="_blank"
+      [href]="kgr"
+      hidden
+      #kgrRef>
+      Show more info on this dataset <i class="fas fa-external-link-alt"></i>
+    </a>
+  </div>
+
+  <div
+    *ngIf="dataset.preview"
+    (click)="previewDataset($event)"
+    class="ds-container ml-1 p-2 preview-container text-muted d-flex align-items-center"
+    [hoverable]="{translateY:-3}">
+    <i class="fas fa-eye"></i>
+  </div>
+</div>
+
+<ng-template #defaultDisplay>
+  Nothing to display ...
+</ng-template>
diff --git a/src/ui/fileviewer/chart.interface.ts b/src/ui/databrowserModule/fileviewer/chart.interface.ts
similarity index 100%
rename from src/ui/fileviewer/chart.interface.ts
rename to src/ui/databrowserModule/fileviewer/chart.interface.ts
diff --git a/src/ui/databrowserModule/fileviewer/dedicated/dedicated.component.ts b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f7ddd748850aef15f3f9a6bc50845cafe3ee4db3
--- /dev/null
+++ b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.component.ts
@@ -0,0 +1,46 @@
+import { Component, Input } from "@angular/core";
+import { ViewerPreviewFile } from "src/services/state/dataStore.store";
+import { DatabrowserService } from "../../databrowser.service";
+
+@Component({
+  selector : 'dedicated-viewer',
+  templateUrl : './dedicated.template.html',
+  styleUrls : [
+    `./dedicated.style.css`
+  ]
+})
+
+export class DedicatedViewer{
+  @Input() previewFile : ViewerPreviewFile
+
+  constructor(
+    private dbService:DatabrowserService,
+  ){
+
+  }
+
+  get isShowing(){
+    return this.dbService.ngLayers.has(this.previewFile.url)
+  }
+
+  showDedicatedView(){
+    this.dbService.showNewNgLayer({ url: this.previewFile.url })
+  }
+
+  removeDedicatedView(){
+    this.dbService.removeNgLayer({ url: this.previewFile.url })
+  }
+  
+  click(event:MouseEvent){
+    event.preventDefault()
+    this.isShowing
+      ? this.removeDedicatedView()
+      : this.showDedicatedView()
+  }
+
+  get tooltipText(){
+    return this.isShowing
+      ? 'Remove this file in the viewer'
+      : 'View this file in the viewer'
+  }
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/fileviewer/dedicated/dedicated.style.css b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..c1ee41eefe5349674cd7d350e80275c86bea4843
--- /dev/null
+++ b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.style.css
@@ -0,0 +1,5 @@
+a
+{
+  margin: 0 1em;
+  transition: all 200ms ease;
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/fileviewer/dedicated/dedicated.template.html b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..3517468edf64042507f0768dead716797805adfe
--- /dev/null
+++ b/src/ui/databrowserModule/fileviewer/dedicated/dedicated.template.html
@@ -0,0 +1,13 @@
+<div class="alert">
+  You can directly preview this nifti file overlaying the atlas viewer.
+</div>
+
+<a 
+  href="#"
+  [tooltip]="tooltipText"
+  container="body"
+  class="btn btn-sm rounded-circle"
+  [ngClass]="isShowing ? 'btn-primary' : 'btn-outline-secondary'"
+  (click)="click($event)">
+  <i class="far fa-eye"></i>
+</a>
\ No newline at end of file
diff --git a/src/ui/fileviewer/fileviewer.component.ts b/src/ui/databrowserModule/fileviewer/fileviewer.component.ts
similarity index 79%
rename from src/ui/fileviewer/fileviewer.component.ts
rename to src/ui/databrowserModule/fileviewer/fileviewer.component.ts
index 7ad43ca02cf943a4b2bb75df913c7683f45c925e..1048721014ffc5d81ff849ec2ccb635c2611e6ee 100644
--- a/src/ui/fileviewer/fileviewer.component.ts
+++ b/src/ui/databrowserModule/fileviewer/fileviewer.component.ts
@@ -1,9 +1,9 @@
 import { Component, Input, OnChanges, OnDestroy, ViewChild, ElementRef, OnInit, Output, EventEmitter } from '@angular/core'
 
 import { DomSanitizer } from '@angular/platform-browser';
-import { File } from '../../services/stateStore.service';
 import { interval,from } from 'rxjs';
 import { switchMap,take,retry } from 'rxjs/operators'
+import { ViewerPreviewFile } from 'src/services/state/dataStore.store';
 
 @Component({
   selector : 'file-viewer',
@@ -14,7 +14,10 @@ import { switchMap,take,retry } from 'rxjs/operators'
 })
 
 export class FileViewer implements OnChanges,OnDestroy,OnInit{
-  @Input() searchResultFile : File
+  /**
+   * fetched directly from KG
+   */
+  @Input() previewFile : ViewerPreviewFile
   
   @ViewChild('childChart') childChart : ChartComponentInterface
 
@@ -39,11 +42,7 @@ export class FileViewer implements OnChanges,OnDestroy,OnInit{
   }
 
   get downloadUrl(){
-    return this.searchResultFile.url ? 
-      this.searchResultFile.url : 
-      this._downloadUrl ? 
-        this.sanitizer.bypassSecurityTrustResourceUrl(this._downloadUrl)  :
-        null
+    return this.previewFile.url
   }
 
   /* TODO require better way to check if a chart exists */
@@ -53,8 +52,7 @@ export class FileViewer implements OnChanges,OnDestroy,OnInit{
     const timerSet$ = timer$.pipe(
       switchMap(()=>from(new Promise((rs,rj)=>{
         if(!this.childChart)
-          rj('chart not defined after 500ms')
-        
+          return rj('chart not defined after 500ms')
         this.childChart.canvas.nativeElement.toBlob((blob)=>{
           blob ? rs(blob) : rj('blob is undefined')
           
@@ -69,8 +67,8 @@ export class FileViewer implements OnChanges,OnDestroy,OnInit{
     },(err)=>console.warn('warning',err))
 
 
-    if(!this.searchResultFile.url && this.searchResultFile.data){
-      const stringJson = JSON.stringify(this.searchResultFile.data)
+    if(!this.previewFile.url && this.previewFile.data){
+      const stringJson = JSON.stringify(this.previewFile.data)
       const newBlob = new Blob([stringJson],{type:'application/octet-stream'})
       this._downloadUrl = URL.createObjectURL(newBlob)
     }
@@ -79,14 +77,16 @@ export class FileViewer implements OnChanges,OnDestroy,OnInit{
   private revokeUrls(){
     if(this._downloadUrl){
       URL.revokeObjectURL(this._downloadUrl)
+      this._downloadUrl = null
     }
     if(this._pngDownloadUrl){
       URL.revokeObjectURL(this._pngDownloadUrl)
+      this._pngDownloadUrl = null
     }
   }
 
   get downloadName(){
-    return this.searchResultFile.filename
+    return this.previewFile.name
   }
 
   get downloadPng(){
diff --git a/src/ui/fileviewer/fileviewer.style.css b/src/ui/databrowserModule/fileviewer/fileviewer.style.css
similarity index 100%
rename from src/ui/fileviewer/fileviewer.style.css
rename to src/ui/databrowserModule/fileviewer/fileviewer.style.css
diff --git a/src/ui/databrowserModule/fileviewer/fileviewer.template.html b/src/ui/databrowserModule/fileviewer/fileviewer.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..033ed3a4521d45d2ef0a8803a1023bb2c6416667
--- /dev/null
+++ b/src/ui/databrowserModule/fileviewer/fileviewer.template.html
@@ -0,0 +1,112 @@
+<div *ngIf = "!previewFile">
+  previewFile as an input is required for this component to work...
+</div>
+
+<!-- KG Entry Viewer -->
+<!-- <kg-entry-viewer *ngIf = "previewFile.kgID && false" [dataset] = "previewFile">
+</kg-entry-viewer> -->
+
+<!-- citation -->
+<!-- <citations-component 
+  citationContainer
+  *ngIf = "previewFile && !previewFile.kgID"
+  [properties] = "previewFile.datasetProperties">
+</citations-component> -->
+
+<!-- file viewer -->
+<div 
+  *ngIf = "previewFile"
+  [ngSwitch]="previewFile.mimetype">
+
+  <div emptyRow *ngSwitchCase = "'application/octet-stream'">
+    <!-- downloadable data -->
+  </div>
+
+  <!-- image  -->
+  <div *ngSwitchCase="'image/jpeg'" >
+    <!-- TODO figure out a more elegant way of dealing with draggable img -->
+    <img draggable = "false" [src] = "previewFile.url" />
+  </div>
+
+  <!-- data container -->
+  <div [ngSwitch]="previewFile.data.chartType" *ngSwitchCase="'application/json'">
+    <radar-chart
+      #childChart
+      [colors] = "previewFile.data.colors"
+      [options] = "previewFile.data.chartOptions"
+      [labels]="previewFile.data.labels"
+      [radarDatasets]="previewFile.data.datasets"
+      *ngSwitchCase="'radar'">
+
+    </radar-chart>
+    <line-chart
+      #childChart
+      [colors] = "previewFile.data.colors"
+      [options] = "previewFile.data.chartOptions"
+      [lineDatasets] = "previewFile.data.datasets"
+      *ngSwitchCase = "'line'">
+
+    </line-chart>
+    <div *ngSwitchDefault>
+      The json file is not a chart. I mean, we could dump the json, but would it really help?
+    </div>
+  </div>
+  <div *ngSwitchCase = "'application/hibop'">
+    <div mimetypeTextContainer>
+        You will need to install the HiBoP software on your computer, click the 'Download File' button and open the .hibop file.
+    </div>
+  </div>
+  <div *ngSwitchCase = "'application/nifti'">
+    <dedicated-viewer
+      [previewFile] = "previewFile">
+    </dedicated-viewer>
+  </div>
+  <div *ngSwitchCase = "'application/nehuba-layer'">
+    APPLICATION NEHUBA LAYER - NOT YET IMPLEMENTED
+    <!-- <dedicated-view-controller
+      [dedicatedViewString]="'nehuba-layer://'+previewFile.url"
+      [dedicatedViewNehubaLayerObject]="previewFile.data">
+
+    </dedicated-view-controller> -->
+  </div>
+  <div *ngSwitchDefault>
+    <div mimetypeTextContainer>
+      The selected file with the mimetype {{ previewFile.mimetype }} could not be displayed.
+    </div>
+  </div>
+</div>
+
+<!-- caption -->
+<!-- <readmore-component *ngIf = "previewFile.properties && previewFile.properties.description">
+  <small id = "captions">
+    {{ previewFile.properties.description }}
+  </small>
+</readmore-component> -->
+
+<div
+  anchorContainer
+  *ngIf="downloadUrl">
+  <a
+    class="btn btn-sm btn-outline-secondary rounded-circle"
+    tooltip="Download file"
+    container="body"
+    [href]="downloadUrl"
+    target="_blank"
+    download>
+    <i class="far fa-arrow-alt-circle-down"></i>
+  </a>
+</div>
+
+<div
+  anchorContainer
+  *ngIf="downloadPng">
+  <a
+    class="btn btn-sm btn-outline-secondary rounded-circle"
+    tooltip="Download chart as an image"
+    container="body"
+    [href]="downloadPng"
+    target="_blank"
+    download>
+    <i class="far fa-image"></i>
+  </a>
+</div>
diff --git a/src/ui/fileviewer/line/line.chart.component.ts b/src/ui/databrowserModule/fileviewer/line/line.chart.component.ts
similarity index 100%
rename from src/ui/fileviewer/line/line.chart.component.ts
rename to src/ui/databrowserModule/fileviewer/line/line.chart.component.ts
diff --git a/src/ui/fileviewer/line/line.chart.style.css b/src/ui/databrowserModule/fileviewer/line/line.chart.style.css
similarity index 100%
rename from src/ui/fileviewer/line/line.chart.style.css
rename to src/ui/databrowserModule/fileviewer/line/line.chart.style.css
diff --git a/src/ui/databrowserModule/fileviewer/line/line.chart.template.html b/src/ui/databrowserModule/fileviewer/line/line.chart.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..b3882ceae471095800bf33ff36a652f1090d5fa6
--- /dev/null
+++ b/src/ui/databrowserModule/fileviewer/line/line.chart.template.html
@@ -0,0 +1,26 @@
+<canvas 
+  *ngIf="shapedLineChartDatasets"
+  (mousewheel)="mousescroll($event)"
+  width='100%'
+  height='100%'
+  baseChart 
+  chartType="line"
+  [options]="chartOption"
+  [colors]="colors"
+  [datasets]="shapedLineChartDatasets"
+  [labels]="chartDataset.labels"
+  #canvas>
+</canvas>
+<a
+  [download]="csvTitle"
+  [href]="csvDataUrl"
+  tooltip="Download line graph as csv"
+  container="body"
+  class="btn btn-sm btn-outline-secondary rounded-circle"
+  *ngIf="shapedLineChartDatasets">
+  <i class="fas fa-file-csv"></i>
+</a>
+<span
+  *ngIf="!shapedLineChartDatasets">
+  datasets are required to display linear graph
+</span>
\ No newline at end of file
diff --git a/src/ui/fileviewer/radar/radar.chart.component.ts b/src/ui/databrowserModule/fileviewer/radar/radar.chart.component.ts
similarity index 100%
rename from src/ui/fileviewer/radar/radar.chart.component.ts
rename to src/ui/databrowserModule/fileviewer/radar/radar.chart.component.ts
diff --git a/src/ui/fileviewer/radar/radar.chart.style.css b/src/ui/databrowserModule/fileviewer/radar/radar.chart.style.css
similarity index 100%
rename from src/ui/fileviewer/radar/radar.chart.style.css
rename to src/ui/databrowserModule/fileviewer/radar/radar.chart.style.css
diff --git a/src/ui/fileviewer/radar/radar.chart.template.html b/src/ui/databrowserModule/fileviewer/radar/radar.chart.template.html
similarity index 100%
rename from src/ui/fileviewer/radar/radar.chart.template.html
rename to src/ui/databrowserModule/fileviewer/radar/radar.chart.template.html
diff --git a/src/ui/databrowserModule/modalityPicker/modalityPicker.component.ts b/src/ui/databrowserModule/modalityPicker/modalityPicker.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..01bca9eb65e9c2408932916af8f4ae4352239fb2
--- /dev/null
+++ b/src/ui/databrowserModule/modalityPicker/modalityPicker.component.ts
@@ -0,0 +1,64 @@
+import { Component, EventEmitter, Input, Output, OnChanges } from "@angular/core";
+import { CountedDataModality } from "../databrowser.service";
+
+@Component({
+  selector: 'modality-picker',
+  templateUrl: './modalityPicker.template.html',
+  styleUrls: [
+    './modalityPicker.style.css'
+  ]
+})
+
+export class ModalityPicker implements OnChanges{
+
+  public modalityVisibility: Set<string> = new Set()
+
+  @Input()
+  public countedDataM: CountedDataModality[] = []
+
+
+  public checkedModality: CountedDataModality[] = []
+
+  @Output()
+  public modalityFilterEmitter: EventEmitter<CountedDataModality[]> = new EventEmitter()
+
+  // filter(dataentries:DataEntry[]) {
+  //   return this.modalityVisibility.size === 0
+  //     ? dataentries
+  //     : dataentries.filter(de => de.activity.some(a => a.methods.some(m => this.modalityVisibility.has(this.dbService.temporaryFilterDataentryName(m)))))
+  // }
+
+  ngOnChanges(){
+    this.checkedModality = this.countedDataM.filter(d => d.visible)
+  }
+
+  /**
+   * TODO
+   * togglemodailty should emit event, and let parent handle state
+   */
+  toggleModality(modality: Partial<CountedDataModality>){
+    this.modalityFilterEmitter.emit(
+      this.countedDataM.map(d => d.name === modality.name
+        ? {
+          ...d,
+          visible: !d.visible
+        }
+        : d)
+    )
+  }
+
+  uncheckModality(modality:string){
+    this.toggleModality({name: modality})
+  }
+
+  clearAll(){
+    this.modalityFilterEmitter.emit(
+      this.countedDataM.map(d => {
+        return {
+          ...d,
+          visible: false
+        }
+      })
+    )
+  }
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/modalityPicker/modalityPicker.style.css b/src/ui/databrowserModule/modalityPicker/modalityPicker.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..3b98629ee72bc7cad5fe813ef8aaa846e2b03e50
--- /dev/null
+++ b/src/ui/databrowserModule/modalityPicker/modalityPicker.style.css
@@ -0,0 +1,10 @@
+div
+{
+  white-space: nowrap;
+}
+
+.clickable:hover
+{
+  color:#dbb556;
+  cursor:default;
+}
diff --git a/src/ui/databrowserModule/modalityPicker/modalityPicker.template.html b/src/ui/databrowserModule/modalityPicker/modalityPicker.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..f980ef04c3a2689d8258523bf383ff4d4b29beb6
--- /dev/null
+++ b/src/ui/databrowserModule/modalityPicker/modalityPicker.template.html
@@ -0,0 +1,27 @@
+<div class="ws-initial">
+  <div
+    *ngIf="checkedModality.length > 0"
+    (click)="clearAll()"
+    class="btn btn-sm btn-link ">
+    clear all
+  </div>
+  <pill-component
+    class="mw-60"
+    [containerStyle]="{backgroundColor:'rgba(128,128,128,0.5)'}"
+    [closeBtnStyle]="{backgroundColor:'rgba(128,128,128,0.8)'}"
+    (closeClicked)="uncheckModality(dataM.name)"
+    [title]="dataM.name"
+    *ngFor="let dataM of checkedModality">
+  
+  </pill-component>
+</div>
+
+<div
+  *ngFor="let datamodality of countedDataM"
+  (click)="toggleModality(datamodality)"
+  class="clickable">
+  <i [ngClass]="datamodality.visible ? 'far fa-check-square' : 'text-muted far fa-square'">
+
+  </i>
+  {{ datamodality.name }} <span class="text-muted">({{ datamodality.occurance }})</span>
+</div>
\ No newline at end of file
diff --git a/src/ui/databrowserModule/preview/preview.component.ts b/src/ui/databrowserModule/preview/preview.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..19921de6402afa653c9e2b6ec68352df13e7dfdd
--- /dev/null
+++ b/src/ui/databrowserModule/preview/preview.component.ts
@@ -0,0 +1,61 @@
+import { Component, Input, OnInit } from "@angular/core";
+import { DatabrowserService } from "../databrowser.service";
+import { ViewerPreviewFile } from "src/services/state/dataStore.store";
+
+const getRenderNodeFn = ({name : activeFileName = ''} = {}) => ({name = '', path = 'unpathed'}) => name
+? activeFileName === name
+  ? `<span class="text-warning">${name}</span>`
+  : name
+: path
+
+@Component({
+  selector: 'preview-component',
+  templateUrl: './preview.template.html',
+  styleUrls: [
+    './preview.style.css'
+  ]
+})
+
+export class PreviewComponent implements OnInit{
+  @Input() datasetName: string
+
+  public previewFiles: ViewerPreviewFile[] = []
+  public activeFile: ViewerPreviewFile
+  private error: string
+
+  constructor(
+    private dbrService:DatabrowserService
+  ){
+    this.renderNode = getRenderNodeFn()
+  }
+
+  previewFileClick(ev, el){
+    
+    ev.event.preventDefault()
+    ev.event.stopPropagation()
+
+    if(ev.inputItem.children.length > 0){
+      el.toggleCollapse(ev.inputItem)
+    }else{
+      this.activeFile = ev.inputItem
+      this.renderNode = getRenderNodeFn(this.activeFile)
+    }
+  }
+
+  public renderNode: (obj:any) => string
+
+  ngOnInit(){
+    if (this.datasetName) {
+      this.dbrService.fetchPreviewData(this.datasetName)
+        .then(json => {
+          this.previewFiles = json as ViewerPreviewFile[]
+          if (this.previewFiles.length > 0)
+            this.activeFile = this.previewFiles[0]
+            this.renderNode = getRenderNodeFn(this.activeFile)
+        })
+        .catch(e => {
+          this.error = JSON.stringify(e)
+        })
+    }
+  }
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/preview/preview.style.css b/src/ui/databrowserModule/preview/preview.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..23018c4cb9c1562bb329a8416a148bb819a503ed
--- /dev/null
+++ b/src/ui/databrowserModule/preview/preview.style.css
@@ -0,0 +1,12 @@
+:host
+{
+  display: block;
+  width: 100%;
+}
+
+.readmore-wrapper
+{
+  font-size: 80%;
+  max-height: 25em;
+  overflow: auto;
+}
\ No newline at end of file
diff --git a/src/ui/databrowserModule/preview/preview.template.html b/src/ui/databrowserModule/preview/preview.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..1eb28f46694f3252ffc923011d1106230a6543b8
--- /dev/null
+++ b/src/ui/databrowserModule/preview/preview.template.html
@@ -0,0 +1,22 @@
+<div *ngIf="activeFile">
+  {{ activeFile.name }}
+</div>
+<file-viewer
+  *ngIf="activeFile"
+  [previewFile]="activeFile">
+
+</file-viewer>
+
+<hr />
+<div class="readmore-wrapper">
+
+  <flat-tree-component
+    #flatTreeNode
+    *ngIf="previewFiles | copyProperty : 'filename' : 'path' | pathToNestedChildren | aggregateArrayIntoRootPipe : 'Files'; let rootFile"
+    [useDefaultList]="true"
+    [renderNode]="renderNode"
+    [inputItem]="rootFile"
+    (treeNodeClick)="previewFileClick($event, flatTreeNode)">
+  
+  </flat-tree-component>
+</div>
\ No newline at end of file
diff --git a/src/ui/databrowserModule/util/aggregateArrayIntoRoot.pipe.ts b/src/ui/databrowserModule/util/aggregateArrayIntoRoot.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..513da12531b9531fd5618f23ba5fe1efffdd56aa
--- /dev/null
+++ b/src/ui/databrowserModule/util/aggregateArrayIntoRoot.pipe.ts
@@ -0,0 +1,16 @@
+import { Pipe, PipeTransform } from "@angular/core";
+
+
+@Pipe({
+  name: 'aggregateArrayIntoRootPipe'
+})
+
+export class AggregateArrayIntoRootPipe implements PipeTransform{
+  public transform(array: any[], rootName: string = 'Root Element', childrenPropertyName: string = 'children'){
+    const returnObj = {
+      name: rootName
+    }
+    returnObj[childrenPropertyName] = array
+    return returnObj
+  }
+}
\ No newline at end of file
diff --git a/src/util/pipes/copyProperty.pipe.spec.ts b/src/ui/databrowserModule/util/copyProperty.pipe.spec.ts
similarity index 100%
rename from src/util/pipes/copyProperty.pipe.spec.ts
rename to src/ui/databrowserModule/util/copyProperty.pipe.spec.ts
diff --git a/src/util/pipes/copyProperty.pipe.ts b/src/ui/databrowserModule/util/copyProperty.pipe.ts
similarity index 100%
rename from src/util/pipes/copyProperty.pipe.ts
rename to src/ui/databrowserModule/util/copyProperty.pipe.ts
diff --git a/src/ui/databrowserModule/util/filterDataEntriesByMethods.pipe.ts b/src/ui/databrowserModule/util/filterDataEntriesByMethods.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3c3db25513fe1663c8c9e8bc7308d85a9a290583
--- /dev/null
+++ b/src/ui/databrowserModule/util/filterDataEntriesByMethods.pipe.ts
@@ -0,0 +1,23 @@
+import { PipeTransform, Pipe } from "@angular/core";
+import { DataEntry } from "src/services/stateStore.service";
+import { temporaryFilterDataentryName, CountedDataModality } from '../databrowser.service'
+
+export const NO_METHODS = `NO_METHODS`
+
+@Pipe({
+  name : 'filterDataEntriesByMethods'
+})
+
+export class FilterDataEntriesbyMethods implements PipeTransform{
+  public transform(dataEntries:DataEntry[],dataModalities:CountedDataModality[]):DataEntry[]{
+    const noMethodDisplayName = temporaryFilterDataentryName(NO_METHODS)
+    const includeEmpty = dataModalities.some(d => d.name === noMethodDisplayName)
+    return dataEntries && dataModalities && dataModalities.length > 0
+      ? dataEntries.filter(dataEntry => {
+          return includeEmpty && dataEntry.methods.length === 0
+            || dataEntry.methods.some(m =>
+                dataModalities.findIndex(dm => dm.name === temporaryFilterDataentryName(m)) >= 0)
+        })
+      : dataEntries
+  }
+}
diff --git a/src/ui/databrowserModule/util/filterDataEntriesByRegion.pipe.ts b/src/ui/databrowserModule/util/filterDataEntriesByRegion.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3f45fa9d105f4a34e47dd2d1e1f428670467aa13
--- /dev/null
+++ b/src/ui/databrowserModule/util/filterDataEntriesByRegion.pipe.ts
@@ -0,0 +1,55 @@
+import { Pipe, PipeTransform } from "@angular/core";
+import { DataEntry } from "src/services/stateStore.service";
+
+const isSubRegion = (high, low) => (high.id && low.id && high.id === low.id) || high.name === low.name
+  ? true
+  : high.children && high.children.some
+    ? high.children.some(r => isSubRegion(r, low))
+    : false
+
+const filterSubSelect = (dataEntry, selectedRegions) => 
+  dataEntry.parcellationRegion.some(pr => selectedRegions.some(sr => isSubRegion(pr,sr)))
+
+@Pipe({
+  name: 'filterDataEntriesByRegion'
+})
+
+export class FilterDataEntriesByRegion implements PipeTransform{
+  public transform(dataentries: DataEntry[], selectedRegions: any[], flattenedAllRegions: any[]) {
+    return dataentries && selectedRegions && selectedRegions.length > 0
+      ? dataentries
+          .map(de => {
+            /**
+             * translate parcellationRegion to region representation
+             */
+            const newParcellationRegion = de.parcellationRegion.map(({name, id, ...rest}) => {
+
+              /**
+               * TODO: temporary hack, some dataset region name is not exactly the same as region
+               */
+              /* https://stackoverflow.com/a/9310752/6059235 */
+              const regex = new RegExp(name.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&'), 'i')
+
+              const found = flattenedAllRegions.find(r => {
+                /**
+                 * TODO replace pseudo id with real uuid
+                 */
+                return (r.id && id && r.id === id) || regex.test(r.name)
+                /**
+                 * more correct, but probably should use UUID in the future
+                 */
+                return r.name === name
+              })
+              return found
+                ? { name, id, ...rest, ...found }
+                : { name, id, ...rest }
+            })
+            return {
+              ...de,
+              parcellationRegion: newParcellationRegion
+            }
+          })
+          .filter(de => filterSubSelect(de, selectedRegions))
+      : dataentries
+  }
+}
\ No newline at end of file
diff --git a/src/util/pipes/pathToNestedChildren.pipe.spec.ts b/src/ui/databrowserModule/util/pathToNestedChildren.pipe.spec.ts
similarity index 100%
rename from src/util/pipes/pathToNestedChildren.pipe.spec.ts
rename to src/ui/databrowserModule/util/pathToNestedChildren.pipe.spec.ts
diff --git a/src/util/pipes/pathToNestedChildren.pipe.ts b/src/ui/databrowserModule/util/pathToNestedChildren.pipe.ts
similarity index 100%
rename from src/util/pipes/pathToNestedChildren.pipe.ts
rename to src/ui/databrowserModule/util/pathToNestedChildren.pipe.ts
diff --git a/src/ui/datasetViewer/datasetViewer.component.ts b/src/ui/datasetViewer/datasetViewer.component.ts
deleted file mode 100644
index cbd5b087aa54d053da76d65fe553a711d92f80d5..0000000000000000000000000000000000000000
--- a/src/ui/datasetViewer/datasetViewer.component.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-import { Component, Input, Output, EventEmitter } from "@angular/core";
-import { DataEntry } from "../../services/stateStore.service";
-
-@Component({
-  selector : 'dataset-viewer',
-  templateUrl : './datasetViewer.template.html',
-  styleUrls : ['./datasetViewer.style.css']
-})
-
-export class DatasetViewerComponent{
-  @Input() dataset : DataEntry
-  @Output() launchFileViewer : EventEmitter<{dataset:DataEntry, file:any}> = new EventEmitter()
-
-  previewFileClick(ev, el){
-    
-    ev.event.preventDefault()
-    ev.event.stopPropagation()
-
-    if(ev.inputItem.children.length > 0){
-      el.toggleCollapse(ev.inputItem)
-    }else{
-      this.launchFileViewer.emit({
-        dataset : this.dataset,
-        file : ev.inputItem
-      })
-    }
-  }
-
-  renderNode(obj){
-    return obj.name
-      ? obj.name
-      : obj.path
-  }
-}
\ No newline at end of file
diff --git a/src/ui/datasetViewer/datasetViewer.template.html b/src/ui/datasetViewer/datasetViewer.template.html
deleted file mode 100644
index 9c31a987c59b4a5493960016d5556870fc238531..0000000000000000000000000000000000000000
--- a/src/ui/datasetViewer/datasetViewer.template.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<div *ngIf = "dataset; else defaultDisplay">
-  <h4 title>
-    {{ dataset.name }}
-  </h4>
-  <kg-entry-viewer *ngIf = "dataset.kgID; else noKgID" [kgQueryString] = "dataset.kgID">
-
-  </kg-entry-viewer>
-
-  <hr />
-  <h5>
-    Preview Dataset
-  </h5>
-  <flat-tree-component
-    #flatTreeNode
-    *ngFor = "let item of dataset.files | copyProperty : 'filename' : 'path' | pathToNestedChildren"
-    [childrenExpanded] = "false"
-    [renderNode] = "renderNode"
-    [inputItem] = " item "
-    (treeNodeClick)= "previewFileClick($event, flatTreeNode)">
-
-  </flat-tree-component>
-  <hr />
-</div>
-
-<ng-template #defaultDisplay>
-  Nothing to display ...
-</ng-template>
-
-<ng-template #noKgID>
-  kgID not specified.
-</ng-template>
diff --git a/src/ui/fileviewer/dedicated/dedicated.component.ts b/src/ui/fileviewer/dedicated/dedicated.component.ts
deleted file mode 100644
index 201f6d3914a9c698c15246fedf91fb54183c0743..0000000000000000000000000000000000000000
--- a/src/ui/fileviewer/dedicated/dedicated.component.ts
+++ /dev/null
@@ -1,69 +0,0 @@
-import { Component, OnDestroy, Input } from "@angular/core";
-import { Store, select } from "@ngrx/store";
-import { DedicatedViewState, File, ADD_NG_LAYER, REMOVE_NG_LAYER, NgViewerStateInterface } from "../../../services/stateStore.service";
-import { Observable, Subscription } from "rxjs";
-import { getActiveColorMapFragmentMain } from "../../nehubaContainer/nehubaContainer.component";
-import { ToastService } from "../../../services/toastService.service";
-
-
-@Component({
-  selector : 'dedicated-viewer',
-  templateUrl : './dedicated.template.html',
-  styleUrls : [
-    `./dedicated.style.css`
-  ]
-})
-
-export class DedicatedViewer implements OnDestroy{
-  @Input() searchResultFile : File
-
-  private ngLayers$ : Observable<NgViewerStateInterface>
-  private ngLayersSubscription : Subscription
-  private ngLayers : Set<string> = new Set()
-
-  constructor(
-    private toastService:ToastService,
-    private store:Store<DedicatedViewState>
-  ){
-    this.ngLayers$ = this.store.pipe(
-      select('ngViewerState')
-    )
-
-    this.ngLayersSubscription = this.ngLayers$.subscribe(layersInterface => this.ngLayers = new Set(layersInterface.layers.map(l => l.source)))
-  }
-
-  get isShowing(){
-    return this.ngLayers.has(`nifti://${this.searchResultFile.url}`)
-  }
-
-  ngOnDestroy(){
-    this.ngLayersSubscription.unsubscribe()
-  }
-
-  showDedicatedView(){
-    this.store.dispatch({
-      type : ADD_NG_LAYER,
-      layer : {
-        name : this.searchResultFile.url,
-        source : `nifti://${this.searchResultFile.url}`,
-        mixability : 'nonmixable',
-        shader : getActiveColorMapFragmentMain()
-      }
-    })
-    setTimeout(() => {
-      if(this.isShowing)
-        this.toastService.showToast('nifti showing')
-      else
-        this.toastService.showToast('nifti cannot be shown until current nifti layer is cleared')
-    }, 128)
-  }
-
-  removeDedicatedView(){
-    this.store.dispatch({
-      type : REMOVE_NG_LAYER,
-      layer : {
-        name : this.searchResultFile.url
-      }
-    })
-  }
-}
\ No newline at end of file
diff --git a/src/ui/fileviewer/dedicated/dedicated.style.css b/src/ui/fileviewer/dedicated/dedicated.style.css
deleted file mode 100644
index 8f54fa6f02a82636b5735c6a65a1a35b80cdfb1e..0000000000000000000000000000000000000000
--- a/src/ui/fileviewer/dedicated/dedicated.style.css
+++ /dev/null
@@ -1,4 +0,0 @@
-a
-{
-  margin: 0 1em;
-}
\ No newline at end of file
diff --git a/src/ui/fileviewer/dedicated/dedicated.template.html b/src/ui/fileviewer/dedicated/dedicated.template.html
deleted file mode 100644
index f1481940c1fd4127bd67908d9b89bc9d3e5d16a4..0000000000000000000000000000000000000000
--- a/src/ui/fileviewer/dedicated/dedicated.template.html
+++ /dev/null
@@ -1,14 +0,0 @@
-<a 
-  href = "#"
-  (click) = "$event.preventDefault();showDedicatedView()"
-  *ngIf = "!isShowing">
-
-  show this dataset in the viewer 
-</a>
-<a 
-  href = "#"
-  (click) = "$event.preventDefault();removeDedicatedView()"
-  *ngIf = "isShowing">
-
-  clear this dataset from the viewer
-</a>
\ No newline at end of file
diff --git a/src/ui/fileviewer/fileviewer.template.html b/src/ui/fileviewer/fileviewer.template.html
deleted file mode 100644
index 1f2b7aa4c8096c370b59b20c9b2bebc1bb24d69e..0000000000000000000000000000000000000000
--- a/src/ui/fileviewer/fileviewer.template.html
+++ /dev/null
@@ -1,93 +0,0 @@
-<div *ngIf = "!searchResultFile">
-  searchResultFile as an input is required for this component to work...
-</div>
-
-<!-- KG Entry Viewer -->
-<kg-entry-viewer *ngIf = "searchResultFile.kgID && false" [kgQueryString] = "searchResultFile.kgID">
-
-</kg-entry-viewer>
-
-<!-- citation -->
-<citations-component 
-  citationContainer
-  *ngIf = "searchResultFile && !searchResultFile.kgID"
-  [properties] = "searchResultFile.datasetProperties">
-</citations-component>
-
-<!-- file viewer -->
-<div 
-  *ngIf = "searchResultFile"
-  [ngSwitch]="searchResultFile.mimetype">
-
-  <div emptyRow *ngSwitchCase = "'application/octet-stream'">
-    <!-- downloadable data -->
-  </div>
-
-  <!-- image  -->
-  <div *ngSwitchCase="'image/jpeg'" >
-    <!-- TODO figure out a more elegant way of dealing with draggable img -->
-    <img draggable = "false" [src] = "searchResultFile.url" />
-  </div>
-
-  <!-- data container -->
-  <div [ngSwitch]="searchResultFile.data.chartType" *ngSwitchCase="'application/json'">
-    <radar-chart
-      #childChart
-      [colors] = "searchResultFile.data.colors"
-      [options] = "searchResultFile.data.chartOptions"
-      [labels]="searchResultFile.data.labels"
-      [radarDatasets]="searchResultFile.data.datasets"
-      *ngSwitchCase="'radar'">
-
-    </radar-chart>
-    <line-chart
-      #childChart
-      [colors] = "searchResultFile.data.colors"
-      [options] = "searchResultFile.data.chartOptions"
-      [lineDatasets] = "searchResultFile.data.datasets"
-      *ngSwitchCase = "'line'">
-
-    </line-chart>
-    <div *ngSwitchDefault>
-      The json file is not a chart. I mean, we could dump the json, but would it really help?
-    </div>
-  </div>
-  <div *ngSwitchCase = "'application/hibop'">
-    <div mimetypeTextContainer>
-        You will need to install the HiBoP software on your computer, click the 'Download File' button and open the .hibop file.
-    </div>
-  </div>
-  <div *ngSwitchCase = "'application/nifti'">
-    <dedicated-viewer
-      [searchResultFile] = "searchResultFile">
-    </dedicated-viewer>
-  </div>
-  <div *ngSwitchCase = "'application/nehuba-layer'">
-    APPLICATION NEHUBA LAYER - NOT YET IMPLEMENTED
-    <!-- <dedicated-view-controller
-      [dedicatedViewString]="'nehuba-layer://'+searchResultFile.url"
-      [dedicatedViewNehubaLayerObject]="searchResultFile.data">
-
-    </dedicated-view-controller> -->
-  </div>
-  <div *ngSwitchDefault>
-    <div mimetypeTextContainer>
-      The selected file with the mimetype {{ searchResultFile.mimetype }} could not be displayed.
-    </div>
-  </div>
-</div>
-
-<!-- caption -->
-<readmore-component *ngIf = "searchResultFile.properties && searchResultFile.properties.description">
-  <small id = "captions">
-    {{ searchResultFile.properties.description }}
-  </small>
-</readmore-component>
-
-<div anchorContainer *ngIf = "downloadUrl">
-  <a [href]="downloadUrl" target = "_blank" download>Download File</a>
-</div>
-
-<div anchorContainer *ngIf = "downloadPng">
-  <a [href] = "downloadPng" target = "_blank" download>Download Chart as Image</a>
-</div>
diff --git a/src/ui/fileviewer/line/line.chart.template.html b/src/ui/fileviewer/line/line.chart.template.html
deleted file mode 100644
index a4026c30ce351a3d0d2bd08de257bc7deecc62f8..0000000000000000000000000000000000000000
--- a/src/ui/fileviewer/line/line.chart.template.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<canvas 
-  *ngIf = "shapedLineChartDatasets"
-  (mousewheel)="mousescroll($event)"
-  width = '100%'
-  height = '100%'
-  baseChart 
-  chartType="line"
-  [options]="chartOption"
-  [colors]="colors"
-  [datasets]="shapedLineChartDatasets"
-  [labels]="chartDataset.labels"
-  #canvas>
-</canvas>
-<a [download] = "csvTitle" [href] = "csvDataUrl" *ngIf = "shapedLineChartDatasets">
-  download line graph as csv
-</a>
-<span
-  *ngIf = "!shapedLineChartDatasets">
-  datasets are required to display linear graph
-</span>
\ No newline at end of file
diff --git a/src/ui/help/help.component.ts b/src/ui/help/help.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..831ff89d96a8bdc4261fe63cab3d85fdc32d3ffa
--- /dev/null
+++ b/src/ui/help/help.component.ts
@@ -0,0 +1,29 @@
+import { Component } from '@angular/core'
+import { AtlasViewerConstantsServices } from 'src/atlasViewer/atlasViewer.constantService.service';
+import { DomSanitizer } from '@angular/platform-browser';
+
+@Component({
+  selector: 'help-component',
+  templateUrl: './help.template.html',
+  styleUrls: [
+    './help.style.css'
+  ]
+})
+
+export class HelpComponent{
+
+  public generalHelp
+  public sliceviewHelp
+  public perspectiveviewHelp
+  public supportText
+
+  constructor(
+    private constantService:AtlasViewerConstantsServices,
+    private sanitizer:DomSanitizer
+  ){
+    this.generalHelp = this.constantService.showHelpGeneralMap
+    this.sliceviewHelp = this.constantService.showHelpSliceViewMap
+    this.perspectiveviewHelp = this.constantService.showHelpPerspectiveViewMap
+    this.supportText = this.sanitizer.bypassSecurityTrustHtml(this.constantService.showHelpSupportText)
+  }
+}
\ No newline at end of file
diff --git a/src/ui/help/help.style.css b/src/ui/help/help.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..709f13dbde0795d58259cf5b74cc764a00610b41
--- /dev/null
+++ b/src/ui/help/help.style.css
@@ -0,0 +1,5 @@
+:host
+{
+  margin: 0.5em 1em;
+  display:block;
+}
\ No newline at end of file
diff --git a/src/ui/help/help.template.html b/src/ui/help/help.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..bf9920671165459859dac9ceb983d75dd1ea7e11
--- /dev/null
+++ b/src/ui/help/help.template.html
@@ -0,0 +1,17 @@
+<div>
+  <h2>General</h2>
+  <div *ngFor = "let entry of generalHelp">
+    <b>{{ entry[0] }}</b>: {{ entry[1] }}
+  </div>
+  <h2>Slice View</h2>
+  <div *ngFor = "let entry of sliceviewHelp">
+    <b>{{ entry[0] }}</b>: {{ entry[1] }}
+  </div>
+  <h2>Perspective View</h2>
+  <div *ngFor = "let entry of perspectiveviewHelp">
+    <b>{{ entry[0] }}</b>: {{ entry[1] }}
+  </div>
+  <hr />
+  <div [innerHTML] = "supportText">
+  </div>
+</div>
\ No newline at end of file
diff --git a/src/ui/kgEntryViewer/kgentry.component.ts b/src/ui/kgEntryViewer/kgentry.component.ts
index bb01d8c7d4a71b1e52e40a5c011226dc5e0d58aa..df948f272a2e3b5f75fd51059ae31b785d032c53 100644
--- a/src/ui/kgEntryViewer/kgentry.component.ts
+++ b/src/ui/kgEntryViewer/kgentry.component.ts
@@ -1,4 +1,5 @@
-import { Component, Input, OnInit } from "@angular/core";
+import { Component, Input } from "@angular/core";
+import { DataEntry } from "src/services/stateStore.service";
 
 @Component({
   selector : 'kg-entry-viewer',
@@ -8,34 +9,12 @@ import { Component, Input, OnInit } from "@angular/core";
   ]
 })
 
-export class KgEntryViewer implements OnInit{
-  @Input() kgQueryString : string = null
+export class KgEntryViewer {
+  @Input() dataset: DataEntry
 
   public kgData : any = null
   public kgError : any = null
 
-  ngOnInit(){
-    if(this.kgQueryString){
-      fetch(`${KGROOT}${this.kgQueryString}`)
-        .then(res => res.json())
-        .then(json => {
-          if(json.found)
-            return json._source
-          else
-            throw new Error('No documents were found.')
-        })
-        .then(json => this.kgData = json)
-        .catch(e => {
-          console.error('fetching KG data error', e)
-          this.kgData = null
-          this.kgError = JSON.stringify(e)
-        })
-    }else{
-      console.error('kgQueryString empty!')
-      this.kgError = 'Knowledge Graph ID empty'
-    }
-  }
-
   get tableColClass1(){
     return `col-xs-4 col-lg-4 tableEntry`
   }
@@ -44,13 +23,7 @@ export class KgEntryViewer implements OnInit{
     return `col-xs-8 col-lg-8 tableEntry`
   }
 
-  get kgHref(){
-    return `https://kg.humanbrainproject.org/webapp/#${this.kgQueryString}`
-  }
-
   public isArray(v){
     return v.constructor === Array
   }
 }
-
-const KGROOT = `https://kg.humanbrainproject.org/proxy/default/kg/`
\ No newline at end of file
diff --git a/src/ui/kgEntryViewer/kgentry.style.css b/src/ui/kgEntryViewer/kgentry.style.css
index 08aa2ff604ef4cb81be1dbfaf936c5df17aff5c5..4ac3fc6f4bd8f118c0339fff562389ee4d18239c 100644
--- a/src/ui/kgEntryViewer/kgentry.style.css
+++ b/src/ui/kgEntryViewer/kgentry.style.css
@@ -22,9 +22,13 @@ div[container]
   overflow:hidden;
 }
 
-a[linkToKG]
+a[link]
 {
   display: inline-block;
   margin-top: 1rem;
-  white-space: nowrap;
+}
+
+a[link]:hover
+{
+  text-decoration: none;
 }
\ No newline at end of file
diff --git a/src/ui/kgEntryViewer/kgentry.template.html b/src/ui/kgEntryViewer/kgentry.template.html
index 8f7e85d366d02bd8addaa6f050a281a9b599b9bd..42e95d0156320d1e8e703bcd09364ea95121bc72 100644
--- a/src/ui/kgEntryViewer/kgentry.template.html
+++ b/src/ui/kgEntryViewer/kgentry.template.html
@@ -1,151 +1,84 @@
-<div *ngIf = "kgData; else showDefault" container>
+<div *ngIf="dataset" container>
 
+  <!-- description -->
   <readmore-component>
-
-    <panel-component *ngIf = "false" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Description
-      </div>
-      <div body>
-        {{ kgData.description.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "false"  [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Project
-      </div>
-      <div body>
-        {{ kgData.component.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "false"  [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Contributor(s)
-      </div>
-      <div body>
-        <!-- TODO maybe object instead of array -->
-        <div *ngFor = "let contributor of kgData.contributors">
-          {{ contributor.value }}
-        </div>
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "false"  [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Custodian(s)
-      </div>
-      <div body>
-        <!-- TODO Maybe array -->
-        {{ kgData.owners.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.publications" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Related Publication(s)
-      </div>
-      <div body>
-        <div *ngIf = "isArray(kgData.publications); else singlePublicationTemplate">
-          <div publicationContainer *ngFor = "let publication of kgData.publications">
-            {{ publication.value.split('\n')[0] }}
-          </div>
-        </div>
-        <ng-template #singlePublicationTemplate>
-          <div publicationContainer>
-            {{ kgData.publications.value.split('\n')[0] }}
-          </div>
-        </ng-template>
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "false"  [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Brain atlas
-      </div>
-      <div body>
-        {{ kgData.atlas.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.preparation" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Preparation
-      </div>
-      <div body>
-        {{ kgData.preparation.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.methods" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Methods
-      </div>
-      <div body>
-        <div *ngIf = "isArray(kgData.methods); else singleMethodTemplate">
-          <div *ngFor = "let method of kgData.methods">
-            {{ method.value }}
-          </div>
-        </div>
-        <ng-template #singleMethodTemplate>
-          {{ kgData.methods.value }}
-        </ng-template>
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.license_info" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        License
-      </div>
-      <div body>
-        {{ kgData.license_info.value }}
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.files" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Files
-      </div>
-      <div body>
-        <div *ngIf = "isArray(kgData.files);else singleFileTemplate">
-          <div *ngFor = "let file of kgData.files">
-            {{ file.value }}
-          </div>
-        </div>
-        <ng-template #singleFileTemplate>
-          {{ kgData.files.value }}
-        </ng-template>
-      </div>
-    </panel-component>
-
-    <panel-component *ngIf = "kgData.subjects" [collapseBody] = "true" [bodyCollapsable] = "true">
-      <div heading>
-        Subjects
-      </div>
-      <div body>
-        <kg-entry-viewer-subject-viewer
-          [subjects] = "kgData.subjects">
-
-        </kg-entry-viewer-subject-viewer>
-      </div>
-    </panel-component>
-
+    {{ dataset.description }}
   </readmore-component>
-  <a target = "_blank" [href] = "kgHref" linkToKG>
-    Open in Knowledge Graph <i class = "glyphicon glyphicon-new-window"></i>
-  </a>
 
-</div>
-<ng-template #showDefault>
-  <div reportError *ngIf = "kgError">
-    Fetching metadata from the Knowledge Graph failed - {{ kgError }}
-  </div>
-  <div *ngIf = "!kgError">
-    Fetching KG Data 
+  <!-- other data -->
+
+  <panel-component *ngIf = "false"  [collapseBody] = "true" [bodyCollapsable] = "true">
+    <div heading>
+      Contributor(s)
+    </div>
+    <div body>
+      <!-- TODO maybe object instead of array -->
+      <div *ngFor = "let contributor of dataset.contributors">
+        {{ contributor.value }}
+      </div>
+    </div>
+  </panel-component>
+
+  <panel-component [collapseBody] = "true" [bodyCollapsable] = "true">
+    <div heading>
+      Custodian(s)
+    </div>
+    <div body>
+      <!-- TODO Maybe array -->
+      <div *ngFor="let custodian of dataset.custodians">
+        {{ custodian }}
+      </div>
+    </div>
+  </panel-component>
+
+  <panel-component *ngIf = "dataset.publications" [collapseBody] = "true" [bodyCollapsable] = "true">
+    <div heading>
+      Related Publication(s)
+    </div>
+    <div body>
+      <div *ngFor="let publication of dataset.publications">
+        <a target="_blank" link [href]="'https://doi.org/' + publication.doi">
+          {{ publication.cite }}
+          <i class = "fas fa-new-window"></i>
+        </a>
+      </div>
+    </div>
+  </panel-component>
+
+  <panel-component *ngIf = "dataset.licenseInfo || dataset.license" [collapseBody] = "true" [bodyCollapsable] = "true">
+    <div heading>
+      License
+    </div>
+    <div body>
+      <div *ngFor="let l of dataset.licence">
+        {{ l }}
+      </div>
+      <div *ngFor="let l of dataset.licenseInfo">
+        {{ l }}
+      </div>
+    </div>
+  </panel-component>
+
+  <panel-component *ngIf = "dataset.files" [collapseBody] = "true" [bodyCollapsable] = "true">
+    <div heading>
+      Files
+    </div>
+    <div body>
+      <div *ngFor="let file of dataset.files">
+        <a link target="_blank" [href]="file.absolutePath">
+          {{ file.name }}
+          <i class="fas fa-download-alt"></i>
+        </a>
+      </div>
+    </div>
+  </panel-component>
+
+  <a
+    *ngFor="let ref of dataset.kgReference"
+    [href]="'https://doi.org/' + ref"
+    target="_blank">
+    Open in Knowledge Graph
+    <i class = "fas fa-new-window"></i>
+  </a>
 
-    <span class = "homeAnimationDots loadingAnimationDots">&bull;</span>
-    <span class = "homeAnimationDots loadingAnimationDots">&bull;</span>
-    <span class = "homeAnimationDots loadingAnimationDots">&bull;</span>
-  </div>
-</ng-template>
\ No newline at end of file
+</div>
\ No newline at end of file
diff --git a/src/ui/kgtos/kgtos.component.ts b/src/ui/kgtos/kgtos.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d191860a02a4e316abc662944cc02be068c948ff
--- /dev/null
+++ b/src/ui/kgtos/kgtos.component.ts
@@ -0,0 +1,13 @@
+import { Component } from "@angular/core";
+
+@Component({
+  selector: 'kgtos-component',
+  templateUrl: './kgtos.template.html',
+  styleUrls: [
+    './kgtos.style.css'
+  ]
+})
+
+export class KGToS{
+
+}
\ No newline at end of file
diff --git a/src/ui/kgtos/kgtos.style.css b/src/ui/kgtos/kgtos.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/ui/kgtos/kgtos.template.html b/src/ui/kgtos/kgtos.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..5f8fe4eb6240d7227cec618be5efa43e478cceaf
--- /dev/null
+++ b/src/ui/kgtos/kgtos.template.html
@@ -0,0 +1,20 @@
+<div>
+  <p>
+    The interactive viewer queries HBP Knowledge Graph Data Platform ("KG") for published datasets.
+  </p>
+  <p>
+    Access to the data and metadata provided through KG requires that you cite and acknowledge said data and metadata according to the Terms and Conditions of the Platform.
+  </p>
+  <p>
+    Citation requirements are outlined <a target="_blank" href="https://www.humanbrainproject.eu/en/explore-the-brain/search-terms-of-use#citations">here</a>.
+  </p>
+  <p>
+    Acknowledgement requirements are outlined <a target="_blank" href="https://www.humanbrainproject.eu/en/explore-the-brain/search-terms-of-use#acknowledgements">here</a>.
+  </p>
+  <p>
+    These outlines are based on the authoritative Terms and Conditions are found <a target="_blank" href="https://www.humanbrainproject.eu/en/explore-the-brain/search-terms-of-use">here</a>.
+  </p>
+  <p>
+    If you do not accept the Terms & Conditions you are not permitted to access or use the KG to search for, to submit, to post, or to download any materials found there-in.
+  </p>
+</div>
\ No newline at end of file
diff --git a/src/ui/layerbrowser/layerbrowser.component.ts b/src/ui/layerbrowser/layerbrowser.component.ts
index 5de054dc184ff7b15058974344c54513e4105183..2dd2a8aa0bcca320f72a2eeff7c1ac7b2c3ed72f 100644
--- a/src/ui/layerbrowser/layerbrowser.component.ts
+++ b/src/ui/layerbrowser/layerbrowser.component.ts
@@ -3,12 +3,16 @@ import { NgLayerInterface } from "../../atlasViewer/atlasViewer.component";
 import { Store, select } from "@ngrx/store";
 import { ViewerStateInterface, isDefined, REMOVE_NG_LAYER, FORCE_SHOW_SEGMENT, safeFilter } from "../../services/stateStore.service";
 import { Subscription, Observable } from "rxjs";
-import { filter, distinctUntilChanged, map, delay } from "rxjs/operators";
+import { filter, distinctUntilChanged, map, delay, buffer } from "rxjs/operators";
+import { AtlasViewerConstantsServices } from "src/atlasViewer/atlasViewer.constantService.service";
 
 @Component({
   selector : 'layer-browser',
   templateUrl : './layerbrowser.template.html',
-  styleUrls : [ './layerbrowser.style.css' ]
+  styleUrls : [ 
+    './layerbrowser.style.css',
+    '../btnShadow.style.css'
+  ]
 })
 
 export class LayerBrowser implements OnDestroy{
@@ -21,14 +25,33 @@ export class LayerBrowser implements OnDestroy{
 
   public forceShowSegmentCurrentState : boolean | null = null
   public forceShowSegment$ : Observable<boolean|null>
+  
+  public ngLayers$: Observable<any>
+  public advancedMode: boolean = false
+
   private subscriptions : Subscription[] = []
   private disposeHandler : any
   
   /* TODO temporary measure. when datasetID can be used, will use  */
   public fetchedDataEntries$ : Observable<any>
 
-  constructor(private store : Store<ViewerStateInterface>){
+  constructor(
+    private store : Store<ViewerStateInterface>,
+    private constantsService: AtlasViewerConstantsServices){
+
+    this.ngLayers$ = store.pipe(
+      select('viewerState'),
+      select('templateSelected'),
+      map(templateSelected => (templateSelected && !this.advancedMode && [
+        templateSelected.ngId,
+        ...templateSelected.parcellations.map(p => p.ngId)
+      ]) || [])
+    )
 
+    /**
+     * TODO
+     * this is no longer populated
+     */
     this.fetchedDataEntries$ = this.store.pipe(
       select('dataStore'),
       safeFilter('fetchedDataEntries'),
@@ -41,20 +64,37 @@ export class LayerBrowser implements OnDestroy{
       map(state => state.forceShowSegment)
     )
 
-    this.subscriptions.push(this.store.pipe(
-      select('viewerState'),
-      filter(state => isDefined(state) && isDefined(state.templateSelected)),
-      distinctUntilChanged((o,n) => o.templateSelected.name === n.templateSelected.name),
-      map(state => Object.keys(state.templateSelected.nehubaConfig.dataset.initialNgState.layers)),
-      delay(0)
-    ).subscribe((lockedLayerNames:string[]) => {
-
-      this.lockedLayers = lockedLayerNames
 
-      this.ngLayersChangeHandler()
-      this.disposeHandler = window['viewer'].layerManager.layersChanged.add(() => this.ngLayersChangeHandler())
-      window['viewer'].registerDisposer(this.disposeHandler)
-    }))
+    /**
+     * TODO leakage? after change of template still hanging the reference?
+     */
+    this.subscriptions.push(
+      this.store.pipe(
+        select('viewerState'),
+        select('templateSelected'),
+        distinctUntilChanged((o,n) => o.templateSelected.name === n.templateSelected.name),
+        filter(templateSelected => !!templateSelected),
+        map(templateSelected => Object.keys(templateSelected.nehubaConfig.dataset.initialNgState.layers)),
+        buffer(this.store.pipe(
+          select('ngViewerState'),
+          select('nehubaReady'),
+          filter(flag => flag)
+        )),
+        delay(0),
+        map(arr => arr[arr.length - 1])
+      ).subscribe((lockedLayerNames:string[]) => {
+        /**
+         * TODO
+         * if layerbrowser is init before nehuba
+         * window['viewer'] will return undefined
+         */
+        this.lockedLayers = lockedLayerNames
+
+        this.ngLayersChangeHandler()
+        this.disposeHandler = window['viewer'].layerManager.layersChanged.add(() => this.ngLayersChangeHandler())
+        window['viewer'].registerDisposer(this.disposeHandler)
+      })
+    )
 
     this.subscriptions.push(
       this.forceShowSegment$.subscribe(state => this.forceShowSegmentCurrentState = state)
@@ -66,7 +106,6 @@ export class LayerBrowser implements OnDestroy{
   }
 
   ngLayersChangeHandler(){
-
     this.ngLayers = (window['viewer'].layerManager.managedLayers as any[]).map(obj => ({
       name : obj.name,
       type : obj.initialSpecification.type,
@@ -83,8 +122,8 @@ export class LayerBrowser implements OnDestroy{
 
   checkLocked(ngLayer:NgLayerInterface):boolean{
     if(!this.lockedLayers){
-      /* locked layer undefined. always return true for locked layer check */
-      return true
+      /* locked layer undefined. always return false */
+      return false
     }else
       return this.lockedLayers.findIndex(l => l === ngLayer.name) >= 0
   }
@@ -133,7 +172,7 @@ export class LayerBrowser implements OnDestroy{
 
   segmentationTooltip(){
     return `toggle segments visibility: 
-${this.forceShowSegmentCurrentState === true ? 'always show' : this.forceShowSegmentCurrentState === false ? 'always hide' : 'auto'}`
+    ${this.forceShowSegmentCurrentState === true ? 'always show' : this.forceShowSegmentCurrentState === false ? 'always hide' : 'auto'}`
   }
 
   get segmentationAdditionalClass(){
@@ -145,4 +184,8 @@ ${this.forceShowSegmentCurrentState === true ? 'always show' : this.forceShowSeg
           ? 'muted'
           : 'red' 
   }
+
+  get isMobile(){
+    return this.constantsService.mobile
+  }
 }
diff --git a/src/ui/layerbrowser/layerbrowser.style.css b/src/ui/layerbrowser/layerbrowser.style.css
index 2babcd853d582a36bed00a1710af4bf7529bb445..83bf14bb66993088b1d86f607e24784f07926bd4 100644
--- a/src/ui/layerbrowser/layerbrowser.style.css
+++ b/src/ui/layerbrowser/layerbrowser.style.css
@@ -16,11 +16,6 @@ div[body]
   background-color:rgba(0, 0, 0, 0.1);
 }
 
-.muted
-{
-  opacity : 0.5;
-}
-
 .muted-text
 {
   text-decoration: line-through;
@@ -33,13 +28,17 @@ div[body]
   align-items: center;
 }
 
-.glyphicon.blue
+.fas.blue
 {
   color: #337ab7;
 }
 
-.glyphicon.red
+.fas.red
 {
   color: red;
 }
 
+.noLayerPlaceHolder
+{
+  padding: 0.5em 1em;
+}
\ No newline at end of file
diff --git a/src/ui/layerbrowser/layerbrowser.template.html b/src/ui/layerbrowser/layerbrowser.template.html
index b88d2e18b1a748c40821d5b89ee58ec590704a00..e307ecb73020de8802c64c4ad33ca591d33501e6 100644
--- a/src/ui/layerbrowser/layerbrowser.template.html
+++ b/src/ui/layerbrowser/layerbrowser.template.html
@@ -1,51 +1,71 @@
-<div 
-  class="layerContainer"
-  *ngFor = "let ngLayer of ngLayers">
+<ng-container *ngIf="ngLayers$ | async | filterNgLayer : ngLayers as filteredNgLayers; else noLayerPlaceHolder">
+  <ng-container *ngIf="filteredNgLayers.length > 0; else noLayerPlaceHolder">
+    <div
+      class="layerContainer overflow-hidden"
+      *ngFor = "let ngLayer of filteredNgLayers">
+    
+      <!-- toggle visibility -->
+      <div class="btnWrapper">
+        <div
+          container = "body"
+          placement = "bottom"
+          [tooltip] = "checkLocked(ngLayer) ? 'base layer cannot be hidden' : 'toggle visibility'"
+          (click) = "checkLocked(ngLayer) ? null : toggleVisibility(ngLayer)"
+          class="btn btn-sm btn-outline-secondary rounded-circle">
+          <i [ngClass] = "checkLocked(ngLayer) ? 'fas fa-lock muted' :ngLayer.visible ? 'far fa-eye' : 'far fa-eye-slash'">
+          </i>
+        </div>
+      </div>
 
-  <div>
-    <i 
-      container = "body"
-      placement = "bottom"
-      [tooltip] = "checkLocked(ngLayer) ? 'base layer cannot be hidden' : 'toggle visibility'"
-      (click) = "checkLocked(ngLayer) ? null : toggleVisibility(ngLayer)"
-      class = "glyphicon" 
-      [ngClass] = "checkLocked(ngLayer) ? 'glyphicon-lock muted' :ngLayer.visible ? 'glyphicon-eye-open' : 'glyphicon-eye-close'">
+      <!-- advanced mode only: toggle force show segmentation -->
+      <div class="btnWrapper">
+        <div
+          *ngIf="advancedMode"
+          container="body"
+          placement="bottom"
+          [tooltip]="ngLayer.type === 'segmentation' ? segmentationTooltip() : 'only segmentation layer can hide/show segments'"
+          #forceSegment="bs-tooltip"
+          (click)="forceSegment.hide();toggleForceShowSegment(ngLayer)"
+          class="btn btn-sm btn-outline-secondary rounded-circle">
+          <i 
+            class="fas" 
+            [ngClass]="ngLayer.type === 'segmentation' ? ('fa-th-large ' + segmentationAdditionalClass) : 'fa-lock muted' ">
+      
+          </i>
+        </div>
+      </div>
 
-    </i>
-  </div>
-  <div>
-    <i 
-      container = "body"
-      placement = "bottom"
-      [tooltip] = "ngLayer.type === 'segmentation' ? segmentationTooltip() : 'only segmentation layer can hide/show segments'"
-      (click) = "forceSegment.hide();toggleForceShowSegment(ngLayer)"
-      class = "glyphicon" 
-      #forceSegment = "bs-tooltip"
-      [ngClass] = "ngLayer.type === 'segmentation' ? ('glyphicon-th-large ' + segmentationAdditionalClass) : 'glyphicon-lock muted' ">
+      <!-- remove layer -->
+      <div class="btnWrapper">
+        <div
+          container="body"
+          placement="bottom"
+          [tooltip]="checkLocked(ngLayer) ? 'base layers cannot be removed' : 'remove layer'"
+          (click)="removeLayer(ngLayer)"
+          class="btn btn-sm btn-outline-secondary rounded-circle">
+          <i [ngClass]="checkLocked(ngLayer) ? 'fas fa-lock muted' : 'far fa-times-circle'">
+          </i>
+        </div>
+      </div>
 
-    </i>
-  </div>
-  <div>
-    <i 
-      (click) = "removeLayer(ngLayer)"
-      container = "body"
-      placement = "bottom"
-      [tooltip] = "checkLocked(ngLayer) ? 'base layers cannot be removed' : 'remove layer'"
-      class = "glyphicon"
-      [ngClass] = "checkLocked(ngLayer) ? 'glyphicon-lock muted' : 'glyphicon-remove-circle'">
-
-    </i>
-  </div>
-
-  <panel-component
-    [ngClass] = "{'muted-text muted' : !classVisible(ngLayer)}">
-
-    <div heading>
-      {{ ngLayer.name | getLayerNameFromDatasets : (fetchedDataEntries$ | async) }} : {{ ngLayer.type }}
+      <!-- layer description -->
+      <panel-component [ngClass]="{'muted-text muted' : !classVisible(ngLayer)}">
+    
+        <div heading>
+          {{ ngLayer.name | getLayerNameFromDatasets : (fetchedDataEntries$ | async) }}
+        </div>
+    
+        <div bodyy>
+          {{ ngLayer.source }}
+        </div>
+      </panel-component>
     </div>
+  </ng-container>
+</ng-container>
 
-    <div bodyy>
-      {{ ngLayer.source }}
-    </div>
-  </panel-component>
-</div>
\ No newline at end of file
+<!-- fall back when no layers are showing -->
+<ng-template #noLayerPlaceHolder>
+  <h5 class="noLayerPlaceHolder text-muted">
+    No additional layers added.
+  </h5>
+</ng-template>
\ No newline at end of file
diff --git a/src/ui/menuicons/menuicons.component.ts b/src/ui/menuicons/menuicons.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..57250e2b4963c7a15b1685e7b1a5dc32a50b7199
--- /dev/null
+++ b/src/ui/menuicons/menuicons.component.ts
@@ -0,0 +1,162 @@
+import { Component, ComponentRef, Injector, ComponentFactory, ComponentFactoryResolver, AfterViewInit } from "@angular/core";
+
+import { WidgetServices } from "src/atlasViewer/widgetUnit/widgetService.service";
+import { WidgetUnit } from "src/atlasViewer/widgetUnit/widgetUnit.component";
+import { LayerBrowser } from "src/ui/layerbrowser/layerbrowser.component";
+import { DataBrowser } from "src/ui/databrowserModule/databrowser/databrowser.component";
+import { PluginBannerUI } from "../pluginBanner/pluginBanner.component";
+import { AtlasViewerConstantsServices } from "src/atlasViewer/atlasViewer.constantService.service";
+import { DatabrowserService } from "../databrowserModule/databrowser.service";
+import { PluginServices } from "src/atlasViewer/atlasViewer.pluginService.service";
+
+@Component({
+  selector: 'menu-icons',
+  templateUrl: './menuicons.template.html',
+  styleUrls: [
+    './menuicons.style.css',
+    '../btnShadow.style.css'
+  ]
+})
+
+export class MenuIconsBar{
+
+  /**
+   * databrowser
+   */
+  dbcf: ComponentFactory<DataBrowser>
+  dataBrowser: ComponentRef<DataBrowser> = null
+  dbWidget: ComponentRef<WidgetUnit> = null
+
+  /**
+   * layerBrowser
+   */
+  lbcf: ComponentFactory<LayerBrowser>
+  layerBrowser: ComponentRef<LayerBrowser> = null
+  lbWidget: ComponentRef<WidgetUnit> = null
+
+  /**
+   * pluginBrowser
+   */
+  pbcf: ComponentFactory<PluginBannerUI>
+  pluginBanner: ComponentRef<PluginBannerUI> = null
+  pbWidget: ComponentRef<WidgetUnit> = null
+
+  get isMobile(){
+    return this.constantService.mobile
+  }
+
+  constructor(
+    private widgetServices:WidgetServices,
+    private injector:Injector,
+    private constantService:AtlasViewerConstantsServices,
+    public dbService: DatabrowserService,
+    cfr: ComponentFactoryResolver,
+    public pluginServices:PluginServices
+  ){
+
+    this.dbService.createDatabrowser = this.clickSearch.bind(this)
+
+    this.dbcf = cfr.resolveComponentFactory(DataBrowser)
+    this.lbcf = cfr.resolveComponentFactory(LayerBrowser)
+    this.pbcf = cfr.resolveComponentFactory(PluginBannerUI)
+  }
+
+  /**
+   * TODO
+   * temporary measure
+   * migrate to  nehubaOverlay
+   */
+  public clickSearch({ regions, template, parcellation }){
+    const dataBrowser = this.dbcf.create(this.injector)
+    dataBrowser.instance.regions = regions
+    dataBrowser.instance.template = template
+    dataBrowser.instance.parcellation = parcellation
+    const title = regions.length > 1
+      ? `Search: ${regions.length} regions`
+      : `Search: ${regions[0].name}`
+    const widgetUnit = this.widgetServices.addNewWidget(dataBrowser, {
+      exitable: true,
+      persistency: true,
+      state: 'floating',
+      title,
+      titleHTML: `<i class="fas fa-search"></i> ${title}`
+    })
+    return {
+      dataBrowser,
+      widgetUnit
+    }
+  }
+
+  public catchError(e) {
+    
+  }
+
+  public clickLayer(event: MouseEvent){
+
+    if (this.lbWidget) {
+      this.lbWidget.destroy()
+      this.lbWidget = null
+      return
+    }
+    this.layerBrowser = this.lbcf.create(this.injector)
+    this.lbWidget = this.widgetServices.addNewWidget(this.layerBrowser, {
+      exitable: true,
+      persistency: true,
+      state: 'floating',
+      title: 'Layer Browser',
+      titleHTML: '<i class="fas fa-layer-group"></i> Layer Browser'
+    })
+
+    this.lbWidget.onDestroy(() => {
+      this.layerBrowser = null
+      this.lbWidget = null
+    })
+
+    const el = event.currentTarget as HTMLElement
+    const top = el.offsetTop
+    const left = el.offsetLeft + 50
+    this.lbWidget.instance.position = [left, top]
+  }
+
+  public clickPlugins(event: MouseEvent){
+    if(this.pbWidget) {
+      this.pbWidget.destroy()
+      this.pbWidget = null
+      return
+    }
+    this.pluginBanner = this.pbcf.create(this.injector)
+    this.pbWidget = this.widgetServices.addNewWidget(this.pluginBanner, {
+      exitable: true,
+      persistency: true,
+      state: 'floating',
+      title: 'Plugin Browser',
+      titleHTML: '<i class="fas fa-tools"></i> Plugin Browser'
+    })
+
+    this.pbWidget.onDestroy(() => {
+      this.pbWidget = null
+      this.pluginBanner = null
+    })
+
+    const el = event.currentTarget as HTMLElement
+    const top = el.offsetTop
+    const left = el.offsetLeft + 50
+    this.pbWidget.instance.position = [left, top]
+  }
+
+  get databrowserIsShowing() {
+    return this.dataBrowser !== null
+  }
+
+  get layerbrowserIsShowing() {
+    return this.layerBrowser !== null
+  }
+
+  get pluginbrowserIsShowing() {
+    return this.pluginBanner !== null
+  }
+
+  get dataBrowserTitle() {
+    return `Browse`
+  }
+}
\ No newline at end of file
diff --git a/src/ui/menuicons/menuicons.style.css b/src/ui/menuicons/menuicons.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..8a1663369fecdd67c4c7c70ee73774316f49e430
--- /dev/null
+++ b/src/ui/menuicons/menuicons.style.css
@@ -0,0 +1,23 @@
+:host
+{
+  display: flex;
+  flex-direction: column;
+  align-items: flex-start;
+}
+
+:host > *
+{
+  margin-top: 1em;
+  display:inline-block;
+}
+
+:host >>> .tooltip-inner
+{
+  background-color: rgba(128, 128, 128, 0.5);
+}
+
+:host >>> .tooltip.right .tooltip-arrow::before,
+:host >>> .tooltip.right .tooltip-arrow
+{
+  border-right-color: rgba(128, 128, 128, 0.5);
+}
\ No newline at end of file
diff --git a/src/ui/menuicons/menuicons.template.html b/src/ui/menuicons/menuicons.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..4ea766718ce041ddf963a9d880fa55462020d4e8
--- /dev/null
+++ b/src/ui/menuicons/menuicons.template.html
@@ -0,0 +1,93 @@
+<logo-container *ngIf="!isMobile">
+</logo-container>
+
+<div
+  *ngIf="false"
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  class="btnWrapper">
+  <div
+    [tooltip]="dataBrowserTitle"
+    placement="right"
+    (click)="clickSearch($event)"
+    [ngClass]="databrowserIsShowing ? 'btn-primary' : 'btn-secondary'"
+    class="shadow btn btn-sm rounded-circle">
+    <i class="fas fa-search">
+      
+    </i>
+  </div>
+</div>
+
+<div
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  class="btnWrapper">
+  <div
+    tooltip="Layer"
+    placement="right"
+    (click)="clickLayer($event)"
+    [ngClass]="layerbrowserIsShowing ? 'btn-primary' : 'btn-secondary'"
+    class="shadow btn btn-sm rounded-circle">
+    <i class="fas fa-layer-group">
+      
+    </i>
+  </div>
+</div>
+
+<div
+  *ngIf="false"
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  class="btnWrapper">
+  <div
+    tooltip="Plugins"
+    (click)="clickPlugins($event)"
+    placement="right"
+    [ngClass]="pluginbrowserIsShowing ? 'btn-primary' : 'btn-secondary'"
+    class="shadow btn btn-sm rounded-circle">
+    <i class="fas fa-tools">
+      
+    </i>
+  </div>
+</div>
+
+<div
+  *ngFor="let manifest of pluginServices.fetchedPluginManifests"
+  [tooltip]="manifest.displayName || manifest.name"
+  placement="right"
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  class="btnWrapper">
+
+  <div
+    (click)="pluginServices.launchPlugin(manifest).catch(catchError)"
+    [ngClass]="!pluginServices.launchedPlugins.has(manifest.name) ? 'btn-outline-secondary' : pluginServices.pluginMinimised(manifest) ? 'btn-outline-info' : 'btn-info'"
+    class="shadow btn btn-sm rounded-circle">
+    {{ (manifest.displayName || manifest.name).slice(0, 1) }}
+  </div>
+</div>
+
+<div
+  *ngFor="let manifest of pluginServices.orphanPlugins"
+  [tooltip]="manifest.displayName || manifest.name"
+  placement="right"
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  class="btnWrapper">
+
+  <div
+    (click)="pluginServices.launchPlugin(manifest).catch(catchError)"
+    [ngClass]="pluginServices.pluginMinimised(manifest) ? 'btn-outline-info' : 'btn-info'"
+    class="shadow btn btn-sm rounded-circle">
+    {{ (manifest.displayName || manifest.name).slice(0, 1) }}
+  </div>
+</div>
+
+<div
+  *ngFor="let wu of dbService.instantiatedWidgetUnits"
+  [ngClass]="isMobile ? 'btnWrapper-lg' : ''"
+  placement="right"
+  [tooltip]="wu.title"
+  class="btnWrapper">
+  <div
+    (click)="widgetServices.minimisedWindow.delete(wu)"
+    [ngClass]="widgetServices.minimisedWindow.has(wu) ? 'btn-outline-info' : 'btn-info'"
+    class="shadow btn btn-sm rounded-circle">
+    <i class="fas fa-search"></i>
+  </div>
+</div>
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.component.ts b/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.component.ts
index 7ee2e92976f0a0ee3d40ccc235b96a0fbde81e08..77c4992fda331347cd90be8d5e8af289118a743e 100644
--- a/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.component.ts
+++ b/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.component.ts
@@ -18,13 +18,13 @@ export class LandmarkUnit implements OnChanges{
   @Input() highlight : boolean = false
   @Input() flatProjection : boolean = false
 
-  @Input() glyphiconClass : string = 'glyphicon-map-marker'
+  @Input() fasClass : string = 'fa-map-marker'
 
   @HostBinding('style.transform')
   transform : string = `translate(${this.positionX}px, ${this.positionY}px)`
 
   get className() {
-    return `glyphicon ${this.glyphiconClass}`
+    return `fas ${this.fasClass}`
   }
   styleNode(){
     return({
diff --git a/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.style.css b/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.style.css
index 1846dd902ecf97e188ab0c165ac62056a19eae3d..6a09196fe028c971d28eb6f22ac3e87d385dafb7 100644
--- a/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.style.css
+++ b/src/ui/nehubaContainer/landmarkUnit/landmarkUnit.style.css
@@ -79,7 +79,7 @@ div[landmarkContainer]
   top:0px;
 }
 
-[nodeView] > .glyphicon
+[nodeView] > .fas
 {
   margin-left:-0.5em;
   margin-top:-1em;
diff --git a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.component.ts b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.component.ts
index 1435c56ec723cabacf6431c434df7a527f7a5174..9be2dc7823401a0eeb0344ce0c6caaa2bc8b1a60 100644
--- a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.component.ts
+++ b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.component.ts
@@ -1,6 +1,7 @@
-import { Component, Input, Output,EventEmitter, ElementRef, ViewChild, AfterViewInit, ChangeDetectionStrategy, OnDestroy } from "@angular/core";
+import { Component, Input, Output,EventEmitter, ElementRef, ViewChild, AfterViewInit, ChangeDetectionStrategy, OnDestroy, OnInit, OnChanges } from "@angular/core";
 import { fromEvent, Subject, Observable, merge, concat, of, combineLatest } from "rxjs";
-import { map, switchMap, takeUntil, filter, scan, take } from "rxjs/operators";
+import { map, switchMap, takeUntil, filter, scan, take, tap } from "rxjs/operators";
+import { clamp } from "src/util/generator";
 
 @Component({
   selector : 'mobile-overlay',
@@ -24,15 +25,15 @@ div:not(.active) > span:before
   display: inline-block;
 }
     `
-  ],
-  changeDetection: ChangeDetectionStrategy.OnPush
+  ]
 })
 
-export class MobileOverlay implements AfterViewInit, OnDestroy{
+export class MobileOverlay implements OnInit, OnDestroy{
   @Input() tunableProperties : string [] = []
   @Output() deltaValue : EventEmitter<{delta:number, selectedProp : string}> = new EventEmitter() 
   @ViewChild('initiator', {read: ElementRef}) initiator : ElementRef
   @ViewChild('mobileMenuContainer', {read: ElementRef}) menuContainer : ElementRef
+  @ViewChild('intersector', {read: ElementRef}) intersector: ElementRef
 
   private _onDestroySubject : Subject<boolean> = new Subject()
 
@@ -42,52 +43,104 @@ export class MobileOverlay implements AfterViewInit, OnDestroy{
       ? this._focusedProperties
       : this.tunableProperties[0]
   }
+  get focusedIndex(){
+    return this._focusedProperties
+      ? this.tunableProperties.findIndex(p => p === this._focusedProperties)
+      : 0
+  }
 
   public showScreen$ : Observable<boolean>
   public showProperties$ : Observable<boolean>
+  public showDelta$: Observable<boolean>
+  public showInitiator$: Observable<boolean>
   private _drag$ : Observable<any>
-
+  private intersectionObserver: IntersectionObserver
+  
   ngOnDestroy(){
     this._onDestroySubject.next(true)
     this._onDestroySubject.complete()
   }
 
-  ngAfterViewInit(){
-
-    this.showScreen$ = merge(
-      fromEvent(this.initiator.nativeElement, 'touchstart'),
-      fromEvent(this.initiator.nativeElement, 'touchend'),
-    ).pipe(
-      map((ev:TouchEvent) => ev.touches.length === 1)
-    )
+  ngOnInit(){
+    const config = {
+      root: this.intersector.nativeElement,
+      threshold: [...Array(10)].map((_, k) => k / 10)
+    }
 
+    this.intersectionObserver = new IntersectionObserver((arg) => {
+      if (arg[0].isIntersecting) {
+        this.focusItemIndex = 2- Math.floor(arg[0].intersectionRatio * this.tunableProperties.length)
+      }
+    }, config)
+
+    this.intersectionObserver.observe(this.menuContainer.nativeElement)
+  
+    const scanDragScanAccumulator: (acc:TouchEvent[], item: TouchEvent, idx: number) => TouchEvent[] = (acc,curr) => acc.length < 2
+      ? acc.concat(curr)
+      : acc.slice(1).concat(curr)
+      
     this._drag$ = fromEvent(this.initiator.nativeElement, 'touchmove').pipe(
       takeUntil(fromEvent(this.initiator.nativeElement, 'touchend').pipe(
         filter((ev:TouchEvent) => ev.touches.length === 0)
       )),
       map((ev:TouchEvent) => (ev.preventDefault(), ev.stopPropagation(), ev)),
       filter((ev:TouchEvent) => ev.touches.length === 1),
-      scan((acc,curr) => acc.length < 2
-        ? acc.concat(curr)
-        : acc.slice(1).concat(curr), []),
+      scan(scanDragScanAccumulator, []),
       filter(ev => ev.length === 2)
     )
 
-    this.showProperties$ = fromEvent(this.initiator.nativeElement, 'touchstart').pipe(    
-      switchMap(() => concat(
-        this._drag$.pipe(
-          map(double => ({
-            deltaX : double[1].touches[0].screenX - double[0].touches[0].screenX,
-            deltaY : double[1].touches[0].screenY - double[0].touches[0].screenY
-          })),
-          scan((acc, _curr) => acc),
-          map(v => v.deltaY ** 2 > v.deltaX ** 2)
-        ),
-        of(false)
-        )
+    this.showProperties$ = concat(
+      of(false),
+      fromEvent(this.initiator.nativeElement, 'touchstart').pipe( 
+        switchMap(() => concat(
+          this._drag$.pipe(
+            map(double => ({
+              deltaX : double[1].touches[0].screenX - double[0].touches[0].screenX,
+              deltaY : double[1].touches[0].screenY - double[0].touches[0].screenY
+            })),
+            scan((acc, _curr) => acc),
+            map(v => v.deltaY ** 2 > v.deltaX ** 2)
+          ),
+          of(false)
+        ))
+      )
+    )
+
+
+    this.showDelta$ = concat(
+      of(false),
+      fromEvent(this.initiator.nativeElement, 'touchstart').pipe( 
+        switchMap(() => concat(
+          this._drag$.pipe(
+            map(double => ({
+              deltaX : double[1].touches[0].screenX - double[0].touches[0].screenX,
+              deltaY : double[1].touches[0].screenY - double[0].touches[0].screenY
+            })),
+            scan((acc, _curr) => acc),
+            map(v => v.deltaX ** 2 > v.deltaY ** 2)
+          ),
+          of(false)
+        ))
       )
     )
 
+    this.showInitiator$ = combineLatest(
+      this.showProperties$,
+      this.showDelta$
+    ).pipe(
+      map(([flag1, flag2]) => !flag1 && !flag2)
+    )
+
+    this.showScreen$ = combineLatest(
+      merge(
+        fromEvent(this.initiator.nativeElement, 'touchstart'),
+        fromEvent(this.initiator.nativeElement, 'touchend')
+      ),
+      this.showInitiator$
+    ).pipe(
+      map(([ev, showInitiator] : [TouchEvent, boolean]) => showInitiator && ev.touches.length === 1)
+    )
+
     fromEvent(this.initiator.nativeElement, 'touchstart').pipe(
       switchMap(() => this._drag$.pipe(
         map(double => ({
@@ -130,7 +183,16 @@ export class MobileOverlay implements AfterViewInit, OnDestroy{
       filter(v => v[0]),
       map(v => v[1]),
       takeUntil(this._onDestroySubject)
-    ).subscribe(v => this.scrollHeight = v.deltaY)
+    ).subscribe(v => {
+      const deltaY = v.deltaY
+      const cellHeight = this.menuContainer && this.tunableProperties && this.tunableProperties.length > 0 && this.menuContainer.nativeElement.offsetHeight / this.tunableProperties.length
+      const adjHeight = - this.focusedIndex * cellHeight - cellHeight * 0.5
+
+      const min = - cellHeight * 0.5
+      const max = - this.tunableProperties.length * cellHeight + cellHeight * 0.5
+      const finalYTranslate = clamp(adjHeight + deltaY, min, max )
+      this.menuTransform = `translate(0px, ${finalYTranslate}px)`
+    })
 
     this.showProperties$.pipe(
       takeUntil(this._onDestroySubject),
@@ -139,47 +201,12 @@ export class MobileOverlay implements AfterViewInit, OnDestroy{
       if(this.focusItemIndex >= 0){
         this._focusedProperties = this.tunableProperties[this.focusItemIndex]
       }
-      this.scrollHeight = 0
     })
+    
   }
 
-  scrollHeight : number = 0
-
-  get defaultY(){
-    return this.tunableProperties.findIndex(p => p === this.focusedProperty) * this.menuCellHeight
-  }
-
-  get menuTransform(){
-    return `translate(0px, ${this.menuYTranslate}px)`
-  }
-
-  get menuYTranslate(){
-    return this.menuContainer
-      ? Math.max(
-          Math.min(
-            this.defaultY + this.scrollHeight, 
-            this.menuContainerHeight / 2 - this.menuCellHeight / 2
-            ),
-          - this.menuContainerHeight / 2 + this.menuCellHeight / 2
-          )
-      : this.defaultY
-  }
-
-  get menuContainerHeight(){
-    return this.menuContainer
-      ? this.menuContainer.nativeElement.offsetHeight
-      : 0
-  }
+  public menuTransform = `translate(0px, 0px)`
 
-  get menuCellHeight(){
-    return this.tunableProperties.length > 0
-      ? this.menuContainerHeight / this.tunableProperties.length
-      : 0
-  }
+  public focusItemIndex: number = 0
 
-  get focusItemIndex():number{
-    return this.menuContainer
-      ? Math.floor((this.menuContainerHeight / 2 - this.menuYTranslate) / this.menuCellHeight)
-      : -1
-  }
 }
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.style.css b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.style.css
index 881f794c5971dc9d57a77ede558fffa70bb20f54..df352ebd76c28846b95958ebb0e4ed143da236d2 100644
--- a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.style.css
+++ b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.style.css
@@ -5,7 +5,7 @@
   top: 0;
   left: 0;
   position: absolute;
-  z-index: 9999;
+  z-index: 999;
 
   pointer-events: none;
 }
@@ -18,24 +18,34 @@
   left: 0;
   position: absolute;
 
-  display: flex;
-  justify-content: center;
-  align-items: center;
-
   color : black;
   background-color: rgba(255, 255, 255, 0.5);
 }
 
+
 :host-context([darktheme="true"]) [screen]
 {
   color : white;
   background-color: rgba(0, 0, 0, 0.5);
 }
 
+[intersector]
+{
+  position: absolute;
+  top: 50%;
+  left: 0;
+  width: 100%;
+  height: 50%;
+
+  display: flex;
+  flex-direction: column;
+  justify-content: flex-start;
+  align-items: center;
+}
+
 [mobileMenuContainer]
 {
-  z-index: 9999;
-  transform: translate(0, 155px);
+  z-index: 1000;
 }
 
 .scrollFocus:after
diff --git a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.template.html b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.template.html
index 541b687d8831e900e346db53607c2f14d812a67c..9770b46d5a6473606f49bd94c70893e50db71c61 100644
--- a/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.template.html
+++ b/src/ui/nehubaContainer/mobileOverlay/mobileOverlay.template.html
@@ -1,18 +1,26 @@
-<div screen *ngIf = "showProperties$ | async">
-  <div [style.transform] = "menuTransform" class = "btn-group-vertical" role = "group" mobileMenuContainer #mobileMenuContainer>
-    <div 
-      *ngFor = "let p of tunableProperties; let i = index"
-      [ngClass] = "{'active' : p === focusedProperty, 'scrollFocus' : i === focusItemIndex}" 
-      class = "btn btn-default theme-controlled">
-      <span>
-        {{ p }}
-      </span>
+<div screen [hidden]="!(showProperties$ | async)">
+  <div intersector #intersector>
+    <div [style.transform] = "menuTransform" class = "btn-group-vertical" role = "group" mobileMenuContainer #mobileMenuContainer>
+      <div 
+        *ngFor = "let p of tunableProperties; let i = index"
+        [ngClass] = "{'active' : p === focusedProperty, 'scrollFocus': i === focusItemIndex}" 
+        class = "btn btn-default theme-controlled property scrollFocus">
+        <!-- scrollFocus class -->
+        <span>
+          {{ p }}
+        </span>
+      </div>
     </div>
   </div>
 </div>
-<ng-content *ngIf = "showScreen$ | async" select = "[guide]" guide>
+
+<ng-content *ngIf="showDelta$ | async" select="[delta]" guide>
 </ng-content>
-<div #initiator>
+
+<ng-content *ngIf="showScreen$ | async" select="[guide]" guide>
+</ng-content>
+
+<div [hidden]="!(showInitiator$ | async)" #initiator>
   <ng-content select="[initiator]">
   </ng-content>
 </div>
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/nehubaContainer.component.ts b/src/ui/nehubaContainer/nehubaContainer.component.ts
index 9c8496ccf529ad10b7d7e5032df43a435febf9d4..21cf79327d3d41222895e53c0f4e6f737cff494c 100644
--- a/src/ui/nehubaContainer/nehubaContainer.component.ts
+++ b/src/ui/nehubaContainer/nehubaContainer.component.ts
@@ -1,13 +1,17 @@
 import { Component, ViewChild, ViewContainerRef, ComponentFactoryResolver, ComponentFactory, ComponentRef, OnInit, OnDestroy, ElementRef } from "@angular/core";
 import { NehubaViewerUnit } from "./nehubaViewer/nehubaViewer.component";
 import { Store, select } from "@ngrx/store";
-import { ViewerStateInterface, safeFilter, SELECT_REGIONS, getLabelIndexMap, CHANGE_NAVIGATION, isDefined, MOUSE_OVER_SEGMENT, USER_LANDMARKS, ADD_NG_LAYER, REMOVE_NG_LAYER, NgViewerStateInterface, MOUSE_OVER_LANDMARK, SELECT_LANDMARKS, Landmark, PointLandmarkGeometry, PlaneLandmarkGeometry, OtherLandmarkGeometry } from "../../services/stateStore.service";
+import { ViewerStateInterface, safeFilter, CHANGE_NAVIGATION, isDefined, USER_LANDMARKS, ADD_NG_LAYER, REMOVE_NG_LAYER, NgViewerStateInterface, MOUSE_OVER_LANDMARK, SELECT_LANDMARKS, Landmark, PointLandmarkGeometry, PlaneLandmarkGeometry, OtherLandmarkGeometry, getNgIds, getMultiNgIdsRegionsLabelIndexMap, generateLabelIndexId } from "../../services/stateStore.service";
 import { Observable, Subscription, fromEvent, combineLatest, merge } from "rxjs";
-import { filter,map, take, scan, debounceTime, distinctUntilChanged, switchMap, skip, withLatestFrom, buffer } from "rxjs/operators";
+import { filter,map, take, scan, debounceTime, distinctUntilChanged, switchMap, skip, withLatestFrom, buffer, tap } from "rxjs/operators";
 import { AtlasViewerAPIServices, UserLandmark } from "../../atlasViewer/atlasViewer.apiService.service";
 import { timedValues } from "../../util/generator";
-import { AtlasViewerDataService } from "../../atlasViewer/atlasViewer.dataService.service";
 import { AtlasViewerConstantsServices } from "../../atlasViewer/atlasViewer.constantService.service";
+import { ViewerConfiguration } from "src/services/state/viewerConfig.store";
+import { pipeFromArray } from "rxjs/internal/util/pipe";
+import { NEHUBA_READY } from "src/services/state/ngViewerState.store";
+import { MOUSE_OVER_SEGMENTS } from "src/services/state/uiState.store";
+import { SELECT_REGIONS_WITH_ID } from "src/services/state/viewerState.store";
 
 @Component({
   selector : 'ui-nehuba-container',
@@ -29,6 +33,8 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
   public viewerLoaded : boolean = false
 
+  private viewerPerformanceConfig$: Observable<ViewerConfiguration>
+
   public sliceViewLoading0$: Observable<boolean>
   public sliceViewLoading1$: Observable<boolean>
   public sliceViewLoading2$: Observable<boolean>
@@ -52,17 +58,17 @@ export class NehubaContainer implements OnInit, OnDestroy{
   private spatialResultsVisible : boolean = false
 
   private selectedTemplate : any | null
-  private selectedRegionIndexSet : Set<number> = new Set()
+  private selectedRegionIndexSet : Set<string> = new Set()
   public fetchedSpatialData : Landmark[] = []
 
-  private ngLayersRegister : NgViewerStateInterface = {layers : [], forceShowSegment: null}
+  private ngLayersRegister : Partial<NgViewerStateInterface> = {layers : [], forceShowSegment: null}
   private ngLayers$ : Observable<NgViewerStateInterface>
   
   public selectedParcellation : any | null
 
   private cr : ComponentRef<NehubaViewerUnit>
   public nehubaViewer : NehubaViewerUnit
-  private regionsLabelIndexMap : Map<number,any> = new Map()
+  private multiNgIdsRegionsLabelIndexMap: Map<string, Map<number, any>> = new Map()
   private landmarksLabelIndexMap : Map<number, any> = new Map()
   private landmarksNameMap : Map<string,number> = new Map()
   
@@ -72,15 +78,27 @@ export class NehubaContainer implements OnInit, OnDestroy{
   private nehubaViewerSubscriptions : Subscription[] = []
 
   public nanometersToOffsetPixelsFn : Function[] = []
+  private viewerConfig : Partial<ViewerConfiguration> = {}
 
   constructor(
     private constantService : AtlasViewerConstantsServices,
-    private atlasViewerDataService : AtlasViewerDataService,
     private apiService :AtlasViewerAPIServices,
     private csf:ComponentFactoryResolver,
     private store : Store<ViewerStateInterface>,
     private elementRef : ElementRef
   ){
+    this.viewerPerformanceConfig$ = this.store.pipe(
+      select('viewerConfigState'),
+      /**
+       * TODO: this is only a bandaid fix. Technically, we should also implement
+       * logic to take the previously set config to apply oninit
+       */
+      distinctUntilChanged(),
+      debounceTime(200),
+      tap(viewerConfig => this.viewerConfig = viewerConfig ),
+      filter(() => isDefined(this.nehubaViewer) && isDefined(this.nehubaViewer.nehubaViewer))
+    )
+
     this.nehubaViewerFactory = this.csf.resolveComponentFactory(NehubaViewerUnit)
 
     this.newViewer$ = this.store.pipe(
@@ -100,8 +118,8 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
     this.selectedRegions$ = this.store.pipe(
       select('viewerState'),
-      safeFilter('regionsSelected'),
-      map(state=>state.regionsSelected)
+      select('regionsSelected'),
+      filter(rs => !!rs)
     )
 
     this.selectedLandmarks$ = this.store.pipe(
@@ -177,28 +195,32 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
     // TODO hack, even though octant is hidden, it seems with VTK, one can highlight
     this.onHoverSegmentName$ = combineLatest(
-        this.store.pipe(
-          select('uiState'),
-          filter(state=>isDefined(state)),
-          map(state=>state.mouseOverSegment ?
-            state.mouseOverSegment.constructor === Number ? 
-              state.mouseOverSegment.toString() : 
-              state.mouseOverSegment.name :
-            '' ),
-          distinctUntilChanged()
-        ),
-        this.onHoverLandmark$
-      ).pipe(
-        map(results => results[1] === null ? results[0] : '')
-      )
+      this.store.pipe(
+        select('uiState'),
+        filter(state=>isDefined(state)),
+        map(state=>state.mouseOverSegment ?
+          state.mouseOverSegment.constructor === Number ? 
+            state.mouseOverSegment.toString() : 
+            state.mouseOverSegment.name :
+          '' ),
+        distinctUntilChanged()
+      ),
+      this.onHoverLandmark$
+    ).pipe(
+      map(results => results[1] === null ? results[0] : '')
+    )
     
     /* each time a new viewer is initialised, take the first event to get the translation function */
     this.newViewer$.pipe(
-      switchMap(() => fromEvent(this.elementRef.nativeElement, 'sliceRenderEvent')
-        .pipe(
-          ...takeOnePipe
-        )
-      )
+      // switchMap(() => fromEvent(this.elementRef.nativeElement, 'sliceRenderEvent')
+      //   .pipe(
+      //     ...takeOnePipe
+      //   )
+      // )
+
+      switchMap(() => pipeFromArray([...takeOnePipe])(fromEvent(this.elementRef.nativeElement, 'sliceRenderEvent')))
+
+
     ).subscribe((events)=>{
       [0,1,2].forEach(idx=>this.nanometersToOffsetPixelsFn[idx] = (events[idx] as any).detail.nanometersToOffsetPixels)
     })
@@ -245,15 +267,18 @@ export class NehubaContainer implements OnInit, OnDestroy{
           const e = (event as any)
           const lastLoadedIdString = e.detail.lastLoadedMeshId.split(',')[0]
           const lastLoadedIdNum = Number(lastLoadedIdString)
+          /**
+           * TODO dig into event detail to see if the exact mesh loaded
+           */
           return e.detail.meshesLoaded >= this.nehubaViewer.numMeshesToBeLoaded
             ? null
             : isNaN(lastLoadedIdNum)
               ? 'Loading unknown chunk'
               : lastLoadedIdNum >= 65500
                 ? 'Loading auxiliary chunk'
-                : this.regionsLabelIndexMap.get(lastLoadedIdNum)
-                  ? `Loading ${this.regionsLabelIndexMap.get(lastLoadedIdNum).name}`
-                  : 'Loading unknown chunk ...'
+                // : this.regionsLabelIndexMap.get(lastLoadedIdNum)
+                //   ? `Loading ${this.regionsLabelIndexMap.get(lastLoadedIdNum).name}`
+                  : 'Loading meshes ...'
         })
       )
 
@@ -274,6 +299,12 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
   ngOnInit(){
 
+    this.subscriptions.push(
+      this.viewerPerformanceConfig$.subscribe(config => {
+        this.nehubaViewer.applyPerformanceConfig(config)
+      })
+    )
+
     this.subscriptions.push(
       this.fetchedSpatialDatasets$.subscribe(datasets => {
         this.landmarksLabelIndexMap = new Map(datasets.map((v,idx) => [idx, v]) as [number, any][])
@@ -306,7 +337,8 @@ export class NehubaContainer implements OnInit, OnDestroy{
                     : null)
             )
         }else{
-          this.nehubaViewer.removeSpatialSearch3DLandmarks()
+          if (this.nehubaViewer && this.nehubaViewer.removeSpatialSearch3DLandmarks instanceof Function)
+            this.nehubaViewer.removeSpatialSearch3DLandmarks()
         }
       })
     )
@@ -317,7 +349,7 @@ export class NehubaContainer implements OnInit, OnDestroy{
       ).subscribe(landmarks => {
         this.userLandmarks = landmarks
         if(this.nehubaViewer){
-          this.nehubaViewer.addUserLandmarks(landmarks)
+          this.nehubaViewer.updateUserLandmarks(landmarks)
         }
       })
     )
@@ -349,8 +381,22 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
     /* order of subscription will determine the order of execution */
     this.subscriptions.push(
-      this.newViewer$.subscribe((state)=>{
-
+      this.newViewer$.pipe(
+        map(state => {
+          const deepCopiedState = JSON.parse(JSON.stringify(state))
+          const navigation = deepCopiedState.templateSelected.nehubaConfig.dataset.initialNgState.navigation
+          if (!navigation) {
+            return deepCopiedState
+          }
+          navigation.zoomFactor = calculateSliceZoomFactor(navigation.zoomFactor)
+          deepCopiedState.templateSelected.nehubaConfig.dataset.initialNgState.navigation = navigation
+          return deepCopiedState
+        })
+      ).subscribe((state)=>{
+        this.store.dispatch({
+          type: NEHUBA_READY,
+          nehubaReady: false
+        })
         this.nehubaViewerSubscriptions.forEach(s=>s.unsubscribe())
 
         this.selectedTemplate = state.templateSelected
@@ -409,7 +455,7 @@ export class NehubaContainer implements OnInit, OnDestroy{
           if(!this.nehubaViewer) return
 
           /* selectedregionindexset needs to be updated regardless of forceshowsegment */
-          this.selectedRegionIndexSet = new Set(regions.map(r=>Number(r.labelIndex)))
+          this.selectedRegionIndexSet = new Set(regions.map(({ngId, labelIndex})=>generateLabelIndexId({ ngId, labelIndex })))
 
           if( forceShowSegment === false || (forceShowSegment === null && hideSegmentFlag) ){
             this.nehubaViewer.hideAllSeg()
@@ -463,7 +509,7 @@ export class NehubaContainer implements OnInit, OnDestroy{
         Object.assign({},navigation,{
           positionReal : true
         })
-      this.nehubaViewer.initRegions = regions.map(re=>re.labelIndex)
+      this.nehubaViewer.initRegions = regions.map(({ ngId, labelIndex }) =>generateLabelIndexId({ ngId, labelIndex }))
     })
 
     this.subscriptions.push(
@@ -544,53 +590,18 @@ export class NehubaContainer implements OnInit, OnDestroy{
   public showObliqueRotate$ : Observable<boolean>
 
   ngAfterViewInit(){
-    // if(this.isMobile){
-
-    //   this.showObliqueScreen$ = merge(
-    //     fromEvent(this.mobileObliqueCtrl.nativeElement, 'touchstart'),
-    //     fromEvent(this.mobileObliqueCtrl.nativeElement, 'touchend')
-    //   ).pipe(
-    //     map((ev:TouchEvent) => ev.touches.length === 1)
-    //   )
-
-    //   fromEvent(this.mobileObliqueCtrl.nativeElement, 'touchstart').pipe(
-    //     switchMap(() => fromEvent(this.mobileObliqueCtrl.nativeElement, 'touchmove').pipe(
-    //       takeUntil(fromEvent(this.mobileObliqueCtrl.nativeElement, 'touchend').pipe(
-    //         filter((ev:TouchEvent) => ev.touches.length === 0)
-    //       )),
-    //       map((ev:TouchEvent) => (ev.preventDefault(), ev.stopPropagation(), ev)),
-    //       filter((ev:TouchEvent) => ev.touches.length === 1),
-    //       map((ev:TouchEvent) => [ev.touches[0].screenX, ev.touches[0].screenY] ),
-
-    //       /* TODO reduce boiler plate, export */
-    //       scan((acc,curr) => acc.length < 2
-    //         ? acc.concat([curr])
-    //         : acc.slice(1).concat([curr]), []),
-    //       filter(isdouble => isdouble.length === 2),
-    //       map(double => ({
-    //         deltaX : double[1][0] - double[0][0],
-    //         deltaY : double[1][1] - double[0][1]
-    //       }))
-    //     ))
-    //   )
-    //     .subscribe(({deltaX, deltaY}) => {
-          
-          
-          
-    //     })
-    // }
   }
 
   ngOnDestroy(){
     this.subscriptions.forEach(s=>s.unsubscribe())
   }
 
-  get tunableMobileProperties(){
-    return ['Oblique Rotate X', 'Oblique Rotate Y', 'Oblique Rotate Z']
-  }
+  public tunableMobileProperties = ['Oblique Rotate X', 'Oblique Rotate Y', 'Oblique Rotate Z']
+  public selectedProp = null
 
   handleMobileOverlayEvent(obj:any){
     const {delta, selectedProp} = obj
+    this.selectedProp = selectedProp
 
     const idx = this.tunableMobileProperties.findIndex(p => p === selectedProp)
     idx === 0
@@ -638,11 +649,24 @@ export class NehubaContainer implements OnInit, OnDestroy{
   }
 
   private handleParcellation(parcellation:any){
-    this.regionsLabelIndexMap = getLabelIndexMap(parcellation.regions)
-    this.nehubaViewer.regionsLabelIndexMap = this.regionsLabelIndexMap
+    /**
+     * parcellaiton may be undefined
+     */
+    if ( !(parcellation && parcellation.regions)) {
+      return
+    }
+
+    /**
+     * first, get all all the ngIds, including parent id from parcellation (if defined)
+     */
+    const ngIds = getNgIds(parcellation.regions).concat( parcellation.ngId ? parcellation.ngId : [])
+
+    this.multiNgIdsRegionsLabelIndexMap = getMultiNgIdsRegionsLabelIndexMap(parcellation)
+
+    this.nehubaViewer.multiNgIdsLabelIndexMap = this.multiNgIdsRegionsLabelIndexMap
 
     /* TODO replace with proper KG id */
-    this.nehubaViewer.parcellationId = parcellation.ngId
+    this.nehubaViewer.ngIds = [...ngIds]
     this.selectedParcellation = parcellation
   }
 
@@ -652,6 +676,10 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
   private createNewNehuba(template:any){
 
+    /**
+     * TODO if plugin subscribes to viewerHandle, and then new template is selected, changes willl not be be sent
+     * could be considered as a bug. 
+     */
     this.apiService.interactiveViewer.viewerHandle = null
 
     this.viewerLoaded = true
@@ -660,7 +688,19 @@ export class NehubaContainer implements OnInit, OnDestroy{
     this.container.clear()
     this.cr = this.container.createComponent(this.nehubaViewerFactory)
     this.nehubaViewer = this.cr.instance
-    this.nehubaViewer.config = template.nehubaConfig
+
+    /**
+     * apply viewer config such as gpu limit
+     */
+    const { gpuLimit = null } = this.viewerConfig
+    const { nehubaConfig } = template
+    
+    if (gpuLimit) {
+      const initialNgState = nehubaConfig && nehubaConfig.dataset && nehubaConfig.dataset.initialNgState
+      initialNgState['gpuLimit'] = gpuLimit
+    }
+    
+    this.nehubaViewer.config = nehubaConfig
 
     /* TODO replace with id from KG */
     this.nehubaViewer.templateId = template.name
@@ -669,6 +709,18 @@ export class NehubaContainer implements OnInit, OnDestroy{
       this.nehubaViewer.debouncedViewerPositionChange.subscribe(this.handleEmittedNavigationChange.bind(this))
     )
 
+    this.nehubaViewerSubscriptions.push(
+      /**
+       * TODO when user selects new template, window.viewer 
+       */
+      this.nehubaViewer.nehubaReady.subscribe(() => {
+        this.store.dispatch({
+          type: NEHUBA_READY,
+          nehubaReady: true
+        })
+      })
+    )
+
     this.nehubaViewerSubscriptions.push(
       this.nehubaViewer.debouncedViewerPositionChange.pipe(
         distinctUntilChanged((a,b) => 
@@ -676,11 +728,34 @@ export class NehubaContainer implements OnInit, OnDestroy{
       ).subscribe(this.handleNavigationPositionAndNavigationZoomChange.bind(this))
     )
 
+    const accumulatorFn: (
+      acc:Map<string, { segment: string | null, segmentId: number | null }>,
+      arg: {layer: {name: string}, segmentId: number|null, segment: string | null}
+    ) => Map<string, {segment: string | null, segmentId: number|null}>
+    = (acc, arg) => {
+      const { layer, segment, segmentId } = arg
+      const { name } = layer
+      const newMap = new Map(acc)
+      newMap.set(name, {segment, segmentId})
+      return newMap
+    }
+
     this.nehubaViewerSubscriptions.push(
-      this.nehubaViewer.mouseoverSegmentEmitter.subscribe(emitted => {
+      
+      this.nehubaViewer.mouseoverSegmentEmitter.pipe(
+        scan(accumulatorFn, new Map()),
+        map(map => Array.from(map.entries()).filter(([_ngId, { segmentId }]) => segmentId))
+      ).subscribe(arrOfArr => {
         this.store.dispatch({
-          type : MOUSE_OVER_SEGMENT,
-          segment : emitted
+          type: MOUSE_OVER_SEGMENTS,
+          segments: arrOfArr.map( ([ngId, {segment, segmentId}]) => {
+            return {
+              layer: {
+                name: ngId,
+              },
+              segment: segment || `${ngId}#${segmentId}`
+            }
+          } )
         })
       })
     )
@@ -694,22 +769,41 @@ export class NehubaContainer implements OnInit, OnDestroy{
       })
     )
 
+    const onhoverSegments$ = this.store.pipe(
+      select('uiState'),
+      select('mouseOverSegments'),
+      filter(v => !!v),
+      distinctUntilChanged((o, n) => o.length === n.length && n.every(segment => o.find(oSegment => oSegment.layer.name === segment.layer.name && oSegment.segment === segment.segment) ) )
+    )
+
     // TODO hack, even though octant is hidden, it seems with vtk one can mouse on hover
     this.nehubaViewerSubscriptions.push(
       this.nehubaViewer.regionSelectionEmitter.pipe(
         withLatestFrom(this.onHoverLandmark$),
         filter(results => results[1] === null),
-        map(results => results[0])
-      ).subscribe((region:any) => {
-        this.selectedRegionIndexSet.has(region.labelIndex) ?
-          this.store.dispatch({
-            type : SELECT_REGIONS,
-            selectRegions : [...this.selectedRegionIndexSet].filter(idx=>idx!==region.labelIndex).map(idx=>this.regionsLabelIndexMap.get(idx))
-          }) :
-          this.store.dispatch({
-            type : SELECT_REGIONS,
-            selectRegions : [...this.selectedRegionIndexSet].map(idx=>this.regionsLabelIndexMap.get(idx)).concat(region)
+        withLatestFrom(onhoverSegments$),
+        map(results => results[1]),
+        filter(arr => arr.length > 0),
+        map(arr => {
+          return arr.map(({ layer, segment }) => {
+            const ngId = segment.ngId || layer.name
+            const labelIndex = segment.labelIndex
+            return generateLabelIndexId({ ngId, labelIndex })
           })
+        })
+      ).subscribe((ids:string[]) => {
+        const deselectFlag = ids.some(id => this.selectedRegionIndexSet.has(id))
+
+        const set = new Set(this.selectedRegionIndexSet)
+        if (deselectFlag) {
+          ids.forEach(id => set.delete(id))
+        } else {
+          ids.forEach(id => set.add(id))
+        }
+        this.store.dispatch({
+          type: SELECT_REGIONS_WITH_ID,
+          selectRegionIds: [...set]
+        })
       })
     )
 
@@ -735,11 +829,16 @@ export class NehubaContainer implements OnInit, OnDestroy{
         orientation : quat
       }),
       showSegment : (labelIndex) => {
-        if(!this.selectedRegionIndexSet.has(labelIndex)) 
-          this.store.dispatch({
-            type : SELECT_REGIONS,
-            selectRegions :  [labelIndex, ...this.selectedRegionIndexSet]
-          })
+        /**
+         * TODO reenable with updated select_regions api
+         */
+        console.warn(`showSegment is temporarily disabled`)
+
+        // if(!this.selectedRegionIndexSet.has(labelIndex)) 
+        //   this.store.dispatch({
+        //     type : SELECT_REGIONS,
+        //     selectRegions :  [labelIndex, ...this.selectedRegionIndexSet]
+        //   })
       },
       add3DLandmarks : landmarks => {
         // TODO check uniqueness of ID
@@ -761,22 +860,33 @@ export class NehubaContainer implements OnInit, OnDestroy{
         })
       },
       hideSegment : (labelIndex) => {
-        if(this.selectedRegionIndexSet.has(labelIndex)){
-          this.store.dispatch({
-            type :SELECT_REGIONS,
-            selectRegions : [...this.selectedRegionIndexSet].filter(num=>num!==labelIndex)
-          })
-        }
+        /**
+         * TODO reenable with updated select_regions api
+         */
+        console.warn(`hideSegment is temporarily disabled`)
+
+        // if(this.selectedRegionIndexSet.has(labelIndex)){
+        //   this.store.dispatch({
+        //     type :SELECT_REGIONS,
+        //     selectRegions : [...this.selectedRegionIndexSet].filter(num=>num!==labelIndex)
+        //   })
+        // }
       },
       showAllSegments : () => {
+        const selectRegionIds = []
+        this.multiNgIdsRegionsLabelIndexMap.forEach((map, ngId) => {
+          Array.from(map.keys()).forEach(labelIndex => {
+            selectRegionIds.push(generateLabelIndexId({ ngId, labelIndex }))
+          })
+        })
         this.store.dispatch({
-          type : SELECT_REGIONS,
-          selectRegions : this.regionsLabelIndexMap.keys()
+          type : SELECT_REGIONS_WITH_ID,
+          selectRegionIds 
         })
       },
       hideAllSegments : () => {
         this.store.dispatch({
-          type : SELECT_REGIONS,
+          type : SELECT_REGIONS_WITH_ID,
           selectRegions : []
         })
       },
@@ -794,11 +904,12 @@ export class NehubaContainer implements OnInit, OnDestroy{
         fromEvent(this.elementRef.nativeElement,'mousemove').pipe(
           map((ev:MouseEvent)=>({eventName :'mousemove',event:ev}))
         ),
-
-        //TODO solve bug, mousedown does not get bubbled up because neuroglancer preventsPropagation
-        fromEvent(this.elementRef.nativeElement,'mousedown').pipe(
-          map((ev:MouseEvent)=>({eventName :'mousedown',event:ev}))
-        ),
+        /**
+         * neuroglancer prevents propagation, so use capture instead
+         */
+        Observable.create(observer => {
+          this.elementRef.nativeElement.addEventListener('mousedown', event => observer.next({eventName: 'mousedown', event}), true)
+        }) as Observable<{eventName: string, event: MouseEvent}>,
         fromEvent(this.elementRef.nativeElement,'mouseup').pipe(
           map((ev:MouseEvent)=>({eventName :'mouseup',event:ev}))
         ),
@@ -815,13 +926,13 @@ export class NehubaContainer implements OnInit, OnDestroy{
 
     const center = navigation.position.map(n=>n/1e6)
     const searchWidth = this.constantService.spatialWidth / 4 * navigation.zoom / 1e6
-    const templateSpace = this.selectedTemplate.name
-    this.atlasViewerDataService.spatialSearch({
-      center,
-      searchWidth,
-      templateSpace,
-      pageNo : 0
-    })
+    const { selectedTemplate } = this
+    // this.atlasViewerDataService.spatialSearch({
+    //   center,
+    //   searchWidth,
+    //   selectedTemplate,
+    //   pageNo : 0
+    // })
   }
 
   /* because the navigation can be changed from two sources, 
@@ -860,6 +971,11 @@ export class NehubaContainer implements OnInit, OnDestroy{
     /* extract the animation object */
     const { animation, ..._navigation } = navigation
 
+    /**
+     * remove keys that are falsy
+     */
+    Object.keys(_navigation).forEach(key => (!_navigation[key]) && delete _navigation[key])
+    
     if( animation ){
       /* animated */
 
@@ -915,90 +1031,6 @@ export class NehubaContainer implements OnInit, OnDestroy{
     }
   }
 
-  /* related to info-card */
-
-  statusPanelRealSpace : boolean = true
-
-  get mouseCoord():string{
-    return this.nehubaViewer ?
-      this.statusPanelRealSpace ? 
-        this.nehubaViewer.mousePosReal ? 
-          Array.from(this.nehubaViewer.mousePosReal.map(n=> isNaN(n) ? 0 : n/1e6))
-            .map(n=>n.toFixed(3)+'mm').join(' , ') : 
-          '0mm , 0mm , 0mm (mousePosReal not yet defined)' :
-        this.nehubaViewer.mousePosVoxel ? 
-          this.nehubaViewer.mousePosVoxel.join(' , ') :
-          '0 , 0 , 0 (mousePosVoxel not yet defined)' :
-      '0 , 0 , 0 (nehubaViewer not defined)'
-  }
-
-  editingNavState : boolean = false
-
-  textNavigateTo(string:string){
-    if(string.split(/[\s|,]+/).length>=3 && string.split(/[\s|,]+/).slice(0,3).every(entry=>!isNaN(Number(entry.replace(/mm/,''))))){
-      const pos = (string.split(/[\s|,]+/).slice(0,3).map((entry)=>Number(entry.replace(/mm/,''))*(this.statusPanelRealSpace ? 1000000 : 1)))
-      this.nehubaViewer.setNavigationState({
-        position : (pos as [number,number,number]),
-        positionReal : this.statusPanelRealSpace
-      })
-    }else{
-      console.log('input did not parse to coordinates ',string)
-    }
-  }
-
-  navigationValue(){
-    return this.nehubaViewer ? 
-      this.statusPanelRealSpace ? 
-        Array.from(this.nehubaViewer.navPosReal.map(n=> isNaN(n) ? 0 : n/1e6))
-          .map(n=>n.toFixed(3)+'mm').join(' , ') :
-        Array.from(this.nehubaViewer.navPosVoxel.map(n=> isNaN(n) ? 0 : n)).join(' , ') :
-      `[0,0,0] (neubaViewer is undefined)`
-  }
-
-  get showCitation(){
-    return this.selectedTemplate && this.selectedTemplate.properties && this.selectedTemplate.properties.publications && this.selectedTemplate.properties.publications.constructor === Array
-  }
-
-  resetNavigation(){
-    const initialNgState = this.selectedTemplate.nehubaConfig.dataset.initialNgState
-    
-    const perspectiveZoom = initialNgState ? initialNgState.perspectiveZoom : undefined
-    const perspectiveOrientation = initialNgState ? initialNgState.perspectiveOrientation : undefined
-    const zoom = initialNgState ? 
-      initialNgState.navigation ?
-        initialNgState.navigation.zoomFactor :
-        undefined :
-      undefined
-
-    const position = initialNgState ? 
-      initialNgState.navigation ?
-        initialNgState.navigation.pose ?
-          initialNgState.navigation.pose.position.voxelCoordinates ?
-            initialNgState.navigation.pose.position.voxelCoordinates :
-            undefined :
-          undefined :
-        undefined :
-      undefined
-
-    const orientation = [0,0,0,1]
-
-    this.store.dispatch({
-      type : CHANGE_NAVIGATION,
-      navigation : Object.assign({},
-        {
-          perspectiveZoom,
-          perspectiveOrientation,
-          zoom,
-          position,
-          orientation
-        },{
-          positionReal : false,
-          animation : {
-
-          }
-        })
-    })
-  }
 }
 
 export const identifySrcElement = (element:HTMLElement) => {
@@ -1035,11 +1067,6 @@ export const takeOnePipe = [
   take(1)
 ]
 
-export const CM_THRESHOLD = `0.05`
-export const CM_MATLAB_JET = `float r;if( x < 0.7 ){r = 4.0 * x - 1.5;} else {r = -4.0 * x + 4.5;}float g;if (x < 0.5) {g = 4.0 * x - 0.5;} else {g = -4.0 * x + 3.5;}float b;if (x < 0.3) {b = 4.0 * x + 0.5;} else {b = -4.0 * x + 2.5;}float a = 1.0;`
-export const getActiveColorMapFragmentMain = ():string=>`void main(){float x = toNormalized(getDataValue());${CM_MATLAB_JET}if(x>${CM_THRESHOLD}){emitRGB(vec3(r,g,b));}else{emitTransparent();}}`
-
-
 export const singleLmUnchanged = (lm:{id:string,position:[number,number,number]}, map: Map<string,[number,number,number]>) => 
   map.has(lm.id) && map.get(lm.id).every((value,idx) => value === lm.position[idx])
 
@@ -1049,4 +1076,8 @@ export const userLmUnchanged = (oldlms, newlms) => {
 
   return oldlms.every(lm => singleLmUnchanged(lm, newmap as Map<string,[number,number,number]>))
     && newlms.every(lm => singleLmUnchanged(lm, oldmap as Map<string, [number,number,number]>))
-}
\ No newline at end of file
+}
+
+export const calculateSliceZoomFactor = (originalZoom) => originalZoom
+  ? 700 * originalZoom / Math.min(window.innerHeight, window.innerWidth)
+  : 1e7
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/nehubaContainer.style.css b/src/ui/nehubaContainer/nehubaContainer.style.css
index 2e3528179021066ffa9ea544fc427164394836d1..301c664de0d831b6e848e7f5af27422c27326749 100644
--- a/src/ui/nehubaContainer/nehubaContainer.style.css
+++ b/src/ui/nehubaContainer/nehubaContainer.style.css
@@ -5,10 +5,6 @@
   position:relative;
 }
 
-div[textContainer]
-{
-  padding-left:1em;
-}
 
 input[navigateInput]
 {
@@ -20,36 +16,6 @@ input[navigateInput]
 }
 
 
-:host-context([darktheme=false]) input[navigateInput]
-{
-  border:rgba(0,0,0,0.2) solid 1px;
-}
-
-:host-context([darktheme=true]) input[navigateInput]
-{
-  border:rgba(255,255,255,0.2) solid 1px;
-}
-
-div[statusCard]
-{
-  position:absolute;
-  left:1em;
-  bottom:1em;
-  width : 20em;
-  pointer-events: all;
-}
-
-:host-context([darktheme=false]) div[statusCard]
-{
-  background-color:rgba(230,230,230,0.8);
-}
-
-:host-context([darktheme=true]) div[statusCard]
-{
-  background-color:rgba(20,20,20,0.8);
-  color : rgba(250,250,250,0.8);
-}
-
 div[landmarkMasterContainer]
 {
   background:none;
@@ -85,10 +51,6 @@ div[landmarkMasterContainer] > div > [landmarkContainer] > div
   margin-top:-1em;
 }
 
-small[onHoverSegment]
-{
-  margin-left:2em;
-}
 
 [citationContainer]
 {
@@ -103,11 +65,6 @@ hr
   margin : 0.2em 2em;
 }
 
-div[linksContainer]
-{
-  display:inline-block;
-  margin-left:1em;
-}
 
 div[landmarkMasterContainer] > div > [landmarkContainer] > div.loadingIndicator
 {
@@ -141,13 +98,15 @@ div.loadingIndicator div.spinnerAnimationCircle
 div[mobileObliqueCtrl]
 {
   font-size: 200%;
-  margin-top:-2.5rem;
-  margin-left:-2.5rem;
-  padding-left: 1rem;
-  padding-top: 1rem;
   position: absolute;
   top: 50%;
   left: 50%;
+  width: 0;
+  height: 0;
+
+  display: flex;
+  align-items: center;
+  justify-content: center;
   pointer-events: all;
 }
 
@@ -163,12 +122,47 @@ div[mobileObliqueScreen]
   pointer-events: all;
 }
 
-div[mobileObliqueGuide]
+div.base
 {
   position : absolute;
-  top: 40%;
-  width: 100%;
+  top: 50%;
+  left: 50%;
+  width: 0;
+  height: 0;
   display:flex;
-  flex-direction: column;
+  flex-direction: column-reverse;
   align-items: center;
 }
+
+div[delta]
+{
+  white-space: nowrap
+}
+
+div[mobileObliqueGuide]
+{
+  background-color: rgba(250,250,250,0.8);
+}
+
+div[mobileObliqueGuide] > *
+{
+  white-space: nowrap;
+}
+
+:host-context([darktheme="true"]) div[mobileObliqueGuide]
+{
+
+  background-color: rgba(50,50,50,0.8);
+  color: white;
+}
+
+div#scratch-pad
+{
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  pointer-events: none;
+}
+
diff --git a/src/ui/nehubaContainer/nehubaContainer.template.html b/src/ui/nehubaContainer/nehubaContainer.template.html
index 2d484c3c77e5e5eefdbd75f0d5a66546225ac77c..4356da032ab0fb41b1761d4ac770ed3cbfb31e4a 100644
--- a/src/ui/nehubaContainer/nehubaContainer.template.html
+++ b/src/ui/nehubaContainer/nehubaContainer.template.html
@@ -1,83 +1,66 @@
 <ng-template #container>
 </ng-template>
 
-<ui-splashscreen *ngIf = "!viewerLoaded">
+<ui-splashscreen (contextmenu)="$event.stopPropagation();" *ngIf="!viewerLoaded">
 </ui-splashscreen>
 
 <div landmarkMasterContainer>
 
   <div>
-    <layout-floating-container
-      pos00 
-      landmarkContainer>
-      <nehuba-2dlandmark-unit 
-        *ngFor = "let spatialData of (selectedPtLandmarks$ | async)"
-        (mouseenter) = "handleMouseEnterLandmark(spatialData)"
-        (mouseleave) = "handleMouseLeaveLandmark(spatialData)"
-        [highlight] = "spatialData.highlight ? spatialData.highlight : false"
-        [glyphiconClass] = "spatialData.type === 'userLandmark' ? 'glyphicon-chevron-down' : 'glyphicon-map-marker'"
-        [positionX] = "getPositionX(0,spatialData)"
-        [positionY] = "getPositionY(0,spatialData)"
-        [positionZ] = "getPositionZ(0,spatialData)">
+    <layout-floating-container pos00 landmarkContainer>
+      <nehuba-2dlandmark-unit *ngFor="let spatialData of (selectedPtLandmarks$ | async)"
+        (mouseenter)="handleMouseEnterLandmark(spatialData)" (mouseleave)="handleMouseLeaveLandmark(spatialData)"
+        [highlight]="spatialData.highlight ? spatialData.highlight : false"
+        [fasClass]="spatialData.type === 'userLandmark' ? 'fa-chevron-down' : 'fa-map-marker'"
+        [positionX]="getPositionX(0,spatialData)" [positionY]="getPositionY(0,spatialData)"
+        [positionZ]="getPositionZ(0,spatialData)">
       </nehuba-2dlandmark-unit>
 
-      <div *ngIf = "sliceViewLoading0$ | async" class = "loadingIndicator">
-        <div class = "spinnerAnimationCircle">
+      <div *ngIf="sliceViewLoading0$ | async" class="loadingIndicator">
+        <div class="spinnerAnimationCircle">
 
         </div>
       </div>
     </layout-floating-container>
   </div>
   <div>
-    <layout-floating-container  
-      pos01 
-      landmarkContainer>
-      <nehuba-2dlandmark-unit 
-        *ngFor = "let spatialData of (selectedPtLandmarks$ | async)"
-        (mouseenter) = "handleMouseEnterLandmark(spatialData)"
-        (mouseleave) = "handleMouseLeaveLandmark(spatialData)"
-        [highlight] = "spatialData.highlight ? spatialData.highlight : false"
-        [glyphiconClass] = "spatialData.type === 'userLandmark' ? 'glyphicon-chevron-down' : 'glyphicon-map-marker'"
-        [positionX] = "getPositionX(1,spatialData)"
-        [positionY] = "getPositionY(1,spatialData)"
-        [positionZ] = "getPositionZ(1,spatialData)">
+    <layout-floating-container pos01 landmarkContainer>
+      <nehuba-2dlandmark-unit *ngFor="let spatialData of (selectedPtLandmarks$ | async)"
+        (mouseenter)="handleMouseEnterLandmark(spatialData)" (mouseleave)="handleMouseLeaveLandmark(spatialData)"
+        [highlight]="spatialData.highlight ? spatialData.highlight : false"
+        [fasClass]="spatialData.type === 'userLandmark' ? 'fa-chevron-down' : 'fa-map-marker'"
+        [positionX]="getPositionX(1,spatialData)" [positionY]="getPositionY(1,spatialData)"
+        [positionZ]="getPositionZ(1,spatialData)">
       </nehuba-2dlandmark-unit>
-      
-      <div *ngIf = "sliceViewLoading1$ | async" class = "loadingIndicator">
-        <div class = "spinnerAnimationCircle">
+
+      <div *ngIf="sliceViewLoading1$ | async" class="loadingIndicator">
+        <div class="spinnerAnimationCircle">
 
         </div>
       </div>
     </layout-floating-container>
   </div>
   <div>
-    <layout-floating-container  
-      pos10 
-      landmarkContainer>
-      <nehuba-2dlandmark-unit 
-        *ngFor = "let spatialData of (selectedPtLandmarks$ | async)"
-        (mouseenter) = "handleMouseEnterLandmark(spatialData)"
-        (mouseleave) = "handleMouseLeaveLandmark(spatialData)"
-        [highlight] = "spatialData.highlight ? spatialData.highlight : false"
-        [glyphiconClass] = "spatialData.type === 'userLandmark' ? 'glyphicon-chevron-down' : 'glyphicon-map-marker'"
-        [positionX] = "getPositionX(2,spatialData)"
-        [positionY] = "getPositionY(2,spatialData)"
-        [positionZ] = "getPositionZ(2,spatialData)">
+    <layout-floating-container pos10 landmarkContainer>
+      <nehuba-2dlandmark-unit *ngFor="let spatialData of (selectedPtLandmarks$ | async)"
+        (mouseenter)="handleMouseEnterLandmark(spatialData)" (mouseleave)="handleMouseLeaveLandmark(spatialData)"
+        [highlight]="spatialData.highlight ? spatialData.highlight : false"
+        [fasClass]="spatialData.type === 'userLandmark' ? 'fa-chevron-down' : 'fa-map-marker'"
+        [positionX]="getPositionX(2,spatialData)" [positionY]="getPositionY(2,spatialData)"
+        [positionZ]="getPositionZ(2,spatialData)">
       </nehuba-2dlandmark-unit>
-      
-      <div *ngIf = "sliceViewLoading2$ | async" class = "loadingIndicator">
-        <div class = "spinnerAnimationCircle">
+
+      <div *ngIf="sliceViewLoading2$ | async" class="loadingIndicator">
+        <div class="spinnerAnimationCircle">
 
         </div>
       </div>
     </layout-floating-container>
   </div>
   <div>
-    <layout-floating-container  
-      pos11 
-      landmarkContainer>
-      <div *ngIf = "perspectiveViewLoading$ | async" class = "loadingIndicator">
-        <div class = "spinnerAnimationCircle"></div>
+    <layout-floating-container pos11 landmarkContainer>
+      <div *ngIf="perspectiveViewLoading$ | async" class="loadingIndicator">
+        <div class="spinnerAnimationCircle"></div>
         <div perspectiveLoadingText>
           {{ perspectiveViewLoading$ | async }}
         </div>
@@ -86,60 +69,35 @@
   </div>
 </div>
 
-<layout-floating-container *ngIf = "viewerLoaded">
+<layout-floating-container *ngIf="viewerLoaded">
+  <!-- StatusCard container-->
+  <ui-status-card [selectedTemplate]="selectedTemplate" [isMobile]="isMobile"
+    [onHoverSegmentName]="onHoverSegmentName$ | async" [nehubaViewer]="nehubaViewer">
+  </ui-status-card>
+</layout-floating-container>
 
-  <!-- TODO export status card to its own container -->
-  <div statusCard>
+<div id="scratch-pad">
 
-    <hr *ngIf = "showCitation && !isMobile" />
+</div>
 
-    <div linksContainer>
-      <a href = "#" (click)="$event.preventDefault();statusPanelRealSpace = !statusPanelRealSpace">
-        {{statusPanelRealSpace ? 'real space' : 'voxel space'}}
-      </a>
-  
-      <a href = "#" (click) = "$event.preventDefault();resetNavigation()">
-        reset navigation
-      </a>
-    </div>
-  
-    <br />
-    <div textContainer>
-      <small>Navigation: </small>
-        <input 
-          (keydown.enter) = "textNavigateTo(navigateInput.value)"
-          (keydown.tab) = "textNavigateTo(navigateInput.value)"
-          [ngModel] = "navigationValue()" 
-          spellcheck = "false"
-          #navigateInput
-          navigateInput/>
-    
-      <br />
-      <small *ngIf = "!isMobile">Mouse: </small>
-      <small *ngIf = "!isMobile">
-        {{ mouseCoord }}
-      </small> 
-      <br *ngIf = "!isMobile" />
-      <small onHoverSegment>
-        {{ onHoverSegmentName$ | async }}
-      </small>
+<mobile-overlay *ngIf="isMobile && viewerLoaded" [tunableProperties]="tunableMobileProperties"
+  (deltaValue)="handleMobileOverlayEvent($event)">
+  <div class="base" delta>
+    <div mobileObliqueGuide class="p-2 mb-4 shadow">
+      {{ selectedProp }}
     </div>
   </div>
-</layout-floating-container>
-
-<mobile-overlay 
-  *ngIf = "isMobile && viewerLoaded" 
-  [tunableProperties] = "tunableMobileProperties" 
-  (deltaValue) = "handleMobileOverlayEvent($event)">
-  <div mobileObliqueGuide guide>
-    <div>
-      <i class="glyphicon glyphicon-resize-vertical"></i> oblique mode 
-    </div>
-    <div>
-      <i class="glyphicon glyphicon-resize-horizontal"></i> rotate slice
+  <div class="base" guide>
+    <div mobileObliqueGuide class="p-2 mb-4 shadow">
+      <div>
+        <i class="fas fa-arrows-alt-v"></i> oblique mode
+      </div>
+      <div>
+        <i class="fas fa-arrows-alt-h"></i> rotate slice
+      </div>
     </div>
   </div>
-  <div mobileObliqueCtrl initiator>
-    <i class="glyphicon glyphicon-globe"></i>
+  <div (contextmenu)="$event.stopPropagation(); $event.preventDefaul();" mobileObliqueCtrl initiator>
+    <i class="fas fa-globe"></i>
   </div>
-</mobile-overlay>
+</mobile-overlay>
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/nehubaViewer/nehubaViewer.component.ts b/src/ui/nehubaContainer/nehubaViewer/nehubaViewer.component.ts
index 3d46a0226dab5d2ea445651da46bb03c4d5ceaec..b0dbdd4c142815150892d2c7117fedc39287eded 100644
--- a/src/ui/nehubaContainer/nehubaViewer/nehubaViewer.component.ts
+++ b/src/ui/nehubaContainer/nehubaViewer/nehubaViewer.component.ts
@@ -6,7 +6,13 @@ import { AtlasWorkerService } from "../../../atlasViewer/atlasViewer.workerServi
 import { buffer, map, filter, debounceTime, take, takeUntil, scan, switchMap, takeWhile } from "rxjs/operators";
 import { AtlasViewerConstantsServices } from "../../../atlasViewer/atlasViewer.constantService.service";
 import { takeOnePipe, identifySrcElement } from "../nehubaContainer.component";
+import { ViewerConfiguration } from "src/services/state/viewerConfig.store";
+import { pipeFromArray } from "rxjs/internal/util/pipe";
+import { getNgIdLabelIndexFromId } from "src/services/stateStore.service";
 
+/**
+ * no selector is needed, as currently, nehubaviewer is created dynamically
+ */
 @Component({
   templateUrl : './nehubaViewer.template.html',
   styleUrls : [
@@ -16,10 +22,19 @@ import { takeOnePipe, identifySrcElement } from "../nehubaContainer.component";
 
 export class NehubaViewerUnit implements OnDestroy{
   
+  @Output() nehubaReady: EventEmitter<null> = new EventEmitter()
   @Output() debouncedViewerPositionChange : EventEmitter<any> = new EventEmitter()
-  @Output() mouseoverSegmentEmitter : EventEmitter<any | number | null> = new EventEmitter()
+  @Output() mouseoverSegmentEmitter: 
+    EventEmitter<{
+      segmentId: number | null,
+      segment:string | null,
+      layer:{
+        name?: string,
+        url?: string
+      }
+    }> = new EventEmitter()
   @Output() mouseoverLandmarkEmitter : EventEmitter<number | null> = new EventEmitter()
-  @Output() regionSelectionEmitter : EventEmitter<any> = new EventEmitter()
+  @Output() regionSelectionEmitter : EventEmitter<{segment:number, layer:{name?: string, url?: string}}> = new EventEmitter()
   @Output() errorEmitter : EventEmitter<any> = new EventEmitter()
 
   /* only used to set initial navigation state */
@@ -44,6 +59,7 @@ export class NehubaViewerUnit implements OnDestroy{
   _s6$ : any
   _s7$ : any
   _s8$ : any
+  _s9$ : any
 
   _s$ : any[] = [
     this._s1$,
@@ -53,7 +69,8 @@ export class NehubaViewerUnit implements OnDestroy{
     this._s5$,
     this._s6$,
     this._s7$,
-    this._s8$
+    this._s8$,
+    this._s9$
   ]
 
   ondestroySubscriptions: any[] = []
@@ -80,9 +97,12 @@ export class NehubaViewerUnit implements OnDestroy{
 
     this.constantService.loadExportNehubaPromise
       .then(() => {
-        const { sliceZoom, sliceViewportWidth, sliceViewportHeight } = this.config.layout.useNehubaPerspective.fixedZoomPerspectiveSlices
-        const dim = Math.min(sliceZoom * sliceViewportWidth, sliceZoom * sliceViewportHeight)
-        this._dim = [dim, dim, dim]
+        const fixedZoomPerspectiveSlices = this.config && this.config.layout && this.config.layout.useNehubaPerspective && this.config.layout.useNehubaPerspective.fixedZoomPerspectiveSlices
+        if (fixedZoomPerspectiveSlices) {
+          const { sliceZoom, sliceViewportWidth, sliceViewportHeight } = fixedZoomPerspectiveSlices
+          const dim = Math.min(sliceZoom * sliceViewportWidth, sliceZoom * sliceViewportHeight)
+          this._dim = [dim, dim, dim]
+        }
         this.patchNG()
         this.loadNehuba()
       })
@@ -141,16 +161,25 @@ export class NehubaViewerUnit implements OnDestroy{
             /* worker responded with not assembled landmark, no need to act */
             return false
           }
-          if(!message.data.url){
-            /* file url needs to be defined */
-            return false
-          }
+          /**
+           * nb url may be undefined
+           * if undefined, user have removed all user landmarks, and all that needs to be done
+           * is remove the user landmark layer
+           * 
+           * message.data.url
+           */
+
           return true
         }),
         debounceTime(100),
         map(e => e.data.url)
       ).subscribe(url => {
-        this.removeSpatialSearch3DLandmarks()
+        this.removeuserLandmarks()
+
+        /**
+         * url may be null if user removes all landmarks
+         */
+        if (!url) return
         const _ = {}
         _[this.constantService.ngUserLandmarkLayerName] = {
           type :'mesh',
@@ -208,9 +237,11 @@ export class NehubaViewerUnit implements OnDestroy{
           }
   
           /* if the active parcellation is the current parcellation, load the said mesh */
-          if(baseUrl === this._baseUrl){
-            this.nehubaViewer.setMeshesToLoad([...this.workerService.safeMeshSet.get(this._baseUrl)], {
-              name : this.parcellationId
+          const baseUrlIsInLoadedBaseUrlList = new Set([...this._baseUrls]).has(baseUrl)
+          const baseUrlParcellationId = this._baseUrlToParcellationIdMap.get(baseUrl)
+          if( baseUrlIsInLoadedBaseUrlList && baseUrlParcellationId){
+            this.nehubaViewer.setMeshesToLoad([...this.workerService.safeMeshSet.get(baseUrl)], {
+              name : baseUrlParcellationId
             })
           }
         })
@@ -218,15 +249,26 @@ export class NehubaViewerUnit implements OnDestroy{
     )
   }
 
-  private _baseUrl : string 
+  private _baseUrlToParcellationIdMap:Map<string, string> = new Map()
+  private _baseUrls: string[] = []
+
   get numMeshesToBeLoaded():number{
-    if(!this._baseUrl)
+    if(!this._baseUrls || this._baseUrls.length === 0)
       return 0
 
-    const set = this.workerService.safeMeshSet.get(this._baseUrl)
-    return set
-      ? set.size
-      : 0
+    return this._baseUrls.reduce((acc, curr) => {
+      const mappedSet = this.workerService.safeMeshSet.get(curr)
+      return acc + ((mappedSet && mappedSet.size) || 0)
+    } ,0)
+  }
+
+  public applyPerformanceConfig ({ gpuLimit }: Partial<ViewerConfiguration>) {
+    if (gpuLimit && this.nehubaViewer) {
+      const limit = this.nehubaViewer.ngviewer.state.children.get('gpuMemoryLimit')
+      if (limit && limit.restoreState) {
+        limit.restoreState(gpuLimit)
+      }
+    }
   }
 
   /* required to check if correct landmarks are loaded */
@@ -238,23 +280,28 @@ export class NehubaViewerUnit implements OnDestroy{
     this._templateId = id
   }
 
-  /* required to check if the correct meshes are being loaded */
-  private _parcellationId : string
-  get parcellationId(){
-    return this._parcellationId
+  /** compatible with multiple parcellation id selection */
+  private _ngIds: string[] = []
+  get ngIds(){
+    return this._ngIds
   }
-  set parcellationId(id:string){
 
-    if(this._parcellationId && this.nehubaViewer){
-      const oldlayer = this.nehubaViewer.ngviewer.layerManager.getLayerByName(this._parcellationId)
-      if(oldlayer)oldlayer.setVisible(false)
-      else console.warn('could not find old layer',this.parcellationId)
+  set ngIds(val:string[]){
+
+    if(this.nehubaViewer){
+      this._ngIds.forEach(id => {
+        const oldlayer = this.nehubaViewer.ngviewer.layerManager.getLayerByName(id)
+        if(oldlayer)oldlayer.setVisible(false)
+        else console.warn('could not find old layer', id)
+      })
     }
 
-    this._parcellationId = id
+    this._ngIds = val
     
-    if(this.nehubaViewer)
+    if(this.nehubaViewer){
       this.loadNewParcellation()
+      this.showAllSeg()
+    }
   }
 
   spatialLandmarkSelectionChanged(labels:number[]){
@@ -278,7 +325,7 @@ export class NehubaViewerUnit implements OnDestroy{
     }
   }
 
-  regionsLabelIndexMap : Map<number,any>
+  multiNgIdsLabelIndexMap: Map<string, Map<number, any>>
 
   navPosReal : [number,number,number] = [0,0,0]
   navPosVoxel : [number,number,number] = [0,0,0]
@@ -288,8 +335,9 @@ export class NehubaViewerUnit implements OnDestroy{
 
   viewerState : ViewerState
 
-  private defaultColormap : Map<number,{red:number,green:number,blue:number}>
-  public mouseOverSegment : number | null
+  private multiNgIdColorMap: Map<string, Map<number, {red: number, green:number, blue: number}>>
+  public mouseOverSegment: number | null
+  public mouseOverLayer: {name:string,url:string}| null
 
   private viewportToDatas : [any, any, any] = [null, null, null]
 
@@ -300,17 +348,26 @@ export class NehubaViewerUnit implements OnDestroy{
   loadNehuba(){
     this.nehubaViewer = window['export_nehuba'].createNehubaViewer(this.config, (err)=>{
       /* print in debug mode */
+      console.log(err)
     })
 
-    if(this.regionsLabelIndexMap){
-      const managedLayers = this.nehubaViewer.ngviewer.layerManager.managedLayers
-      managedLayers.slice(1).forEach(layer=>layer.setVisible(false))
-      this.nehubaViewer.redraw()
-    }
+    /**
+     * Hide all layers except the base layer (template)
+     * Then show the layers referenced in multiNgIdLabelIndexMap
+     */
+    const managedLayers = this.nehubaViewer.ngviewer.layerManager.managedLayers
+    managedLayers.slice(1).forEach(layer=>layer.setVisible(false))
+    Array.from(this.multiNgIdsLabelIndexMap.keys()).forEach(ngId => {
+      const layer = this.nehubaViewer.ngviewer.layerManager.getLayerByName(ngId)
+      if (layer) layer.setVisible(true)
+      else console.log('layer unavailable', ngId)
+    })
+    this.nehubaViewer.redraw()
 
     /* creation of the layout is done on next frame, hence the settimeout */
     setTimeout(() => {
       window['viewer'].display.panels.forEach(patchSliceViewPanel) 
+      this.nehubaReady.emit(null)
     })
     
     this.newViewerInit()
@@ -321,9 +378,13 @@ export class NehubaViewerUnit implements OnDestroy{
     this.onDestroyCb.push(() => window['nehubaViewer'] = null)
 
     this.ondestroySubscriptions.push(
-      fromEvent(this.elementRef.nativeElement, 'viewportToData').pipe(
-        ...takeOnePipe
-      ).subscribe((events:CustomEvent[]) => {
+      // fromEvent(this.elementRef.nativeElement, 'viewportToData').pipe(
+      //   ...takeOnePipe
+      // ).subscribe((events:CustomEvent[]) => {
+      //   [0,1,2].forEach(idx => this.viewportToDatas[idx] = events[idx].detail.viewportToData)
+      // })
+      pipeFromArray([...takeOnePipe])(fromEvent(this.elementRef.nativeElement, 'viewportToData'))
+      .subscribe((events:CustomEvent[]) => {
         [0,1,2].forEach(idx => this.viewportToDatas[idx] = events[idx].detail.viewportToData)
       })
     )
@@ -440,9 +501,15 @@ export class NehubaViewerUnit implements OnDestroy{
     
     /* TODO find a more permanent fix to disable double click */
     LayerManager.prototype.invokeAction = (arg) => {
-      const region = this.regionsLabelIndexMap.get(this.mouseOverSegment)
-      if (arg === 'select' && region) {
-        this.regionSelectionEmitter.emit(region)
+
+      /**
+       * The emitted value does not affect the region selection
+       * the region selection is taken care of in nehubaContainer
+       */
+      const map = this.multiNgIdsLabelIndexMap.get(this.mouseOverLayer.name)
+      const region = map && map.get(this.mouseOverSegment)
+      if (arg === 'select') {
+        this.regionSelectionEmitter.emit({ segment: region, layer: this.mouseOverLayer })
       }
     }
 
@@ -450,23 +517,39 @@ export class NehubaViewerUnit implements OnDestroy{
   }
 
   private filterLayers(l:any,layerObj:any):boolean{
-    return Object.keys(layerObj).length == 0 && layerObj.constructor == Object ?
-      true :
-      Object.keys(layerObj).every(key=>
-        !(<Object>l).hasOwnProperty(key) && !l[key] ? 
-          false :
-          layerObj[key] instanceof RegExp ?
-            layerObj[key].test(l[key]) :
-            layerObj[key] == l[key])
+    /**
+     * if selector is an empty object, select all layers
+     */
+    return layerObj instanceof Object && Object.keys(layerObj).every(key => 
+      /**
+       * the property described by the selector must exist and ...
+       */
+      !!l[key] && 
+        /**
+         * if the selector is regex, test layer property
+         */
+        ( layerObj[key] instanceof RegExp
+          ? layerObj[key].test(l[key])
+          /**
+           * if selector is string, test for strict equality
+           */
+          : typeof layerObj[key] === 'string'
+            ? layerObj[key] === l[key]
+            /**
+             * otherwise do not filter
+             */
+            : false 
+        )
+      )
   }
 
   // TODO single landmark for user landmark
-  public addUserLandmarks(landmarks:any[]){
+  public updateUserLandmarks(landmarks:any[]){
     if(!this.nehubaViewer)
       return
     this.workerService.worker.postMessage({
       type : 'GET_USERLANDMARKS_VTK',
-      scale: Math.min(...this.dim.map(v => v * 2e-9)),
+      scale: Math.min(...this.dim.map(v => v * this.constantService.nehubaLandmarkConstant)),
       landmarks : landmarks.map(lm => lm.position.map(coord => coord * 1e6))
     })
   }
@@ -536,35 +619,78 @@ export class NehubaViewerUnit implements OnDestroy{
 
   public hideAllSeg(){
     if(!this.nehubaViewer) return
-    Array.from(this.regionsLabelIndexMap.keys()).forEach(idx=>
-      this.nehubaViewer.hideSegment(idx,{
-        name : this.parcellationId
-      }))
-    this.nehubaViewer.showSegment(0,{
-      name : this.parcellationId
+    Array.from(this.multiNgIdsLabelIndexMap.keys()).forEach(ngId => {
+      
+      Array.from(this.multiNgIdsLabelIndexMap.get(ngId).keys()).forEach(idx => {
+        this.nehubaViewer.hideSegment(idx, {
+          name: ngId
+        })
+      })
+      this.nehubaViewer.showSegment(0, {
+        name: ngId
+      })
     })
   }
 
   public showAllSeg(){
     if(!this.nehubaViewer) return
     this.hideAllSeg()
-    this.nehubaViewer.hideSegment(0,{
-      name : this.parcellationId
+    Array.from(this.multiNgIdsLabelIndexMap.keys()).forEach(ngId => {
+      this.nehubaViewer.hideSegment(0,{
+        name: ngId
+      })
     })
   }
 
-  public showSegs(array:number[]){
+  public showSegs(array: number[] | string[]){
+
     if(!this.nehubaViewer) return
+
     this.hideAllSeg()
 
-    this.nehubaViewer.hideSegment(0,{
-      name : this.parcellationId
-    })
+    
+    if (array.length === 0) return
 
-    array.forEach(idx=>
-      this.nehubaViewer.showSegment(idx,{
-        name : this.parcellationId
-      }))
+    /**
+     * TODO tobe deprecated
+     */
+
+    if (typeof array[0] === 'number') {
+      console.warn(`show seg with number indices has been deprecated`)
+      return
+    } 
+
+    const reduceFn:(acc:Map<string,number[]>,curr:string)=>Map<string,number[]> = (acc, curr) => {
+
+      const newMap = new Map(acc)
+      const { ngId, labelIndex } = getNgIdLabelIndexFromId({ labelIndexId: curr })
+      const exist = newMap.get(ngId)
+      if (!exist) newMap.set(ngId, [Number(labelIndex)])
+      else newMap.set(ngId, [...exist, Number(labelIndex)])
+      return newMap
+    }
+    
+    /**
+     * TODO 
+     * AAAAAAARG TYPESCRIPT WHY YOU SO MAD
+     */
+    //@ts-ignore
+    const newMap:Map<string, number[]> = array.reduce(reduceFn, new Map())
+    
+    /**
+     * TODO
+     * ugh, ugly code. cleanify
+     */
+    newMap.forEach((segs, ngId) => {
+      this.nehubaViewer.hideSegment(0, {
+        name: ngId
+      })
+      segs.forEach(seg => {
+        this.nehubaViewer.showSegment(seg, {
+          name: ngId
+        })
+      })
+    })
   }
 
   private vec3(pos:[number,number,number]){
@@ -612,17 +738,22 @@ export class NehubaViewerUnit implements OnDestroy{
     this.nehubaViewer.ngviewer.navigationState.pose.rotateRelative(this.vec3([0, 0, 1]), amount / 4.0 * Math.PI / 180.0)
   }
 
-  private updateColorMap(arrayIdx:number[]){
+  /**
+   * 
+   * @param arrayIdx label indices of the shown segment(s)
+   * @param ngId segmentation layer name
+   */
+  private updateColorMap(arrayIdx:number[], ngId: string){
     const set = new Set(arrayIdx)
     const newColorMap = new Map(
-      Array.from(this.defaultColormap.entries())
+      Array.from(this.multiNgIdColorMap.get(ngId).entries())
         .map(v=> set.has(v[0]) || set.size === 0 ? 
           v :
           [v[0],{red:255,green:255,blue:255}]) as any
     )
 
     this.nehubaViewer.batchAddAndUpdateSegmentColors(newColorMap,{
-      name:this.parcellationId
+      name: ngId
     })
   }
 
@@ -631,7 +762,10 @@ export class NehubaViewerUnit implements OnDestroy{
     /* isn't this layer specific? */
     /* TODO this is layer specific. need a way to distinguish between different segmentation layers */
     this._s2$ = this.nehubaViewer.mouseOver.segment
-      .subscribe(obj=>this.mouseOverSegment = obj.segment)
+      .subscribe(({ segment, layer })=>{
+        this.mouseOverSegment = segment
+        this.mouseOverLayer = { ...layer }
+      })
 
     if(this.initNav){
       this.setNavigationState(this.initNav)
@@ -647,12 +781,23 @@ export class NehubaViewerUnit implements OnDestroy{
       this.hideAllSeg()
     }
 
-    this._s8$ = this.nehubaViewer.mouseOver.segment.subscribe(({segment, ...rest})=>{
-      if( segment && segment < 65500 ) {
-        const region = this.regionsLabelIndexMap.get(segment)
-        this.mouseoverSegmentEmitter.emit(region ? region : segment)
+    this._s8$ = this.nehubaViewer.mouseOver.segment.subscribe(({segment: segmentId, layer, ...rest})=>{
+      
+      const {name = 'unnamed'} = layer
+      if( segmentId && segmentId < 65500 ) {
+        const map = this.multiNgIdsLabelIndexMap.get(name)
+        const region = map && map.get(segmentId)
+        this.mouseoverSegmentEmitter.emit({
+          layer,
+          segment: region,
+          segmentId
+        })
       }else{
-        this.mouseoverSegmentEmitter.emit(null)
+        this.mouseoverSegmentEmitter.emit({
+          layer,
+          segment: null,
+          segmentId
+        })
       }
     })
 
@@ -724,72 +869,97 @@ export class NehubaViewerUnit implements OnDestroy{
   private loadNewParcellation(){
 
     /* show correct segmentation layer */
-
-    const newlayer = this.nehubaViewer.ngviewer.layerManager.getLayerByName(this.parcellationId)
-    if(newlayer)newlayer.setVisible(true)
-    else console.warn('could not find new layer',this.parcellationId)
-
-    const regex = /^(\S.*?)\:\/\/(.*?)$/.exec(newlayer.sourceUrl)
-    
-    if(!regex || !regex[2]){
-      console.error('could not parse baseUrl')
-      return
-    }
-    if(regex[1] !== 'precomputed'){
-      console.error('sourceUrl is not precomputed')
-      return
-    }
-    
-    const baseUrl = regex[2]
-    this._baseUrl = baseUrl
-
-    /* load meshes */
-    /* TODO could be a bug where user loads new parcellation, but before the worker could completely populate the list */
-    const set = this.workerService.safeMeshSet.get(baseUrl)
-    if(set){
-      this.nehubaViewer.setMeshesToLoad([...set],{
-        name : this.parcellationId
-      })
-    }else{
-      if(newlayer){
-        this.zone.runOutsideAngular(() => 
-          this.workerService.worker.postMessage({
-            type : 'CHECK_MESHES',
-            parcellationId : this.parcellationId,
-            baseUrl,
-            indices : [
-              ...Array.from(this.regionsLabelIndexMap.keys()),
-              ...getAuxilliaryLabelIndices()
-            ]
-          })
-        )
+    this._baseUrls = []
+
+    this.ngIds.map(id => {
+      const newlayer = this.nehubaViewer.ngviewer.layerManager.getLayerByName(id)
+      if(newlayer)newlayer.setVisible(true)
+      else console.warn('could not find new layer',id)
+
+      const regex = /^(\S.*?)\:\/\/(.*?)$/.exec(newlayer.sourceUrl)
+      
+      if(!regex || !regex[2]){
+        console.error('could not parse baseUrl')
+        return
       }
-    }
+      if(regex[1] !== 'precomputed'){
+        console.error('sourceUrl is not precomputed')
+        return
+      }
+      
+      const baseUrl = regex[2]
+      this._baseUrls.push(baseUrl)
+      this._baseUrlToParcellationIdMap.set(baseUrl, id)
+
+      /* load meshes */
+      /* TODO could be a bug where user loads new parcellation, but before the worker could completely populate the list */
+      const set = this.workerService.safeMeshSet.get(baseUrl)
+      if(set){
+        this.nehubaViewer.setMeshesToLoad([...set],{
+          name : id
+        })
+      }else{
+        if(newlayer){
+          this.zone.runOutsideAngular(() => 
+            this.workerService.worker.postMessage({
+              type : 'CHECK_MESHES',
+              parcellationId : id,
+              baseUrl,
+              indices : [
+                ...Array.from(this.multiNgIdsLabelIndexMap.get(id).keys()),
+                ...getAuxilliaryLabelIndices()
+              ]
+            })
+          )
+        }
+      }
+    })
 
-    this.defaultColormap = new Map(
-      Array.from(
-        [
-          ...this.regionsLabelIndexMap.entries(),
-          ...getAuxilliaryLabelIndices().map(i=>([
-              i, {}
-            ]))
-          
-        ]
-      ).map((val:[number,any])=>([val[0],this.getRgb(val[0],val[1].rgb)])) as any)
+    const obj = Array.from(this.multiNgIdsLabelIndexMap.keys()).map(ngId => {
+      return [
+        ngId, 
+        new Map(Array.from(
+          [
+            ...this.multiNgIdsLabelIndexMap.get(ngId).entries(),
+            ...getAuxilliaryLabelIndices().map(i => {
+              return [i, {}]
+            })
+          ]
+        ).map((val:[number,any])=>([val[0],this.getRgb(val[0],val[1].rgb)])) as any)
+      ]
+    }) as [string, Map<number, {red:number, green: number, blue: number}>][]
+
+    this.multiNgIdColorMap = new Map(obj)
 
     /* load colour maps */
-    this.nehubaViewer.batchAddAndUpdateSegmentColors(
-      this.defaultColormap,
-      { name : this.parcellationId })
+
+    Array.from(this.multiNgIdColorMap.entries()).forEach(([ngId, map]) => {
+
+      this.nehubaViewer.batchAddAndUpdateSegmentColors(
+        map,
+        { name : ngId })
+    })
 
     this._s$.forEach(_s$=>{
       if(_s$) _s$.unsubscribe()
     })
 
     if(this._s1$)this._s1$.unsubscribe()
-    this._s1$ = this.nehubaViewer.getShownSegmentsObservable({
-      name : this.parcellationId
-    }).subscribe(arrayIdx=>this.updateColorMap(arrayIdx))
+    if(this._s9$)this._s9$.unsubscribe()
+
+    const arr = Array.from(this.multiNgIdsLabelIndexMap.keys()).map(ngId => {
+      return this.nehubaViewer.getShownSegmentsObservable({
+        name: ngId
+      }).subscribe(arrayIdx => this.updateColorMap(arrayIdx, ngId))
+    })
+
+    this._s9$ = {
+      unsubscribe: () => {
+        while(arr.length > 0) {
+          arr.pop().unsubscribe()
+        }
+      }
+    }
   }
 
   private getRgb(labelIndex:number,rgb?:number[]):{red:number,green:number,blue:number}{
diff --git a/src/ui/nehubaContainer/statusCard/statusCard.component.ts b/src/ui/nehubaContainer/statusCard/statusCard.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ece941ab7752d6bacd9bb8471e968b1d099c5588
--- /dev/null
+++ b/src/ui/nehubaContainer/statusCard/statusCard.component.ts
@@ -0,0 +1,113 @@
+import { Component, Input } from "@angular/core";
+import { CHANGE_NAVIGATION, ViewerStateInterface } from "src/services/stateStore.service";
+import { Store } from "@ngrx/store";
+import { NehubaViewerUnit } from "../nehubaViewer/nehubaViewer.component";
+
+@Component({
+    selector : 'ui-status-card',
+    templateUrl : './statusCard.template.html',
+    styleUrls : ['./statusCard.style.css']
+  })  
+export class StatusCardComponent{
+
+  @Input() selectedTemplate: any;
+  @Input() isMobile: boolean;
+  @Input() nehubaViewer: NehubaViewerUnit;
+  @Input() onHoverSegmentName: String;
+
+  constructor(
+    private store : Store<ViewerStateInterface>,
+  ) {}
+
+  statusPanelRealSpace : boolean = true
+
+  get mouseCoord():string{
+    return this.nehubaViewer ?
+      this.statusPanelRealSpace ? 
+        this.nehubaViewer.mousePosReal ? 
+          Array.from(this.nehubaViewer.mousePosReal.map(n=> isNaN(n) ? 0 : n/1e6))
+            .map(n=>n.toFixed(3)+'mm').join(' , ') : 
+          '0mm , 0mm , 0mm (mousePosReal not yet defined)' :
+        this.nehubaViewer.mousePosVoxel ? 
+          this.nehubaViewer.mousePosVoxel.join(' , ') :
+          '0 , 0 , 0 (mousePosVoxel not yet defined)' :
+      '0 , 0 , 0 (nehubaViewer not defined)'
+  }
+
+  editingNavState : boolean = false
+
+  textNavigateTo(string:string){
+    if(string.split(/[\s|,]+/).length>=3 && string.split(/[\s|,]+/).slice(0,3).every(entry=>!isNaN(Number(entry.replace(/mm/,''))))){
+      const pos = (string.split(/[\s|,]+/).slice(0,3).map((entry)=>Number(entry.replace(/mm/,''))*(this.statusPanelRealSpace ? 1000000 : 1)))
+      this.nehubaViewer.setNavigationState({
+        position : (pos as [number,number,number]),
+        positionReal : this.statusPanelRealSpace
+      })
+    }else{
+      console.log('input did not parse to coordinates ',string)
+    }
+  }
+
+  navigationValue(){
+    return this.nehubaViewer ? 
+      this.statusPanelRealSpace ? 
+        Array.from(this.nehubaViewer.navPosReal.map(n=> isNaN(n) ? 0 : n/1e6))
+          .map(n=>n.toFixed(3)+'mm').join(' , ') :
+        Array.from(this.nehubaViewer.navPosVoxel.map(n=> isNaN(n) ? 0 : n)).join(' , ') :
+      `[0,0,0] (neubaViewer is undefined)`
+  }
+  
+  /**
+   * TODO
+   * maybe have a nehuba manager service
+   * so that reset navigation logic can stay there
+   * 
+   * When that happens, we don't even need selectTemplate input
+   * 
+   * the info re: nehubaViewer can stay there, too
+   */
+  resetNavigation({rotation: rotationFlag = false, position: positionFlag = false, zoom : zoomFlag = false} : {rotation?: boolean, position?: boolean, zoom?: boolean}){
+    const initialNgState = this.selectedTemplate.nehubaConfig.dataset.initialNgState
+    
+    const perspectiveZoom = initialNgState ? initialNgState.perspectiveZoom : undefined
+    const perspectiveOrientation = initialNgState ? initialNgState.perspectiveOrientation : undefined
+    const zoom = (zoomFlag
+      && initialNgState
+      && initialNgState.navigation
+      && initialNgState.navigation.zoomFactor)
+      || undefined
+
+    const position = (positionFlag
+      && initialNgState
+      && initialNgState.navigation
+      && initialNgState.navigation.pose
+      && initialNgState.navigation.pose.position.voxelCoordinates
+      && initialNgState.navigation.pose.position.voxelCoordinates)
+      || undefined
+
+    const orientation = rotationFlag
+      ? [0,0,0,1]
+      : undefined
+
+    this.store.dispatch({
+      type : CHANGE_NAVIGATION,
+      navigation : {
+        ...{
+          perspectiveZoom,
+          perspectiveOrientation,
+          zoom,
+          position,
+          orientation
+        },
+        ...{
+          positionReal : false,
+          animation : {}
+        }
+      }
+    })
+  }
+}
+
+
+    
+
diff --git a/src/ui/nehubaContainer/statusCard/statusCard.style.css b/src/ui/nehubaContainer/statusCard/statusCard.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..868a99e2faf458301d36b3d9dae1a1763055eced
--- /dev/null
+++ b/src/ui/nehubaContainer/statusCard/statusCard.style.css
@@ -0,0 +1,52 @@
+div[statusCard]
+{
+  position:absolute;
+  left:1em;
+  bottom:1em;
+  width : 20em;
+  pointer-events: all;
+}
+
+input
+{
+  background-color:rgba(240, 240, 240, 0.8);
+}
+
+:host-context([darktheme=false]) div[statusCard]
+{
+  background-color:rgba(230,230,230,0.8);
+}
+
+:host-context([darktheme=true]) div[statusCard],
+:host-context([darktheme=true]) input
+{
+  background-color:rgba(20,20,20,0.8);
+  color : rgba(250,250,250,0.8);
+}
+
+div[linksContainer]
+{
+  display:inline-block;
+  margin-left:1em;
+}
+
+div[textContainer]
+{
+  padding-left:1em;
+}
+
+small[onHoverSegment]
+{
+  margin-left:2em;
+}
+
+
+:host-context([darktheme=false]) input[navigateInput]
+{
+  border:rgba(0,0,0,0.2) solid 1px;
+}
+
+:host-context([darktheme=true]) input[navigateInput]
+{
+  border:rgba(255,255,255,0.2) solid 1px;
+}
\ No newline at end of file
diff --git a/src/ui/nehubaContainer/statusCard/statusCard.template.html b/src/ui/nehubaContainer/statusCard/statusCard.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..c5e3f8541750cf7cd9dad508ef3843faf9155120
--- /dev/null
+++ b/src/ui/nehubaContainer/statusCard/statusCard.template.html
@@ -0,0 +1,57 @@
+<div statusCard>
+
+    <div linksContainer>
+      <span>
+        reset: 
+      </span>
+      <a
+        href="#"
+        (click)="$event.preventDefault();resetNavigation({position:true})">
+        position
+      </a>
+
+      <a
+        href="#"
+        (click)="$event.preventDefault();resetNavigation({rotation:true})">
+        rotation
+      </a>
+
+      <a
+        href="#"
+        (click)="$event.preventDefault();resetNavigation({zoom:true})">
+        zoom
+      </a>
+
+      <br />
+      <span>
+        space:
+      </span>
+      
+      <a href="#" (click)="$event.preventDefault();statusPanelRealSpace=!statusPanelRealSpace">
+        {{statusPanelRealSpace ? 'physical' : 'voxel'}}
+      </a>
+  
+    </div>
+  
+    <br />
+    <div textContainer>
+      <small>Navigation: </small>
+        <input 
+          (keydown.enter)="textNavigateTo(navigateInput.value)"
+          (keydown.tab)="textNavigateTo(navigateInput.value)"
+          [ngModel]="navigationValue()" 
+          spellcheck="false"
+          #navigateInput
+          navigateInput/>
+    
+      <br />
+      <small *ngIf="!isMobile">Mouse: </small>
+      <small *ngIf="!isMobile">
+        {{ mouseCoord }}
+      </small> 
+      <br *ngIf="!isMobile" />
+      <small onHoverSegment>
+        {{ onHoverSegmentName }}
+      </small>
+    </div>
+  </div>
\ No newline at end of file
diff --git a/src/ui/pluginBanner/pluginBanner.component.ts b/src/ui/pluginBanner/pluginBanner.component.ts
index cef22dab98c5acd66ce308f345da4b0eb2330356..0b4fe404a110a3d82cfdade361e894e0a2aeb92e 100644
--- a/src/ui/pluginBanner/pluginBanner.component.ts
+++ b/src/ui/pluginBanner/pluginBanner.component.ts
@@ -1,12 +1,13 @@
 import { Component } from "@angular/core";
-import { PluginServices, PluginManifest } from "../../atlasViewer/atlasViewer.pluginService.service";
+import { PluginServices, PluginManifest } from "src/atlasViewer/atlasViewer.pluginService.service";
 
 
 @Component({
   selector : 'plugin-banner',
   templateUrl : './pluginBanner.template.html',
   styleUrls : [
-    `./pluginBanner.style.css`
+    `./pluginBanner.style.css`,
+    '../../css/darkBtns.css'
   ]
 })
 
@@ -23,7 +24,7 @@ export class PluginBannerUI{
   }
 
   get pluginEnabledFlag(){
-    return PLUGINDEV || BUNDLEDPLUGINS.length > 0
+    return true || PLUGINDEV || BUNDLEDPLUGINS.length > 0
       ? true
       : false
   }
diff --git a/src/ui/pluginBanner/pluginBanner.style.css b/src/ui/pluginBanner/pluginBanner.style.css
index 208e7f758e019dd197f9b80c56ee25c3032d82d3..2bb0a1504f1dc55fb60862cb95908e844d4c7993 100644
--- a/src/ui/pluginBanner/pluginBanner.style.css
+++ b/src/ui/pluginBanner/pluginBanner.style.css
@@ -27,12 +27,6 @@
   cursor: default;
 }
 
-:host-context([darktheme="true"]) .btn-default
-{
-  background-color:rgba(80,80,80,0.8);
-  color:white;
-}
-
 .btn.btn-disabled.btn.btn-disabled.btn.btn-disabled.btn.btn-disabled
 {
   opacity:0.5;
diff --git a/src/util/pipes/filterNameBySearch.pipe.ts b/src/ui/regionHierachy/filterNameBySearch.pipe.ts
similarity index 100%
rename from src/util/pipes/filterNameBySearch.pipe.ts
rename to src/ui/regionHierachy/filterNameBySearch.pipe.ts
diff --git a/src/ui/regionHierachy/regionHierarchy.component.ts b/src/ui/regionHierachy/regionHierarchy.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c4b4a4951d83d5414602696297800046045cd5a9
--- /dev/null
+++ b/src/ui/regionHierachy/regionHierarchy.component.ts
@@ -0,0 +1,208 @@
+import { EventEmitter, Component, ElementRef, ViewChild, HostListener, OnInit, ChangeDetectionStrategy, ChangeDetectorRef, Input, Output, AfterViewInit } from "@angular/core";
+import {  Subscription, Subject, fromEvent } from "rxjs";
+import { buffer, debounceTime } from "rxjs/operators";
+import { FilterNameBySearch } from "./filterNameBySearch.pipe";
+import { generateLabelIndexId } from "src/services/stateStore.service";
+
+const insertHighlight :(name:string, searchTerm:string) => string = (name:string, searchTerm:string = '') => {
+  const regex = new RegExp(searchTerm, 'gi')
+  return searchTerm === '' ?
+    name :
+    name.replace(regex, (s) => `<span class = "highlight">${s}</span>`)
+}
+
+const getDisplayTreeNode : (searchTerm:string, selectedRegions:any[]) => (item:any) => string = (searchTerm:string = '', selectedRegions:any[] = []) => ({ ngId, name, status, labelIndex }) =>  {
+  return !!labelIndex
+    && !!ngId
+    && selectedRegions.findIndex(re =>
+      generateLabelIndexId({ labelIndex: re.labelIndex, ngId: re.ngId }) === generateLabelIndexId({ ngId, labelIndex })
+    ) >= 0
+      ? `<span class="regionSelected">${insertHighlight(name, searchTerm)}</span>` + (status ? ` <span class="text-muted">(${insertHighlight(status, searchTerm)})</span>` : ``)
+      : `<span class="regionNotSelected">${insertHighlight(name, searchTerm)}</span>` + (status ? ` <span class="text-muted">(${insertHighlight(status, searchTerm)})</span>` : ``)
+}
+
+const getFilterTreeBySearch = (pipe:FilterNameBySearch, searchTerm:string) => (node:any) => pipe.transform([node.name, node.status], searchTerm)
+
+@Component({
+  selector: 'region-hierarchy',
+  templateUrl: './regionHierarchy.template.html',
+  styleUrls: [
+    './regionHierarchy.style.css'
+  ],
+  changeDetection: ChangeDetectionStrategy.OnPush
+})
+
+export class RegionHierarchy implements OnInit, AfterViewInit{
+
+  @Input()
+  public selectedRegions: any[] = []
+
+  @Input()
+  public selectedParcellation: any
+
+  @Input() isMobile: boolean;
+
+  private _showRegionTree: boolean = false
+
+  @Output()
+  private showRegionFlagChanged: EventEmitter<boolean> = new EventEmitter()
+
+  @Output()
+  private singleClickRegion: EventEmitter<any> = new EventEmitter()
+
+  @Output()
+  private doubleClickRegion: EventEmitter<any> = new EventEmitter()
+
+  @Output()
+  private clearAllRegions: EventEmitter<null> = new EventEmitter()
+
+  public searchTerm: string = ''
+  private subscriptions: Subscription[] = []
+
+  @ViewChild('searchTermInput', {read: ElementRef})
+  private searchTermInput: ElementRef
+
+  @HostListener('document:click', ['$event'])
+  closeRegion(event: MouseEvent) {
+    const contains = this.el.nativeElement.contains(event.target)
+    this.showRegionTree = contains
+    if (!this.showRegionTree)
+      this.searchTerm = ''
+  }
+
+  get regionsLabelIndexMap() {
+    return null
+  }
+
+  constructor(
+    private cdr:ChangeDetectorRef,
+    private el:ElementRef
+  ){
+
+  }
+
+  ngOnChanges(){
+    this.aggregatedRegionTree = {
+      name: this.selectedParcellation.name,
+      children: this.selectedParcellation.regions
+    }
+    this.displayTreeNode = getDisplayTreeNode(this.searchTerm, this.selectedRegions)
+    this.filterTreeBySearch = getFilterTreeBySearch(this.filterNameBySearchPipe, this.searchTerm)
+  }
+
+  clearRegions(event:MouseEvent){
+    event.stopPropagation()
+    this.clearAllRegions.emit()
+  }
+
+  get showRegionTree(){
+    return this._showRegionTree
+  }
+
+  set showRegionTree(flag: boolean){
+    this._showRegionTree = flag
+    this.showRegionFlagChanged.emit(this._showRegionTree)
+  }
+
+  ngOnInit(){
+
+    this.subscriptions.push(
+      this.handleRegionTreeClickSubject.pipe(
+        buffer(
+          this.handleRegionTreeClickSubject.pipe(
+            debounceTime(200)
+          )
+        )
+      ).subscribe(arr => arr.length > 1 ? this.doubleClick(arr[0]) : this.singleClick(arr[0]))
+    )
+  }
+
+  ngAfterViewInit(){
+    this.subscriptions.push(
+      fromEvent(this.searchTermInput.nativeElement, 'input').pipe(
+        debounceTime(200)
+      ).subscribe(ev => {
+        this.changeSearchTerm(ev)
+      })
+    )
+  }
+
+  getInputPlaceholder(parcellation:any) {
+    if (parcellation)
+      return `Search region in ${parcellation.name}`
+    else
+      return `Start by selecting a template and a parcellation.`
+  }
+
+  escape(event:KeyboardEvent){
+    this.showRegionTree = false
+    this.searchTerm = '';
+    (event.target as HTMLInputElement).blur()
+
+  }
+
+  focusInput(event?:MouseEvent){
+    if (event) {
+      /**
+       * need to stop propagation, or @closeRegion will be triggered
+       */
+      event.stopPropagation()
+    }
+    this.searchTermInput.nativeElement.focus()
+    this.showRegionTree = true
+  }
+
+  /* NB need to bind two way data binding like this. Or else, on searchInput blur, the flat tree will be rebuilt,
+    resulting in first click to be ignored */
+
+  changeSearchTerm(event: any) {
+    if (event.target.value === this.searchTerm)
+      return
+    this.searchTerm = event.target.value
+    /**
+     * TODO maybe introduce debounce
+     */
+    this.ngOnChanges()
+    this.cdr.markForCheck()
+  }
+
+  private handleRegionTreeClickSubject: Subject<any> = new Subject()
+
+  handleClickRegion(obj: any) {
+    const {event} = obj
+    /**
+     * TODO figure out why @closeRegion gets triggered, but also, contains returns false
+     */
+    if (event)
+      event.stopPropagation()
+    this.handleRegionTreeClickSubject.next(obj)
+  }
+
+  /* single click selects/deselects region(s) */
+  private singleClick(obj: any) {
+    if (!obj)
+      return
+    const { inputItem : region } = obj
+    if (!region)
+      return
+    this.singleClickRegion.emit(region)
+  }
+
+  /* double click navigate to the interested area */
+  private doubleClick(obj: any) {
+    if (!obj)
+      return
+    const { inputItem : region } = obj
+    if (!region)
+      return
+    this.doubleClickRegion.emit(region)
+  }
+
+  public displayTreeNode: (item:any) => string
+
+  private filterNameBySearchPipe = new FilterNameBySearch()
+  public filterTreeBySearch: (node:any) => boolean 
+
+  public aggregatedRegionTree: any
+
+}
\ No newline at end of file
diff --git a/src/ui/banner/banner.style.css b/src/ui/regionHierachy/regionHierarchy.style.css
similarity index 58%
rename from src/ui/banner/banner.style.css
rename to src/ui/regionHierachy/regionHierarchy.style.css
index 49fb40e3b1056a10c0d9e8086a28092b6f1539ba..a4aa507f4db1a32886dc1309934dc8481d49a425 100644
--- a/src/ui/banner/banner.style.css
+++ b/src/ui/regionHierachy/regionHierarchy.style.css
@@ -1,47 +1,14 @@
-:host
-{
-  display:block;
-  display:flex;
-  flex-direction: row;
-  flex-wrap : wrap;
-  min-height: 2.8em;
-  padding-left: 1em;
-}
-
-:host > *
-{
-  margin-right:0.5em;
-}
-
-div[treeHeader]
-{
-  height: 55px;
-}
-
-div[treeHeader] > span 
-{
-  display:inline-block;
-  padding: 12px 0px;
-  line-height:2em;
-  border:none;
-}
-
-div[treeHeader] > span.btn.btn-link
-{
-  margin-left:2em;
-}
 
 div[treeContainer]
 {
   padding:1em;
   z-index: 3;
 
-  max-height:40em;
-  width: calc(100% + 2em);
+  height:20em;
+  width: calc(100% + 4em);
   overflow-y:auto;
   overflow-x:hidden;
 
-
   /* color:white;
   background-color:rgba(12,12,12,0.8); */
 }
@@ -64,16 +31,8 @@ div[hideScrollbarcontainer]
   margin-top:2px;
 }
 
-div[searchRegionPopover]
-{
-  height:2em;
-}
-
 input[type="text"]
 {
-  border-radius:0px;
-  height:2.6em;
-  width:20em;
   border:none;
 }
 
@@ -90,15 +49,17 @@ input[type="text"]
   color:rgba(255,255,255,0.8);
 }
 
-i
+.regionSearch
+{
+  width:20em;
+}
+
+.tree-header
 {
-  font-size: 150%;
-  margin:auto 0;
+  flex: 0 0 auto;
 }
 
-citations-component
+.tree-body
 {
-  width: 500px;
-  max-width: 100%;
-  display:block;
+  flex: 1 1 auto;
 }
\ No newline at end of file
diff --git a/src/ui/regionHierachy/regionHierarchy.template.html b/src/ui/regionHierachy/regionHierarchy.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..36ab15e5278d6327517df36f71e137fc96c18f33
--- /dev/null
+++ b/src/ui/regionHierachy/regionHierarchy.template.html
@@ -0,0 +1,47 @@
+<div class="input-group regionSearch">
+  <input 
+    #searchTermInput
+    tabindex="0"
+    (keydown.esc)="escape($event)"
+    (focus)="showRegionTree = true && !isMobile"
+    [value]="searchTerm"
+    class="form-control form-control-sm"
+    type="text" 
+    autocomplete="off"
+    [placeholder]="getInputPlaceholder(selectedParcellation)"/>
+
+</div>
+  
+<div
+  *ngIf="showRegionTree" 
+  hideScrollbarContainer>
+
+  <div
+    class="d-flex flex-column"
+    treeContainer
+    #treeContainer>
+    <div class="tree-header d-inline-flex align-items-center">
+      <div>
+        {{ selectedRegions.length }} {{ selectedRegions.length > 1 ? 'regions' : 'region' }} selected
+      </div>
+      <div
+        (click)="clearRegions($event)"
+        [ngClass]="{ 'invisible': selectedRegions.length === 0 }"
+        class="btn btn-link">
+        clear all
+      </div>
+    </div>
+    
+    <div
+      *ngIf="selectedParcellation && selectedParcellation.regions as regions"
+      class="tree-body">
+      <flat-tree-component
+        (treeNodeClick)="handleClickRegion($event)"
+        [inputItem]="aggregatedRegionTree"
+        [renderNode]="displayTreeNode"
+        [searchFilter]="filterTreeBySearch">
+        
+      </flat-tree-component>
+    </div> 
+  </div>
+</div>
\ No newline at end of file
diff --git a/src/ui/sharedModules/angularMaterial.module.ts b/src/ui/sharedModules/angularMaterial.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f5b4938579625c01795ae85e04c85adac4f6529e
--- /dev/null
+++ b/src/ui/sharedModules/angularMaterial.module.ts
@@ -0,0 +1,8 @@
+import {MatButtonModule, MatCheckboxModule, MatSidenavModule, MatCardModule, MatTabsModule} from '@angular/material';
+import { NgModule } from '@angular/core';
+
+@NgModule({
+  imports: [MatButtonModule, MatCheckboxModule, MatSidenavModule, MatCardModule, MatTabsModule],
+  exports: [MatButtonModule, MatCheckboxModule, MatSidenavModule, MatCardModule, MatTabsModule],
+})
+export class AngularMaterialModule { }
\ No newline at end of file
diff --git a/src/ui/signinBanner/signinBanner.components.ts b/src/ui/signinBanner/signinBanner.components.ts
new file mode 100644
index 0000000000000000000000000000000000000000..75656e0fab1a4b698bcca07bc45eda09ba5ced30
--- /dev/null
+++ b/src/ui/signinBanner/signinBanner.components.ts
@@ -0,0 +1,185 @@
+import { Component, ChangeDetectionStrategy, OnDestroy, OnInit, Input } from "@angular/core";
+import { AtlasViewerConstantsServices } from "src/atlasViewer/atlasViewer.constantService.service";
+import { AuthService, User } from "src/services/auth.service";
+import { Store, select } from "@ngrx/store";
+import { ViewerConfiguration } from "src/services/state/viewerConfig.store";
+import { Subscription, Observable } from "rxjs";
+import { safeFilter, isDefined, NEWVIEWER, SELECT_REGIONS, SELECT_PARCELLATION, CHANGE_NAVIGATION } from "src/services/stateStore.service";
+import { map, filter, distinctUntilChanged } from "rxjs/operators";
+import { regionFlattener } from "src/util/regionFlattener";
+import { ToastService } from "src/services/toastService.service";
+
+const compareParcellation = (o, n) => o.name === n.name
+
+@Component({
+  selector: 'signin-banner',
+  templateUrl: './signinBanner.template.html',
+  styleUrls: [
+    './signinBanner.style.css',
+    '../btnShadow.style.css'
+  ],
+  changeDetection: ChangeDetectionStrategy.OnPush
+})
+
+export class SigninBanner implements OnInit, OnDestroy{
+
+  public compareParcellation = compareParcellation
+
+  private subscriptions: Subscription[] = []
+  public loadedTemplates$: Observable<any[]>
+  public selectedTemplate$: Observable<any>
+  public selectedParcellation$: Observable<any>
+  public selectedRegions$: Observable<any[]>
+  private selectedRegions: any[] = []
+  @Input() darktheme: boolean
+
+  constructor(
+    private constantService: AtlasViewerConstantsServices,
+    private authService: AuthService,
+    private store: Store<ViewerConfiguration>,
+    private toastService: ToastService
+  ){
+    this.loadedTemplates$ = this.store.pipe(
+      select('viewerState'),
+      safeFilter('fetchedTemplates'),
+      map(state => state.fetchedTemplates)
+    )
+
+    this.selectedTemplate$ = this.store.pipe(
+      select('viewerState'),
+      filter(state => isDefined(state) && isDefined(state.templateSelected)),
+      distinctUntilChanged((o, n) => o.templateSelected.name === n.templateSelected.name),
+      map(state => state.templateSelected)
+    )
+
+    this.selectedParcellation$ = this.store.pipe(
+      select('viewerState'),
+      safeFilter('parcellationSelected'),
+      map(state => state.parcellationSelected),
+      distinctUntilChanged((o, n) => o === n || (o && n && o.name === n.name)),
+    )
+
+    this.selectedRegions$ = this.store.pipe(
+      select('viewerState'),
+      safeFilter('regionsSelected'),
+      map(state => state.regionsSelected),
+      distinctUntilChanged((arr1, arr2) => arr1.length === arr2.length && (arr1 as any[]).every((item, index) => item.name === arr2[index].name))
+    )
+  }
+
+  ngOnInit(){
+
+    this.subscriptions.push(
+      this.selectedRegions$.subscribe(regions => {
+        this.selectedRegions = regions
+      })
+    )
+  }
+
+  ngOnDestroy(){
+    this.subscriptions.forEach(s => s.unsubscribe())
+  }
+
+  changeTemplate({ current, previous }){
+    if (previous && current && current.name === previous.name)
+      return
+    this.store.dispatch({
+      type: NEWVIEWER,
+      selectTemplate: current,
+      selectParcellation: current.parcellations[0]
+    })
+  }
+
+  changeParcellation({ current, previous }){
+    const { ngId: prevNgId} = previous
+    const { ngId: currNgId} = current
+    if (prevNgId === currNgId)
+      return
+    this.store.dispatch({
+      type: SELECT_PARCELLATION,
+      selectParcellation: current
+    })
+  }
+
+  // TODO handle mobile
+  handleRegionClick({ mode = 'single', region }){
+    if (!region)
+      return
+    
+    /**
+     * single click on region hierarchy => toggle selection
+     */
+    if (mode === 'single') {
+      const flattenedRegion = regionFlattener(region).filter(r => isDefined(r.labelIndex))
+      const flattenedRegionNames = new Set(flattenedRegion.map(r => r.name))
+      const selectedRegionNames = new Set(this.selectedRegions.map(r => r.name))
+      const selectAll = flattenedRegion.every(r => !selectedRegionNames.has(r.name))
+      this.store.dispatch({
+        type: SELECT_REGIONS,
+        selectRegions: selectAll
+          ? this.selectedRegions.concat(flattenedRegion)
+          : this.selectedRegions.filter(r => !flattenedRegionNames.has(r.name))
+      })
+    }
+
+    /**
+     * double click on region hierarchy => navigate to region area if it exists
+     */
+    if (mode === 'double') {
+
+      /**
+       * if position is defined, go to position (in nm)
+       * if not, show error messagea s toast
+       * 
+       * nb: currently, only supports a single triplet
+       */
+      if (region.position) {
+        this.store.dispatch({
+          type: CHANGE_NAVIGATION,
+          navigation: {
+            position: region.position
+          }
+        })
+      } else {
+        this.toastService.showToast(`${region.name} does not have a position defined`, {
+          timeout: 5000,
+          dismissable: true
+        })
+      }
+    }
+  }
+
+  displayActiveParcellation(parcellation:any){
+    return `<div class="d-flex"><small>Parcellation</small> <small class = "flex-grow-1 mute-text">${parcellation ? '(' + parcellation.name + ')' : ''}</small> <span class = "fas fa-caret-down"></span></div>`
+  }
+
+  displayActiveTemplate(template: any) {
+    return `<div class="d-flex"><small>Template</small> <small class = "flex-grow-1 mute-text">${template ? '(' + template.name + ')' : ''}</small> <span class = "fas fa-caret-down"></span></div>`
+  }
+
+  showHelp() {
+    this.constantService.showHelpSubject$.next()
+  }
+
+  showSignin() {
+    this.constantService.showSigninSubject$.next(this.user)
+  }
+
+  clearAllRegions(){
+    this.store.dispatch({
+      type: SELECT_REGIONS,
+      selectRegions: []
+    })
+  }
+
+  get isMobile(){
+    return this.constantService.mobile
+  }
+
+  get user() : User | null {
+    return this.authService.user
+  }
+
+  public flexItemIsMobileClass = 'mt-2'
+  public flexItemIsDesktopClass = 'mr-2'
+}
\ No newline at end of file
diff --git a/src/ui/signinBanner/signinBanner.style.css b/src/ui/signinBanner/signinBanner.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..126c598d6d8544ae5e1727d105ac6e20dd089c26
--- /dev/null
+++ b/src/ui/signinBanner/signinBanner.style.css
@@ -0,0 +1,58 @@
+:host > *
+{
+  flex: 0 0 0;
+  margin-right: 0.4em;
+  display:inline-block;
+}
+
+.loginPopover
+{
+  width: 0;
+  overflow: visible;
+  display:flex;
+  flex-direction: column;
+
+  align-items: flex-end;
+}
+
+:host > dropdown-component
+{
+  min-width: 10em;
+  flex: 1 0 auto;
+}
+
+region-hierarchy,
+dropdown-component
+{
+  font-size:80%;
+
+}
+
+.whenMobile {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  min-width: 100%;
+}
+
+.whenNotMobile {
+  display: flex;
+}
+
+.login-icon {
+  height: 28px;
+  margin: 0 3px;
+}
+
+.login-button-panel-mobile {
+  order: -1;
+  align-self: flex-start; 
+  width: 100%;
+  display: flex;
+  justify-content: space-between;
+}
+
+.login-button-mobile > button {
+  outline: none;
+  background-color: transparent;
+}
diff --git a/src/ui/signinBanner/signinBanner.template.html b/src/ui/signinBanner/signinBanner.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..51ae875d91ab9e9858a88eb801986edf6b9b7294
--- /dev/null
+++ b/src/ui/signinBanner/signinBanner.template.html
@@ -0,0 +1,78 @@
+<div
+  class="d-flex"  
+  [ngClass]="{ 'flex-column w-100 align-items-stretch' : isMobile}" >
+
+  <dropdown-component
+    (itemSelected)="changeTemplate($event)"
+    [activeDisplay]="displayActiveTemplate"
+    [selectedItem]="selectedTemplate$ | async"
+    [inputArray]="loadedTemplates$ | async | filterNull"
+    [isMobile] = "isMobile"
+    [ngClass]="isMobile ? flexItemIsMobileClass : flexItemIsDesktopClass"
+    [darktheme] ="darktheme">
+  </dropdown-component>
+
+  <ng-container *ngIf="selectedTemplate$ | async as selectedTemplate">
+    <dropdown-component
+      *ngIf="selectedParcellation$ | async as selectedParcellation"
+      (itemSelected)="changeParcellation($event)"
+      [checkSelected]="compareParcellation"
+      [activeDisplay]="displayActiveParcellation"
+      [selectedItem]="selectedParcellation"
+      [inputArray]="selectedTemplate.parcellations"
+      [isMobile] = "isMobile"
+      [ngClass]="isMobile ? flexItemIsMobileClass : flexItemIsDesktopClass"
+      [darktheme] ="darktheme">
+
+    </dropdown-component>
+    <region-hierarchy
+      [selectedRegions]="selectedRegions$ | async | filterNull"
+      (singleClickRegion)="handleRegionClick({ mode: 'single', region: $event })"
+      (doubleClickRegion)="handleRegionClick({ mode: 'double', region: $event })"
+      (clearAllRegions)="clearAllRegions()"
+      [isMobile] = "isMobile"
+      *ngIf="selectedParcellation$ | async as selectedParcellation"
+      class="h-0"
+      [selectedParcellation]="selectedParcellation"
+      [ngClass]="isMobile ? flexItemIsMobileClass : flexItemIsDesktopClass">
+
+    </region-hierarchy>
+  </ng-container>
+
+  <!-- help btn -->
+  <div class="btnWrapper">
+    <div
+      *ngIf="!isMobile" 
+      (click)="showHelp()" 
+      class="btn btn-outline-secondary btn-sm rounded-circle login-icon">
+      <i class="fas fa-question-circle"></i>
+    </div>
+  </div>
+
+  <!-- signin -->
+
+  <div class="btnWrapper">
+
+    <div 
+      *ngIf="!isMobile" 
+      (click)="showSignin()"
+      class="btn btn-outline-secondary btn-sm rounded-circle login-icon">
+      <i
+        [ngClass]="user ? 'fa-user' : 'fa-sign-in-alt'"
+        class="fas"></i>
+    </div>
+  </div>
+
+  <div *ngIf="isMobile" class="login-button-panel-mobile">
+    <div
+      (click)="showSignin()"
+      class="login-button-mobile">
+      <button mat-button [ngStyle]="{'color': darktheme? '#D7D7D7' : 'black'}">Log In</button>
+    </div>
+
+    <div (click)="showHelp()" class="login-button-mobile">
+      <button mat-button [ngStyle]="{'color': darktheme? '#D7D7D7' : 'black'}">Help</button>
+    </div>
+
+  </div>
+</div>
\ No newline at end of file
diff --git a/src/ui/signinModal/signinModal.component.ts b/src/ui/signinModal/signinModal.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ec017cc0a0b8eed5a314a979261c61076d77f0e9
--- /dev/null
+++ b/src/ui/signinModal/signinModal.component.ts
@@ -0,0 +1,35 @@
+import { Component } from "@angular/core";
+import { AuthService, User, AuthMethod } from "src/services/auth.service";
+
+@Component({
+  selector: 'signin-modal',
+  templateUrl: './signinModal.template.html',
+  styleUrls: [
+    './signinModal.style.css'
+  ]
+})
+
+export class SigninModal{
+  constructor(
+    private authService: AuthService
+  ){
+
+  }
+
+  get user() : User | null {
+    return this.authService.user
+  }
+  
+  get loginMethods(): AuthMethod[] {
+    return this.authService.loginMethods
+  }
+  
+  get logoutHref(): String {
+    return this.authService.logoutHref
+  }
+
+  loginBtnOnclick() {
+    this.authService.authSaveState()
+    return true
+  }
+}
\ No newline at end of file
diff --git a/src/ui/signinModal/signinModal.style.css b/src/ui/signinModal/signinModal.style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/ui/signinModal/signinModal.template.html b/src/ui/signinModal/signinModal.template.html
new file mode 100644
index 0000000000000000000000000000000000000000..7d2ce3b21d85ca8840b4bf3592f4067d88507f1c
--- /dev/null
+++ b/src/ui/signinModal/signinModal.template.html
@@ -0,0 +1,24 @@
+<div *ngIf="user; else notLoggedIn">
+  Hi {{ user.name }}.
+  <a
+    (click)="loginBtnOnclick()"
+    [href]="logoutHref"
+    class="btn btn-sm btn-outline-secondary">
+    <i class="fas fa-sign-out-alt"></i> Logout
+  </a>
+</div>
+
+<ng-template #notLoggedIn>
+  <div>
+    Not logged in. Login via:
+    <div class="btn-group-vertical">
+      <a
+        *ngFor="let m of loginMethods"
+        (click)="loginBtnOnclick()"
+        [href]="m.href"
+        class="btn btn-sm btn-outline-secondary">
+        <i class="fas fa-sign-in-alt"></i> {{ m.name }}
+      </a>
+    </div>
+  </div>
+</ng-template>
\ No newline at end of file
diff --git a/src/ui/ui.module.ts b/src/ui/ui.module.ts
index d11c1abf83ba42f8a09d73b0a189a8833f344235..f385e39f89b37fa536cde92a2dae0ae215f1def3 100644
--- a/src/ui/ui.module.ts
+++ b/src/ui/ui.module.ts
@@ -1,5 +1,4 @@
 import { NgModule } from "@angular/core";
-import { BrowserModule } from "@angular/platform-browser";
 import { ComponentsModule } from "../components/components.module";
 
 import { NehubaViewerUnit } from "./nehubaContainer/nehubaViewer/nehubaViewer.component";
@@ -7,23 +6,16 @@ import { NehubaContainer } from "./nehubaContainer/nehubaContainer.component";
 import { SplashScreen } from "./nehubaContainer/splashScreen/splashScreen.component";
 import { LayoutModule } from "../layouts/layout.module";
 import { FormsModule } from "@angular/forms";
-import { DataBrowserUI } from "./databrowser/databrowser.component";
+
 import { GroupDatasetByRegion } from "../util/pipes/groupDataEntriesByRegion.pipe";
 import { filterRegionDataEntries } from "../util/pipes/filterRegionDataEntries.pipe";
-import { FileViewer } from "./fileviewer/fileviewer.component";
+import { MenuIconsBar } from './menuicons/menuicons.component'
+
+import { GetUniquePipe } from "src/util/pipes/getUnique.pipe";
 
-import { ChartsModule } from 'ng2-charts'
-import { RadarChart } from "./fileviewer/radar/radar.chart.component";
-import { LineChart } from "./fileviewer/line/line.chart.component";
-import { PathToNestedChildren } from "../util/pipes/pathToNestedChildren.pipe";
-import { CopyPropertyPipe } from "../util/pipes/copyProperty.pipe";
-import { GetUniqueProperty } from "../util/pipes/getUniqueProperty.pipe";
-import { FilterDataEntriesbyType } from "../util/pipes/filterDataEntriesByType.pipe";
-import { DedicatedViewer } from "./fileviewer/dedicated/dedicated.component";
 import { LandmarkUnit } from "./nehubaContainer/landmarkUnit/landmarkUnit.component";
 import { SafeStylePipe } from "../util/pipes/safeStyle.pipe";
 import { PluginBannerUI } from "./pluginBanner/pluginBanner.component";
-import { AtlasBanner } from "./banner/banner.component";
 import { CitationsContainer } from "./citation/citations.component";
 import { LayerBrowser } from "./layerbrowser/layerbrowser.component";
 import { TooltipModule } from "ngx-bootstrap/tooltip";
@@ -31,7 +23,7 @@ import { KgEntryViewer } from "./kgEntryViewer/kgentry.component";
 import { SubjectViewer } from "./kgEntryViewer/subjectViewer/subjectViewer.component";
 import { GetLayerNameFromDatasets } from "../util/pipes/getLayerNamePipe.pipe";
 import { SortDataEntriesToRegion } from "../util/pipes/sortDataEntriesIntoRegion.pipe";
-import { DatasetViewerComponent } from "./datasetViewer/datasetViewer.component";
+
 import { SpatialLandmarksToDataBrowserItemPipe } from "../util/pipes/spatialLandmarksToDatabrowserItem.pipe";
 import { DownloadDirective } from "../util/directives/download.directive";
 import { LogoContainer } from "./logoContainer/logoContainer.component";
@@ -39,51 +31,70 @@ import { TemplateParcellationCitationsContainer } from "./templateParcellationCi
 import { MobileOverlay } from "./nehubaContainer/mobileOverlay/mobileOverlay.component";
 import { FilterNullPipe } from "../util/pipes/filterNull.pipe";
 import { ShowToastDirective } from "../util/directives/showToast.directive";
+import { HelpComponent } from "./help/help.component";
+import { ConfigComponent } from './config/config.component'
+import { FlatmapArrayPipe } from "src/util/pipes/flatMapArray.pipe";
+import { PopoverModule } from 'ngx-bootstrap/popover'
+import { DatabrowserModule } from "./databrowserModule/databrowser.module";
+import { SigninBanner } from "./signinBanner/signinBanner.components";
+import { SigninModal } from "./signinModal/signinModal.component";
+import { FilterNgLayer } from "src/util/pipes/filterNgLayer.pipe";
+import { UtilModule } from "src/util/util.module";
+import { RegionHierarchy } from "./regionHierachy/regionHierarchy.component";
+import { FilterNameBySearch } from "./regionHierachy/filterNameBySearch.pipe";
+import { StatusCardComponent } from "./nehubaContainer/statusCard/statusCard.component";
+import { CookieAgreement } from "./cookieAgreement/cookieAgreement.component";
+import { KGToS } from "./kgtos/kgtos.component";
+import { AngularMaterialModule } from "./sharedModules/angularMaterial.module";
 
 
 @NgModule({
   imports : [
-    ChartsModule,
     FormsModule,
-    BrowserModule,
     LayoutModule,
     ComponentsModule,
+    DatabrowserModule,
+    UtilModule,
+    AngularMaterialModule,
 
+    PopoverModule.forRoot(),
     TooltipModule.forRoot()
   ],
   declarations : [
     NehubaContainer,
     NehubaViewerUnit,
     SplashScreen,
-    DataBrowserUI,
-    FileViewer,
-    RadarChart,
-    LineChart,
-    DedicatedViewer,
     LandmarkUnit,
-    AtlasBanner,
     PluginBannerUI,
     CitationsContainer,
     LayerBrowser,
     KgEntryViewer,
     SubjectViewer,
-    DatasetViewerComponent,
     LogoContainer,
     TemplateParcellationCitationsContainer,
     MobileOverlay,
+    HelpComponent,
+    ConfigComponent,
+    MenuIconsBar,
+    SigninBanner,
+    SigninModal,
+    RegionHierarchy,
+    StatusCardComponent,
+    CookieAgreement,
+    KGToS,
 
     /* pipes */
     GroupDatasetByRegion,
     filterRegionDataEntries,
-    PathToNestedChildren,
-    CopyPropertyPipe,
-    GetUniqueProperty,
-    FilterDataEntriesbyType,
+    GetUniquePipe,
+    FlatmapArrayPipe,
     SafeStylePipe,
     GetLayerNameFromDatasets,
     SortDataEntriesToRegion,
     SpatialLandmarksToDataBrowserItemPipe,
     FilterNullPipe,
+    FilterNgLayer,
+    FilterNameBySearch,
 
     /* directive */
     DownloadDirective,
@@ -93,28 +104,30 @@ import { ShowToastDirective } from "../util/directives/showToast.directive";
 
     /* dynamically created components needs to be declared here */
     NehubaViewerUnit,
-    FileViewer,
-    DataBrowserUI,
-    DatasetViewerComponent
+    LayerBrowser,
+    PluginBannerUI
   ],
   exports : [
     SubjectViewer,
     KgEntryViewer,
     CitationsContainer,
-    AtlasBanner,
     PluginBannerUI,
     NehubaContainer,
     NehubaViewerUnit,
-    DataBrowserUI,
     LayerBrowser,
-    FileViewer,
     LogoContainer,
-    DatasetViewerComponent,
     TemplateParcellationCitationsContainer,
     MobileOverlay,
+    HelpComponent,
+    ConfigComponent,
+    MenuIconsBar,
+    SigninBanner,
+    SigninModal,
+    CookieAgreement,
+    KGToS,
+    AngularMaterialModule
   ]
 })
 
 export class UIModule{
-
 }
\ No newline at end of file
diff --git a/src/util/directives/FixedMouseContextualContainerDirective.directive.ts b/src/util/directives/FixedMouseContextualContainerDirective.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..47d15bb36b0b40d1a9c9f9f5f4a187ef6591d1d9
--- /dev/null
+++ b/src/util/directives/FixedMouseContextualContainerDirective.directive.ts
@@ -0,0 +1,58 @@
+import { Directive, Input, HostBinding, HostListener, ElementRef, OnChanges, Output, EventEmitter } from "@angular/core";
+
+@Directive({
+  selector: '[fixedMouseContextualContainerDirective]'
+})
+
+export class FixedMouseContextualContainerDirective {
+
+  private defaultPos: [number, number] = [-1e3, -1e3]
+  public isShown: boolean = false
+
+  @Input()
+  public mousePos: [number, number] = this.defaultPos
+
+  @Output()
+  public onShow: EventEmitter<null> = new EventEmitter()
+
+  @Output()
+  public onHide: EventEmitter<null> = new EventEmitter()
+
+  constructor(
+    private el: ElementRef
+  ){
+    
+  }
+
+  public show(){
+    this.transform = `translate(${this.mousePos.map(v => v.toString() + 'px').join(', ')})`
+    this.styleDisplay = 'block'
+    this.isShown = true
+    this.onShow.emit()
+  }
+
+  public hide(){
+    this.transform = `translate(${this.defaultPos.map(v => v.toString() + 'px').join(', ')})`
+    this.styleDisplay = 'none'
+    this.isShown = false
+    this.onHide.emit()
+  }
+
+  @HostBinding('style.display')
+  public styleDisplay = `none`
+
+  @HostBinding('style.transform')
+  public transform = `translate(${this.mousePos.map(v => v.toString() + 'px').join(', ')})`
+
+  @HostListener('document:click', ['$event'])
+  documentClick(event: MouseEvent){
+    if (event.button !== 2) {
+      if (this.styleDisplay === 'none')
+        return
+      if (this.el.nativeElement.contains(event.target))
+        return
+      this.hide()
+    }
+  }
+
+}
\ No newline at end of file
diff --git a/src/util/directives/dockedContainer.directive.ts b/src/util/directives/dockedContainer.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..741fa61fd3d190d1320292b4ed955919938a1749
--- /dev/null
+++ b/src/util/directives/dockedContainer.directive.ts
@@ -0,0 +1,16 @@
+import { Directive, ViewContainerRef } from "@angular/core";
+import { WidgetServices } from "src/atlasViewer/widgetUnit/widgetService.service";
+
+
+@Directive({
+  selector: '[dockedContainerDirective]'
+})
+
+export class DockedContainerDirective{
+  constructor(
+    widgetService: WidgetServices,
+    viewContainerRef: ViewContainerRef
+  ){
+    widgetService.dockedContainer = viewContainerRef
+  }
+}
\ No newline at end of file
diff --git a/src/util/directives/download.directive.ts b/src/util/directives/download.directive.ts
index 4f2e6b317f7a4fc54716959abb3e79885fc3c54c..650c8108b4f32fa657c01cb9a445ebf212129fb0 100644
--- a/src/util/directives/download.directive.ts
+++ b/src/util/directives/download.directive.ts
@@ -10,8 +10,8 @@ export class DownloadDirective{
 
   constructor(public el:ElementRef, public rd2:Renderer2){
     this.downloadIcon = rd2.createElement('i')
-    rd2.addClass(this.downloadIcon, 'glyphicon')
-    rd2.addClass(this.downloadIcon, 'glyphicon-download-alt')
+    rd2.addClass(this.downloadIcon, 'fas')
+    rd2.addClass(this.downloadIcon, 'fa-download-alt')
   }
 
   ngAfterViewInit(){
diff --git a/src/util/directives/floatingContainer.directive.ts b/src/util/directives/floatingContainer.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..740ab815a6ebeb6a7ab1c365467136e5288daacd
--- /dev/null
+++ b/src/util/directives/floatingContainer.directive.ts
@@ -0,0 +1,15 @@
+import { Directive, ViewContainerRef } from "@angular/core";
+import { WidgetServices } from "src/atlasViewer/widgetUnit/widgetService.service";
+
+@Directive({
+  selector: '[floatingContainerDirective]'
+})
+
+export class FloatingContainerDirective{
+  constructor(
+    widgetService: WidgetServices,
+    viewContainerRef: ViewContainerRef
+  ){
+    widgetService.floatingContainer = viewContainerRef
+  }
+}
\ No newline at end of file
diff --git a/src/util/directives/floatingMouseContextualContainer.directive.ts b/src/util/directives/floatingMouseContextualContainer.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2f62b11aac7311055c9023ba1c32e83077807bbb
--- /dev/null
+++ b/src/util/directives/floatingMouseContextualContainer.directive.ts
@@ -0,0 +1,20 @@
+import { Directive, HostListener, HostBinding } from "@angular/core";
+
+@Directive({
+  selector: '[floatingMouseContextualContainerDirective]'
+})
+
+export class FloatingMouseContextualContainerDirective{
+  
+  private mousePos: [number, number] = [0, 0]
+
+  @HostListener('document:mousemove', ['$event'])
+  mousemove(event:MouseEvent){
+    this.mousePos = [event.clientX, event.clientY]
+  }
+
+  @HostBinding('style.transform')
+  get transform(){
+    return `translate(${this.mousePos[0]}px,${this.mousePos[1]}px)`
+  }
+}
\ No newline at end of file
diff --git a/src/util/directives/glyphiconTooltip.directive.ts b/src/util/directives/glyphiconTooltip.directive.ts
index bda7412a118051ca85363a794d81a6806d295277..38544204a3295fc19ca075266350b53482ee6309 100644
--- a/src/util/directives/glyphiconTooltip.directive.ts
+++ b/src/util/directives/glyphiconTooltip.directive.ts
@@ -6,10 +6,10 @@ import { TooltipDirective } from 'ngx-bootstrap/tooltip'
 import { ComponentLoaderFactory } from 'ngx-bootstrap/component-loader';
 
 @Directive({
-  selector : '.glyphicon.glyphicon-screenshot'
+  selector : '.fas.fa-screenshot'
 })
 
-export class GlyphiconTooltipScreenshotDirective extends TooltipDirective{
+export class fasTooltipScreenshotDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -28,10 +28,10 @@ export class GlyphiconTooltipScreenshotDirective extends TooltipDirective{
 }
 
 @Directive({
-  selector : '.glyphicon.glyphicon-remove-sign'
+  selector : '.fas.fa-remove-sign'
 })
 
-export class GlyphiconTooltipRemoveSignDirective extends TooltipDirective{
+export class fasTooltipRemoveSignDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -50,10 +50,10 @@ export class GlyphiconTooltipRemoveSignDirective extends TooltipDirective{
 }
 
 @Directive({
-  selector : '.glyphicon.glyphicon-remove'
+  selector : '.fas.fa-remove'
 })
 
-export class GlyphiconTooltipRemoveDirective extends TooltipDirective{
+export class fasTooltipRemoveDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -72,10 +72,10 @@ export class GlyphiconTooltipRemoveDirective extends TooltipDirective{
 }
 
 @Directive({
-  selector : '.glyphicon.glyphicon-new-window'
+  selector : '.fas.fa-new-window'
 })
 
-export class GlyphiconTooltipNewWindowDirective extends TooltipDirective{
+export class fasTooltipNewWindowDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -94,10 +94,10 @@ export class GlyphiconTooltipNewWindowDirective extends TooltipDirective{
 }
 
 @Directive({
-  selector : '.glyphicon.glyphicon-log-in'
+  selector : '.fas.fa-log-in'
 })
 
-export class GlyphiconTooltipLogInDirective extends TooltipDirective{
+export class fasTooltipLogInDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -115,10 +115,10 @@ export class GlyphiconTooltipLogInDirective extends TooltipDirective{
   }
 }
 @Directive({
-  selector : '.glyphicon.glyphicon-question-sign'
+  selector : '.fas.fa-question-circle'
 })
 
-export class GlyphiconTooltipQuestionSignDirective extends TooltipDirective{
+export class fasTooltipQuestionSignDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
@@ -136,10 +136,10 @@ export class GlyphiconTooltipQuestionSignDirective extends TooltipDirective{
   }
 }
 @Directive({
-  selector : '.glyphicon.glyphicon-info-sign'
+  selector : '.fas.fa-info-sign'
 })
 
-export class GlyphiconTooltipInfoSignDirective extends TooltipDirective{
+export class fasTooltipInfoSignDirective extends TooltipDirective{
   constructor(
     public viewContainerRef:ViewContainerRef,
     public rd : Renderer2,
diff --git a/src/util/directives/help.directive.ts b/src/util/directives/help.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..31257ba52dd6b781cc6313020937647f335816d9
--- /dev/null
+++ b/src/util/directives/help.directive.ts
@@ -0,0 +1,41 @@
+import { Directive, ElementRef, Renderer2 } from '@angular/core'
+import { AtlasViewerConstantsServices } from '../../atlasViewer/atlasViewer.constantService.service';
+
+@Directive({
+  selector : 'div[helpdirective]'
+})
+export class HelpDirective{
+  constructor(
+    // rd2:Renderer2,
+    private elementRef:ElementRef,
+    private constantService:AtlasViewerConstantsServices
+  ){
+    /**
+     * TODO angular does not currently (7.1.2019) support capture events. when it does, use rd should be more efficient
+     */
+    // rd2.listen(elementRef.nativeElement, 'keydown', this.keydownHandler.bind(this))
+    
+  }
+
+  keydownHandler(ev:KeyboardEvent){
+    
+    const target = <HTMLElement> ev.target
+    const tagName = target.tagName
+
+    if (tagName === 'SELECT' || tagName === 'INPUT' || tagName === 'TEXTAREA')
+      return
+    
+    if (ev.key === 'h' || ev.key === 'H' || ev.key === '?') {
+      ev.stopPropagation()
+      ev.preventDefault()
+      /**
+       * call help modal
+       */
+      this.constantService.showHelpSubject$.next()
+    }
+  }
+
+  ngAfterViewInit(){
+    this.elementRef.nativeElement.addEventListener('keydown', this.keydownHandler.bind(this), true)
+  }
+}
\ No newline at end of file
diff --git a/src/util/directives/pluginFactory.directive.ts b/src/util/directives/pluginFactory.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..5bcd04c54fda4798f82c1784408a709b21895f67
--- /dev/null
+++ b/src/util/directives/pluginFactory.directive.ts
@@ -0,0 +1,76 @@
+import { Directive, ViewContainerRef, Renderer2 } from "@angular/core";
+import { PluginServices } from "src/atlasViewer/atlasViewer.pluginService.service";
+import { AtlasViewerAPIServices } from "src/atlasViewer/atlasViewer.apiService.service";
+import { SUPPORT_LIBRARY_MAP } from "src/atlasViewer/atlasViewer.constantService.service";
+
+@Directive({
+  selector: '[pluginFactoryDirective]'
+})
+
+export class PluginFactoryDirective{
+  constructor(
+    pluginService: PluginServices,
+    viewContainerRef: ViewContainerRef,
+    rd2: Renderer2,
+    apiService:AtlasViewerAPIServices
+  ){
+    pluginService.pluginViewContainerRef = viewContainerRef
+    pluginService.appendSrc = (src: HTMLElement) => rd2.appendChild(document.head, src)
+    pluginService.removeSrc = (src: HTMLElement) => rd2.removeChild(document.head, src)
+
+    apiService.interactiveViewer.pluginControl.loadExternalLibraries = (libraries: string[]) => new Promise((resolve, reject) => {
+      const srcHTMLElement = libraries.map(libraryName => ({
+        name: libraryName,
+        srcEl: SUPPORT_LIBRARY_MAP.get(libraryName)
+      }))
+
+      const rejected = srcHTMLElement.filter(scriptObj => scriptObj.srcEl === null)
+      if (rejected.length > 0)
+        return reject(`Some library names cannot be recognised. No libraries were loaded: ${rejected.map(srcObj => srcObj.name).join(', ')}`)
+
+      Promise.all(srcHTMLElement.map(scriptObj => new Promise((rs, rj) => {
+        /**
+         * if browser already support customElements, do not append polyfill
+         */
+        if('customElements' in window && scriptObj.name === 'webcomponentsLite'){
+          return rs()
+        }
+        const existingEntry = apiService.loadedLibraries.get(scriptObj.name)
+        if (existingEntry) {
+          apiService.loadedLibraries.set(scriptObj.name, { counter: existingEntry.counter + 1, src: existingEntry.src })
+          rs()
+        } else {
+          const srcEl = scriptObj.srcEl
+          srcEl.onload = () => rs()
+          srcEl.onerror = (e: any) => rj(e)
+          rd2.appendChild(document.head, srcEl)
+          apiService.loadedLibraries.set(scriptObj.name, { counter: 1, src: srcEl })
+        }
+      })))
+        .then(() => resolve())
+        .catch(e => (console.warn(e), reject(e)))
+    })
+
+    apiService.interactiveViewer.pluginControl.unloadExternalLibraries = (libraries: string[]) =>
+      libraries
+        .filter((stringname) => SUPPORT_LIBRARY_MAP.get(stringname) !== null)
+        .forEach(libname => {
+          const ledger = apiService.loadedLibraries.get(libname!)
+          if (!ledger) {
+            console.warn('unload external libraries error. cannot find ledger entry...', libname, apiService.loadedLibraries)
+            return
+          }
+          if (ledger.src === null) {
+            console.log('webcomponents is native supported. no library needs to be unloaded')
+            return
+          }
+
+          if (ledger.counter - 1 == 0) {
+            rd2.removeChild(document.head, ledger.src)
+            apiService.loadedLibraries.delete(libname!)
+          } else {
+            apiService.loadedLibraries.set(libname!, { counter: ledger.counter - 1, src: ledger.src })
+          }
+        })
+  }
+}
\ No newline at end of file
diff --git a/src/util/directives/toastContainer.directive.ts b/src/util/directives/toastContainer.directive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f32c5346226341b32e88d1ecacfcb658589871a2
--- /dev/null
+++ b/src/util/directives/toastContainer.directive.ts
@@ -0,0 +1,74 @@
+import { Directive, ViewContainerRef, ComponentFactoryResolver, TemplateRef, ComponentRef } from '@angular/core'
+import { ToastService, defaultToastConfig } from 'src/services/toastService.service';
+import { ToastComponent } from 'src/components/toast/toast.component';
+import { AtlasViewerAPIServices } from 'src/atlasViewer/atlasViewer.apiService.service';
+import { ToastHandler } from '../pluginHandlerClasses/toastHandler';
+
+@Directive({
+  selector: '[toastDirective]'
+})
+
+export class ToastContainerDirective{
+  constructor(
+    private viewContainerRef: ViewContainerRef,
+    private toastService: ToastService,
+    private cfr: ComponentFactoryResolver,
+    private apiService: AtlasViewerAPIServices
+  ){
+    const toastComponentFactory = this.cfr.resolveComponentFactory(ToastComponent)
+    
+    this.toastService.showToast = (message, config = {}) => {
+
+      const _config = {
+        ...defaultToastConfig,
+        ...config
+      } 
+      const toastComponent = this.viewContainerRef.createComponent(toastComponentFactory)
+      if(typeof message === 'string')
+        toastComponent.instance.message = message
+      if(message instanceof TemplateRef){
+        toastComponent.instance.messageContainer.createEmbeddedView(message as TemplateRef<any>)
+      }
+         
+      toastComponent.instance.dismissable = _config.dismissable
+      toastComponent.instance.timeout = _config.timeout
+
+      let subscription
+
+      const dismissToast = () => {
+        if(subscription) subscription.unsubscribe()
+        toastComponent.destroy()
+      }
+
+      subscription = toastComponent.instance.dismissed.subscribe(dismissToast)
+      return dismissToast
+    }
+
+    this.apiService.interactiveViewer.uiHandle.getToastHandler = () => {
+      const handler = new ToastHandler()
+      let toastComponent:ComponentRef<ToastComponent>
+      handler.show = () => {
+        toastComponent = this.viewContainerRef.createComponent(toastComponentFactory)
+
+        toastComponent.instance.dismissable = handler.dismissable
+        toastComponent.instance.message = handler.message
+        toastComponent.instance.htmlMessage = handler.htmlMessage
+        toastComponent.instance.timeout = handler.timeout
+
+        const _subscription = toastComponent.instance.dismissed.subscribe(userInitiated => {
+          _subscription.unsubscribe()
+          handler.hide()
+        })
+      }
+
+      handler.hide = () => {
+        if(toastComponent){
+          toastComponent.destroy()
+          toastComponent = null
+        }
+      }
+
+      return handler
+    }
+  }
+}
\ No newline at end of file
diff --git a/src/util/generator.ts b/src/util/generator.ts
index 90992f8fd51ad8dcc6bbc5202aab4e4826efffcc..5db5d5015a93f467bf1862f4a42dc83db42ce43b 100644
--- a/src/util/generator.ts
+++ b/src/util/generator.ts
@@ -12,4 +12,16 @@ export function* timedValues(ms:number = 500,mode:string = 'linear'){
     yield getValue( (Date.now() - startTime) / ms )
   }
   return 1
+}
+
+export function clamp(val: number, min: number, max: number) {
+  const _min = min < max ? min : max
+  const _max = min < max ? max : min
+  return Math.min(
+    Math.max(
+      val,
+      _min
+    ),
+    _max
+  )
 }
\ No newline at end of file
diff --git a/src/util/pipes/filterDataEntriesByType.pipe.ts b/src/util/pipes/filterDataEntriesByType.pipe.ts
deleted file mode 100644
index f343173f3d1261e3f8ddd933bc481ace5452df46..0000000000000000000000000000000000000000
--- a/src/util/pipes/filterDataEntriesByType.pipe.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { PipeTransform, Pipe } from "@angular/core";
-import { DataEntry } from "../../services/stateStore.service";
-
-
-@Pipe({
-  name : 'filterDataEntriesByType'
-})
-
-export class FilterDataEntriesbyType implements PipeTransform{
-  public transform(dataEntries:DataEntry[],hideTypeSet:Set<string>):DataEntry[]{
-    return dataEntries.filter(dataEntry=>!hideTypeSet.has(dataEntry.type))
-  }
-}
\ No newline at end of file
diff --git a/src/util/pipes/filterNgLayer.pipe.ts b/src/util/pipes/filterNgLayer.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ab14b3ab8556bcd85d6153119cc2667fb7f72132
--- /dev/null
+++ b/src/util/pipes/filterNgLayer.pipe.ts
@@ -0,0 +1,13 @@
+import { Pipe, PipeTransform } from "@angular/core";
+import { NgLayerInterface } from "src/atlasViewer/atlasViewer.component";
+
+@Pipe({
+  name: 'filterNgLayer'
+})
+
+export class FilterNgLayer implements PipeTransform{
+  public transform(excludedLayers: string[] = [], ngLayers: NgLayerInterface[]): NgLayerInterface[] {
+    const set = new Set(excludedLayers)
+    return ngLayers.filter(l => !set.has(l.name))
+  }
+}
\ No newline at end of file
diff --git a/src/util/pipes/flatMapArray.pipe.ts b/src/util/pipes/flatMapArray.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1b5d62f0dada9b7048d2d866b8a4cca54525b124
--- /dev/null
+++ b/src/util/pipes/flatMapArray.pipe.ts
@@ -0,0 +1,11 @@
+import { Pipe, PipeTransform } from "@angular/core";
+
+@Pipe({
+  name: 'flatmapArrayPipe'
+})
+
+export class FlatmapArrayPipe implements PipeTransform{
+  public transform(aoa: any[][]){
+    return aoa.reduce((acc, array) => acc.concat(array), [])
+  }
+}
\ No newline at end of file
diff --git a/src/util/pipes/getUnique.pipe.ts b/src/util/pipes/getUnique.pipe.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1ab3800b61ac9f4d7c12390b575523170fee4850
--- /dev/null
+++ b/src/util/pipes/getUnique.pipe.ts
@@ -0,0 +1,12 @@
+import { Pipe, PipeTransform } from "@angular/core";
+
+
+@Pipe({
+  name: 'getUniquePipe'
+})
+
+export class GetUniquePipe implements PipeTransform{
+  public transform(arr: any[]) {
+    return Array.from(new Set(arr))
+  }
+}
\ No newline at end of file
diff --git a/src/util/pipes/getUniqueProperty.pipe.ts b/src/util/pipes/getUniqueProperty.pipe.ts
deleted file mode 100644
index b7754ccaace64af70eebc8eef558f0a259ba4841..0000000000000000000000000000000000000000
--- a/src/util/pipes/getUniqueProperty.pipe.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-import { Pipe, PipeTransform } from "@angular/core";
-
-
-@Pipe({
-  name : 'getUniqueProperty'
-})
-
-export class GetUniqueProperty implements PipeTransform{
-  public transform(arr:any[],prop:string):string[]{
-    return [...arr.reduce((acc:Set<string>,curr)=>{
-      return curr[prop] && typeof curr[prop] === 'string' ? 
-        acc.has(curr[prop]) ?
-          acc :
-          acc.add(curr[prop]) :
-        acc
-    },new Set())]
-  }
-}
\ No newline at end of file
diff --git a/src/util/pipes/groupDataEntriesByRegion.pipe.ts b/src/util/pipes/groupDataEntriesByRegion.pipe.ts
index 7c2986db80ad5ebb4152f332f70aa402473cf3be..f4c326b4c253340fa04c819af0b637b85c974a60 100644
--- a/src/util/pipes/groupDataEntriesByRegion.pipe.ts
+++ b/src/util/pipes/groupDataEntriesByRegion.pipe.ts
@@ -5,22 +5,21 @@ import { DataEntry } from "../../services/stateStore.service";
   name : 'groupDatasetByRegion'
 })
 export class GroupDatasetByRegion implements PipeTransform{
-  public transform(datasets:DataEntry[], regions:any[]): {region:any|null,searchResults:DataEntry[]}[]
-  {
+  public transform(datasets:DataEntry[], regions:any[]): {region:any|null,searchResults:DataEntry[]}[]{
     
     return datasets.reduce((acc,curr)=>{
-      return (curr.regionName && curr.regionName.length > 0)
-        ? curr.regionName.reduce((acc2,reName)=>{
+      return (curr.parcellationRegion && curr.parcellationRegion.length > 0)
+        ? curr.parcellationRegion.reduce((acc2,reName)=>{
           const idx = acc.findIndex(it => it.region === null 
-            ? reName.regionName === 'none' 
-            : it.region.name === reName.regionName )
+            ? reName.name === 'none' 
+            : it.region.name === reName.name )
 
           return idx >= 0
             ? acc2.map((v,i)=> i === idx 
               ? Object.assign({},v,{searchResults : v.searchResults.concat(curr)}) 
               : v ) 
             : acc2.concat({
-                region : this.getRegionFromRegionName(reName.regionName, regions),
+                region : this.getRegionFromRegionName(reName.name, regions),
                 searchResults : [ curr ]
               })
           },acc)
diff --git a/src/util/pipes/sortDataEntriesIntoRegion.pipe.ts b/src/util/pipes/sortDataEntriesIntoRegion.pipe.ts
index 6a32f88ac7e132d9ac74c8f7c09d61f5eea2d9f8..7f2393f94269caa3fc760c50fb025f69dbb47ba3 100644
--- a/src/util/pipes/sortDataEntriesIntoRegion.pipe.ts
+++ b/src/util/pipes/sortDataEntriesIntoRegion.pipe.ts
@@ -9,7 +9,7 @@ export class SortDataEntriesToRegion implements PipeTransform{
   public transform(regions: any[], datasets:DataEntry[]):{region:any,searchResults:DataEntry[]}[]{
     return regions.map(region => ({
       region,
-      searchResults : datasets.filter(dataset => dataset.regionName.some(r => r.regionName === region.name))
+      searchResults : datasets.filter(dataset => dataset.parcellationRegion.some(r => r.name === region.name))
     }))
   }
 }
\ No newline at end of file
diff --git a/src/util/pluginHandlerClasses/toastHandler.ts b/src/util/pluginHandlerClasses/toastHandler.ts
index deae6df7ca4eb39412cee1e5d0398a7ea95d4198..80d696980b907641d384600bcfe3643163037fe8 100644
--- a/src/util/pluginHandlerClasses/toastHandler.ts
+++ b/src/util/pluginHandlerClasses/toastHandler.ts
@@ -4,4 +4,5 @@ export class ToastHandler{
   dismissable : boolean = true
   show : () => void
   hide : () => void
+  htmlMessage: string
 }
\ No newline at end of file
diff --git a/src/util/regionFlattener.spec.ts b/src/util/regionFlattener.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/util/regionFlattener.ts b/src/util/regionFlattener.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d5ae471e16e5a2fba8d4bf3de59720087a4f5491
--- /dev/null
+++ b/src/util/regionFlattener.ts
@@ -0,0 +1,6 @@
+export function regionFlattener(region:any){
+  return[
+    [ region ],
+    ...((region.children && region.children.map && region.children.map(regionFlattener)) || [])
+  ].reduce((acc, item) => acc.concat(item), [])
+}
\ No newline at end of file
diff --git a/src/util/util.module.ts b/src/util/util.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a83857d6198306aad9e955f7247b01baf29e4a2e
--- /dev/null
+++ b/src/util/util.module.ts
@@ -0,0 +1,12 @@
+import { NgModule } from "@angular/core";
+
+@NgModule({
+  declarations: [
+  ],
+  exports: [
+  ]
+})
+
+export class UtilModule{
+
+}
\ No newline at end of file
diff --git a/src/util/worker.js b/src/util/worker.js
index 2d804bf5f8388c5b715655f97f74374bd5b6272d..293d0908b1b145a0283f8bf51e9370c332854db3 100644
--- a/src/util/worker.js
+++ b/src/util/worker.js
@@ -1,5 +1,5 @@
-const validTypes = ['CHECK_MESHES', 'GET_LANDMARKS_VTK', 'GET_USERLANDMARKS_VTK']
-const validOutType = ['CHECKED_MESH', 'ASSEMBLED_LANDMARKS_VTK', 'ASSEMBLED_USERLANDMARKS_VTK']
+const validTypes = ['CHECK_MESHES', 'GET_LANDMARKS_VTK', 'GET_USERLANDMARKS_VTK', 'BUILD_REGION_SELECTION_TREE']
+const validOutType = ['CHECKED_MESH', 'ASSEMBLED_LANDMARKS_VTK', 'ASSEMBLED_USERLANDMARKS_VTK', 'RETURN_REBUILT_REGION_SELECTION_TREE']
 
 const checkMeshes = (action) => {
   
@@ -205,6 +205,22 @@ const getuserLandmarksVtk = (action) => {
   const scale = action.scale
     ? action.scale
     : 2.8
+
+  /**
+   * if userlandmarks vtk is empty, that means user removed all landmarks
+   * thus, removing revoking URL, and send null as assembled userlandmark vtk
+   */
+  if (landmarks.length === 0) {
+
+    if(userLandmarkVtkUrl) URL.revokeObjectURL(userLandmarkVtkUrl)
+
+    postMessage({
+      type: 'ASSEMBLED_USERLANDMARKS_VTK'
+    })
+
+    return
+  }
+
   const vtk = parseLmToVtk(landmarks, scale)
   if(!vtk)
     return
@@ -219,9 +235,42 @@ const getuserLandmarksVtk = (action) => {
   })
 }
 
+const rebuildSelectedRegion = (payload) => {
+  const { selectedRegions, regions } = payload
+
+  /**
+   * active tree branch
+   * branch is active if ALL children are active
+   */
+  const activeTreeBranch = []
+  const isRegionActive = (region) => selectedRegions.some(r => r.name === region.name)
+    || region.children && region.children.length > 0 && region.children.every(isRegionActive)
+  
+  /**
+   * some active tree branch
+   * branch is active if SOME children are active
+   */
+  const someActiveTreeBranch = []
+  const isSomeRegionActive = (region) => selectedRegions.some(r => r.name === region.name)
+    || region.children && region.children.length > 0 && region.children.some(isSomeRegionActive)
+
+  const handleRegion = (r) => {
+    isRegionActive(r) ? activeTreeBranch.push(r) : {}
+    isSomeRegionActive(r) ? someActiveTreeBranch.push(r) : {}
+    if (r.children && r.children.length > 0) 
+      r.children.forEach(handleRegion)
+  }
+  regions.forEach(handleRegion)
+  postMessage({
+    type: 'RETURN_REBUILT_REGION_SELECTION_TREE',
+    rebuiltSelectedRegions: activeTreeBranch,
+    rebuiltSomeSelectedRegions: someActiveTreeBranch
+  })
+}
+
 onmessage = (message) => {
   
-  if(validTypes.findIndex(type => type === message.data.type)>=0){
+  if(validTypes.findIndex(type => type === message.data.type) >= 0){
     switch(message.data.type){
       case 'CHECK_MESHES':
         checkMeshes(message.data)
@@ -232,6 +281,9 @@ onmessage = (message) => {
       case 'GET_USERLANDMARKS_VTK':
         getuserLandmarksVtk(message.data)
         return
+      case 'BUILD_REGION_SELECTION_TREE':
+        rebuildSelectedRegion(message.data)
+        return
       default:
         console.warn('unhandled worker action', message)
     }
diff --git a/tsconfig-aot.json b/tsconfig-aot.json
index 5d0448ad0199cc12fe53cc8fbcf7e697667fc19a..3a4115d6cfbc48fe643b9e85be53edab3f03ff25 100644
--- a/tsconfig-aot.json
+++ b/tsconfig-aot.json
@@ -7,7 +7,8 @@
     "sourceMap": false,
     "baseUrl": ".",
     "paths": {
-      "third_party/*" : ["third_party/*"]
+      "third_party/*" : ["third_party/*"],
+      "src/*" : ["src/*"]
     }
   },
   "angularCompilerOptions":{
diff --git a/tsconfig.json b/tsconfig.json
index a15e5d0540dbc162397df6cfbdcf97b742da48a1..f8617eab74953284b4d1a3f9ed6e234b455c10ab 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -7,7 +7,8 @@
     "sourceMap": true,
     "baseUrl": ".",
     "paths": {
-      "third_party/*" : ["third_party/*"]
+      "third_party/*" : ["third_party/*"],
+      "src/*" : ["src/*"]
     }
   }
 }
\ No newline at end of file
diff --git a/typings/index.d.ts b/typings/index.d.ts
index cc355ad130d56514f9e956fa79c45c19870c0a6e..4007a30c18e11c74bc2854ad9eff4e621525379a 100644
--- a/typings/index.d.ts
+++ b/typings/index.d.ts
@@ -11,4 +11,5 @@ declare module '*.css' {
 declare var PLUGINDEV : string
 declare var BUNDLEDPLUGINS : string[]
 declare var VERSION : string
-declare var PRODUCTION: boolean
\ No newline at end of file
+declare var PRODUCTION: boolean
+declare var BACKEND_URL: string
\ No newline at end of file
diff --git a/webpack.aot.js b/webpack.aot.js
index 881b9448819220303ba63a8349310a7f7ff542e1..2387968a71f12e52d4752e116bc760f0f7c723b4 100644
--- a/webpack.aot.js
+++ b/webpack.aot.js
@@ -66,7 +66,8 @@ module.exports = merge(staticAssets, {
       '.json'
     ],
     alias : {
-      "third_party" : path.resolve(__dirname,'third_party')
+      "third_party" : path.resolve(__dirname,'third_party'),
+      "src" : path.resolve(__dirname,'src')
     }
   }
 })
\ No newline at end of file
diff --git a/webpack.common.js b/webpack.common.js
index cd45150f4c4612bb8a8ba050a0650128c2f3606f..aafad55efaecb04b6f4684048c5fc6e91479d946 100644
--- a/webpack.common.js
+++ b/webpack.common.js
@@ -30,7 +30,8 @@ module.exports = {
       '.json'
     ],
     alias : {
-      "third_party" : path.resolve(__dirname,'third_party')
+      "third_party" : path.resolve(__dirname,'third_party'),
+      "src" : path.resolve(__dirname,'src')
     }
   },
 }
\ No newline at end of file
diff --git a/webpack.staticassets.js b/webpack.staticassets.js
index de19c73b0e9955a1f4f4ab164bc976ea4ba00771..6523e83f2a2128ce581bfaaac5f0755308807f92 100644
--- a/webpack.staticassets.js
+++ b/webpack.staticassets.js
@@ -54,7 +54,8 @@ module.exports = {
           : JSON.stringify('unspecificied hash'),
       PRODUCTION: process.env.PRODUCTION
         ? true
-        : false
+        : false,
+      BACKEND_URL: JSON.stringify(process.env.BACKEND_URL || 'http://localhost:3000/')
     })
     // ...ignoreArr.map(dirname => new webpack.IgnorePlugin(/\.\/plugin_examples/))
   ]