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@@ -1,7 +1,11 @@
--**Human Brain Project Tools Description**
++EBRAINS offers an extensive range of data and also provides compute resources (High-performance computing and Neuromorphic hardware). SLU can guide your journey on EBRAINS, show you how you find tools/data/related research projects appropriate for your research, and help you translate your work from the structured formalization process of science to technical requirements on EBRAINS.
--On this page you will find a list with a brief description of the tools we have for the HBP tools Book, if you have a suggestion or detect an omission please send your feedback to [[slu@ebrains.eu>>mailto:slu@ebrains.eu]].
++EBRAINS support teams will help you connect to the community and give you tips for creating your simulations and models. If you are a neuroscientist, professor, or student, or are you interested in brain-related research, simulation, AI, robotics, and brain-inspired hardware check out what EBRAINS has for you.
++As a researcher, you might find yourself immersed in a continuous flow of information, data, environments, and platforms which offer different tools and aids to fulfill an investigation and publish your results. This new way to doing science helps us advance at a fast pace and reduces efforts on searching data and tests, also allows us to deploy state-of-the-art simulations that can improve the quality of our work, and increase the capacity of neuroscientists for multiscale neural activity modeling of the human brain network.
++
++On this page you will find a quick overview of the different tools and services available in EBRAINS. It will address, in an interactive way, how to use EBRAINS for specific use cases from the participants and focus on exploring all the potential that EBRAINS, as a digital research infrastructure, provides to its users. Researchers will have the opportunity to get creative and combine the different EBRAINS components to respond to existing questions and formulate new avenues based on collaboration, sharing, co-design, and innovation.
++
  {{html}}
  <html>
@@ -1309,7 +1309,7 @@
  <h2><a name="_Toc138932256"><span lang=en-DE>BioNAR</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>BioNAR combines a selection of
++<p class=MsoNormal><span lang=en-DE>&quot;BioNAR combines a selection of
  existing R protocols for network analysis with newly designed original
  methodological features to support step-by-step analysis of
  biological/biomedical networks. BioNAR supports a pipeline approach where many
@@ -1319,7 +1319,7 @@
  independent annotation enrichment</span><span lang=en-DE style='font-family:
  "Times New Roman",serif'> </span><span lang=en-DE>predict a proteins influence
  within and across multiple sub-complexes in the network and estimate the
--co-occurrence or linkage between meta-data at the network level.</span></p>
++co-occurrence or linkage between meta-data at the network level.&quot;</span></p>
  <h2></h2>
@@ -1398,7 +1398,7 @@
  <h2><a name="_Toc138932263"><span lang=en-DE>BrainScaleS</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>Emulate spiking neural networks in
++<p class=MsoNormal><span lang=en-DE>&quot;Emulate spiking neural networks in
  continuous time on the BrainScaleS analog neuromorphic computing system. Models
  and experiments can be described in Python using the PyNN modelling language,
  or in hxtorch, a PyTorch-based machine-learning-friendly API.  The platform can
@@ -1406,7 +1406,7 @@
  lang=en-DE style='font-family:"Times New Roman",serif'> </span><span
  lang=en-DE>in addition, the NMPI web service provides batch-style access. The
  modelling APIs employ common data formats for input and output data, e.g.,
--neo.</span></p>
++neo.&quot;</span></p>
  <h2></h2>
@@ -1423,6 +1423,8 @@
  connected clients. Already-made plugins include CircuitExplorer, DTI,
  AtlasExplorer, CylindricCamera and MoleculeExplorer.</span></p>
++<p class=MsoNormal><span lang=en-DE>https://github.com/BlueBrain/Brayns/ </span></p>
++
  <p class=MsoNormal></p>
  <h2><a name="_Toc138932265"><span lang=en-DE>Brion</span></a></h2>
@@ -1439,10 +1439,10 @@
  <p class=MsoNormal><span lang=en-DE>The BSB reconstructs realistic neural
  circuits by placing and connecting fibres and neurons with detailed
  morphologies or only simplified geometrical features. Configure your model the
--way you need. Interfaces with several simulators (CoreNEURON, Arbor, NEST)
++way you need. Interfaces with several simulators (?CoreNEURON, ?Arbor, ?NEST)
  allow simulation of the reconstructed network and investigation of the
  structure-function-dynamics relationships at different levels of resolution.
--The 'scaffold' design allows an easy model reconfiguration reflecting variants
++The ÒscaffoldÓ design allows an easy model reconfiguration reflecting variants
  across brain regions, animal species and physio-pathological conditions without
  dismounting the basic network structure. The BSB provides effortless parallel
  computing both for the reconstruction and simulation phase.</span></p>
@@ -1452,8 +1452,8 @@
  <h2><a name="_Toc138932267"><span lang=en-DE>BSP Service Account</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>The BSP Service Account is a rest API
--service that allows developers to submit user's jobs on HPC systems and
--retrieve results using the EBRAINS authentication, even if users don't have a
++service that allows developers to submit userÕs jobs on HPC systems and
++retrieve results using the EBRAINS authentication, even if users donÕt have a
  personal account on the available HPC facilities.</span></p>
  <h2></h2>
@@ -1523,11 +1523,11 @@
  EBRAINS infrastructure provider) and storage resources there. This is the
  recommended storage for datasets that are shared by data providers, on the
  condition that these do not contain sensitive personal data. For sharing
--datasets with personal data, users should refer to the Health Data Cloud. The
++datasets with personal data, users should refer to the ?Health Data Cloud. The
  Bucket service is better suited for larger files that are usually not edited,
  such as datasets and videos. For Docker images, users should refer to the
  EBRAINS Docker registry. For smaller files and files which are more likely to
--be edited, users should consider the Collaboratory Drive service.</span></p>
++be edited, users should consider the ?Collaboratory Drive service.</span></p>
  <h2></h2>
@@ -1535,12 +1535,12 @@
  <p class=MsoNormal><span lang=en-DE>The Drive service offers users cloud
  storage space for their files in each collab (workspace). The Drive storage is
--mounted in the Collaboratory Lab to provide persistent storage (as opposed to
++mounted in the ?Collaboratory Lab to provide persistent storage (as opposed to
  the Lab containers which are deleted after a few hours of inactivity). All
  files are under version control. The Drive is intended for smaller files
  (currently limited to 1 GB) that change more often. Users must not save files
  containing personal information in the Drive (i.e. data of living human subjects).
--The Drive is also integrated with the Collaboratory Office service to offer
++The Drive is also integrated with the ?Collaboratory Office service to offer
  easy collaborative editing of Office files online.</span></p>
  <h2></h2>
@@ -1655,7 +1655,7 @@
  interest by downloading a few tiles rather than the entire large image. Tiles
  are also generated at coarser resolutions to support zooming out of large
  images. The service supports multiple input image formats. The serving of tiles
--to apps is provided by the Collaboratory Bucket (based on OpenStack Swift
++to apps is provided by the ?Collaboratory Bucket (based on OpenStack Swift
  object storage), which provides significantly higher network bandwidth than
  could be provided by any VM.</span></p>
@@ -1724,12 +1724,12 @@
  <h2><a name="_Toc138932288"><span lang=en-DE>fairgraph</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>fairgraph is a Python library for working
--with metadata in the EBRAINS Knowledge Graph (KG), with a particular focus on
++with metadata in the ?EBRAINS Knowledge Graph (KG), with a particular focus on
  data reuse, although it is also useful in registering and curating metadata.
  The library represents metadata nodes (also known as openMINDS instances) from
  the KG as Python objects. fairgraph supports querying the KG, following links
  in the graph, downloading data and metadata, and creating new nodes in the KG.
--It builds on openMINDS and on the KG Core Python library.</span></p>
++It builds on ?openMINDS and on the KG Core Python library.</span></p>
  <h2></h2>
@@ -1765,7 +1765,7 @@
  <p class=MsoNormal><span lang=en-DE>This tool was developed to calculate the
  local field potentials (LFP) and magnetoencephalogram (MEG) signals generated
  by a population of neurons described by a mean-field model. The calculation of
--LFP is done via a kernel method based on unitary LFP's (the LFP generated by a
++LFP is done via a kernel method based on unitary LFPÕs (the LFP generated by a
  single axon) which was recently introduced for spiking-networks simulations and
  that we adapt here for mean-field models. The calculation of the magnetic field
  is based on current-dipole and volume-conductor models, where the secondary
@@ -1853,7 +1853,7 @@
  (GDPR). The HDC is a federation of interoperable nodes. Nodes share a common
  system architecture based on CharitŽ Virtual Research Environment (VRE),
  enabling research consortia to manage and process data, and making data
--discoverable and sharable via the EBRAINS Knowledge Graph.</span></p>
++discoverable and sharable via the ?EBRAINS Knowledge Graph.</span></p>
  <p class=MsoNormal></p>
@@ -1919,7 +1919,7 @@
  <h2><a name="_Toc138932304"><span lang=en-DE>Insite</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>Insite enables users to access data via the
--in transit paradigm for NEST, TVB and Arbor simulations. Compared to the
++in transit paradigm for ?NEST, ?TVB and ?Arbor simulations. Compared to the
  traditional approach of offline processing, in transit paradigms allow
  accessing of data while the simulation runs. This is especially useful for
  simulations that produce large amounts of data and are running for a long time.
@@ -1949,7 +1949,7 @@
  requires detailed insights into how areas with specific gene activities and
  microanatomical architectures contribute to brain function and dysfunction. The
  Allen Human Brain Atlas contains regional gene expression data, while the
--Julich Brain Atlas, which can be accessed via siibra, offers 3D
++Julich Brain Atlas, which can be accessed via ?siibra, offers 3D
  cytoarchitectonic maps reflecting the interindividual variability. JuGEx offers
  an integrated framework that combines the analytical benefits of both
  repositories towards a multilevel brain atlas of adult humans. JuGEx is a new
@@ -1967,7 +1967,7 @@
  large-scale brain initiatives universally accessible and useful. It also
  promotes FAIR data principles that will help data publishers to follow best
  practices for data storage and publication. As more and more data publishers
--follow data standards like OpenMINDS or DATS, the quality of data discovery
++follow data standards like ?OpenMINDS or DATS, the quality of data discovery
  through KS will improve. The related publications are also curated from PubMed
  and linked to the concepts in KS to provide an improved search capability.</span></p>
@@ -2072,7 +2072,7 @@
  Acceleration Molecular Dynamics (RAMD) trajectories, it can help to investigate
  dissociation mechanisms by characterising transition states as well as the
  determinants and hot-spots for dissociation. As such, the combined use of
--RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
++??RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
  design of new molecules or ligand optimisation.</span></p>
  <h2></h2>
@@ -2227,14 +2227,14 @@
  <h2><a name="_Toc138932327"><span lang=en-DE>Multi-Brain</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>The Multi-Brain (MB) model has the
++<p class=MsoNormal><span lang=en-DE>&quot;The Multi-Brain (MB) model has the
  general aim of integrating a number of disparate image analysis components
  within a single unified generative modelling framework. Its objective is to
  achieve diffeomorphic alignment of a wide variety of medical image modalities
  into a common anatomical space. This involves the ability to construct a
--&quot;tissue probability template&quot; from a population of scans
++&quot;&quot;tissue probability template&quot;&quot; from a population of scans
  through group-wise alignment. The MB model has been shown to provide accurate
--modelling of the intensity distributions of different imaging modalities.</span></p>
++modelling of the intensity distributions of different imaging modalities.&quot;</span></p>
  <h2></h2>
@@ -2272,7 +2272,7 @@
  of morphological neuron models. These models can be simulated through an
  interface with the NEURON simulator or analysed with two classical methods: (i)
  the separation-of-variables method to obtain impedance kernels as a
--superposition of exponentials and (ii) Koch's method to compute impedances with
++superposition of exponentials and (ii) KochÕs method to compute impedances with
  linearised ion channels analytically in the frequency domain. NEAT also
  implements the neural evaluation tree framework and an associated C++ simulator
  to analyse sub-unit independence. Finally, NEAT implements a new method to
@@ -2314,7 +2314,7 @@
  visualising and analysing simulation results. NEST Desktop allows students to
  explore important concepts in computational neuroscience without the need to
  first learn a simulator control language. This is done by offering a
--server-side NEST simulator, which can also be installed as a package together
++server-side ?NEST simulator, which can also be installed as a package together
  with a web server providing NEST Desktop as visual front-end. Besides local
  installations, distributed setups can be installed, and direct use through
  EBRAINS is possible. NEST Desktop has also been used as a modelling front-end
@@ -2340,7 +2340,7 @@
  <p class=MsoNormal><span lang=en-DE>NESTML is a domain-specific language for
  neuron and synapse models. These dynamical models can be used in simulations of
--brain activity on several platforms, in particular NEST Simulator. NESTML
++brain activity on several platforms, in particular ?NEST Simulator. NESTML
  combines an easy to understand, yet powerful syntax with good simulation
  performance by means of code generation (C++ for NEST Simulator), but flexibly
  supports other simulation engines including neuromorphic hardware.</span></p>
@@ -2353,7 +2353,7 @@
  interfaces to develop data-driven multiscale brain neural circuit models using
  Python and NEURON. Users can define models using a standardised
  JSON-compatible, rule-based, declarative format. Based on these specifications,
--NetPyNE will generate the network in CoreNEURON, enabling users to run
++NetPyNE will generate the network in ?CoreNEURON, enabling users to run
  parallel simulations, optimise and explore network parameters through automated
  batch runs, and use built-in functions for visualisation and analysis (e.g.,
  generate connectivity matrices, voltage traces, spike raster plots, local field
@@ -2441,8 +2441,8 @@
  <p class=MsoNormal><span lang=en-DE>NeuroR is a collection of tools to repair
  morphologies.  This includes cut plane detection, sanitisation (removing
--unifurcations, invalid soma counts, short segments) and 'unravelling': the
--action of 'stretching' the cell that has been shrunk due to  the dehydratation
++unifurcations, invalid soma counts, short segments) and ÒunravellingÓ: the
++action of ÒstretchingÓ the cell that has been shrunk due to  the dehydratation
  caused by the slicing.</span></p>
  <h2></h2>
@@ -2479,7 +2479,7 @@
  <h2><a name="_Toc138932346"><span lang=en-DE>NeuroScheme</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>NeuroScheme uses schematic
++<p class=MsoNormal><span lang=en-DE>&quot;NeuroScheme uses schematic
  representations, such as icons and glyphs, to encode attributes of neural
  structures (neurons, columns, layers, populations, etc.), alleviating problems
  with displaying, navigating and analysing large datasets. It manages
@@ -2487,12 +2487,12 @@
  style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>users can
  navigate through the levels of the hierarchy and hone in on and explore the
  data at their desired level of detail. NeuroScheme has currently two built-in
--&quot;domains&quot;, which specify entities, attributes and
--relationships used for specific use cases: the 'cortex' domain, designed for
++&quot;&quot;domains&quot;&quot;, which specify entities, attributes and
++relationships used for specific use cases: the ÒcortexÓ domain, designed for
  navigating and analysing cerebral cortex structures</span><span lang=en-DE
  style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>and the
--'congen' domain, used to define the properties of cells and connections, create
--circuits of neurons and build populations.</span></p>
++ÒcongenÓ domain, used to define the properties of cells and connections, create
++circuits of neurons and build populations.&quot;</span></p>
  <h2></h2>
@@ -2521,13 +2521,13 @@
  of neural circuits consisting of large numbers of cells. It facilitates the
  recovery and visualisation of the 3D geometry of cells included in databases,
  such as NeuroMorpho, and allows to approximate missing information such as the
--soma's morphology.</span></p>
++somaÕs morphology.</span></p>
  <h2></h2>
  <h2><a name="_Toc138932349"><span lang=en-DE>NMODL Framework</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>NMODL Framework is designed with
++<p class=MsoNormal><span lang=en-DE>&quot;NMODL Framework is designed with
  modern compiler and code generation techniques. It provides modular tools for
  parsing, analysing and transforming NMODL it provides an easy to use, high
  level Python API</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
@@ -2534,7 +2534,7 @@
  </span><span lang=en-DE> it generates optimised code for modern compute architectures
  including CPUs and GPUs</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
  </span><span lang=en-DE> it provides flexibility to implement new simulator
--backends and it supports full NMODL specification.</span></p>
++backends and it supports full NMODL specification.&quot;</span></p>
  <h2></h2>
@@ -2643,7 +2643,7 @@
  <p class=MsoNormal><span lang=en-DE>The EBRAINS Provenance API is a web service
  to facilitate working with computational provenance metadata. Metadata are
--stored in the EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
++stored in the ?EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
  Provenance API provides a somewhat simplified interface compared to accessing
  the KG directly and performs checks of metadata consistency. The service covers
  workflows involving simulation, data analysis, visualisation, optimisation,
@@ -2655,8 +2655,8 @@
  <p class=MsoNormal><span lang=en-DE>A model description written with the PyNN
  API and the Python programming language runs on any simulator that PyNN
--supports (currently NEURON, NEST and Brian 2) as well as on the BrainScaleS
--and SpiNNaker neuromorphic hardware systems. PyNN provides a library of
++supports (currently NEURON, ?NEST and Brian 2) as well as on the ?BrainScaleS
++and ?SpiNNaker neuromorphic hardware systems. PyNN provides a library of
  standard neuron, synapse and synaptic plasticity models, verified to work the
  same on different simulators. PyNN also provides commonly used connectivity
  algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
@@ -2723,10 +2723,10 @@
  <p class=MsoNormal><span lang=en-DE>RateML enables users to generate
  whole-brain network models from a succinct declarative description, in which
  the mathematics of the model are described without specifying how their
--simulation should be implemented. RateML builds on NeuroML's Low Entropy Model
++simulation should be implemented. RateML builds on NeuroMLÕs Low Entropy Model
  Specification (LEMS), an XML-based language for specifying models of dynamical systems,
  allowing descriptions of neural mass and discretized neural field models, as
--implemented by the TVB simulator. The end user describes their model's
++implemented by the TVB simulator. The end user describes their modelÕs
  mathematics once and generates and runs code for different languages, targeting
  both CPUs for fast single simulations and GPUs for parallel ensemble
  simulations.</span></p>
@@ -2737,7 +2737,7 @@
  BrainÊCytoarchitectonic Atlas</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>Many studies have been investigating the
--relationships between interindividual variability in brain regions'
++relationships between interindividual variability in brain regionsÕ
  connectivity and behavioural phenotypes, by utilising connectivity-based
  prediction models. Recently, we demonstrated that an approach based on the
  combination of whole-brain and region-wise CBPP can provide important insight
@@ -2745,7 +2745,7 @@
  offering interpretable patterns. Here, we applied this approach using the
  Julich Brain Cytoarchitectonic Atlas with the resting-state functional
  connectivity and psychometric variables from the Human Connectome Project
--dataset, illustrating each brain region's predictive power for a range of
++dataset, illustrating each brain regionÕs predictive power for a range of
  psychometric variables. As a result, a psychometric prediction profile was
  established for each brain region, which can be validated against brain mapping
  literature.</span></p>
@@ -2830,7 +2830,7 @@
  <h2><a name="_Toc138932371"><span lang=en-DE>siibra-api</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>siibra-api provides an HTTP wrapper around
--siibra-python, allowing developers to access atlas (meta)data over HTTP
++?siibra-python, allowing developers to access atlas (meta)data over HTTP
  protocol. Deployed on the EBRAINS infrastructure, developers can access the
  centralised (meta)data on atlases, as provided by siibra-python, regardless of
  the programming language.</span></p>
@@ -2862,7 +2862,7 @@
  regional data features. It aims to facilitate programmatic and reproducible
  incorporation of brain parcellations and brain region features from different
  sources into neuroscience workflows. Also, siibra-python provides an easy
--access to data features on the EBRAINS Knowledge Graph in a well-structured
++access to data features on the ?EBRAINS Knowledge Graph in a well-structured
  manner. Users can preconfigure their own data to use within siibra-python.</span></p>
  <h2></h2>
@@ -2895,7 +2895,7 @@
  <h2><a name="_Toc138932376"><span lang=en-DE>Snudda</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>Snudda ('touch' in Swedish) allows the user
++<p class=MsoNormal><span lang=en-DE>Snudda (ÔtouchÕ in Swedish) allows the user
  to set up and generate microcircuits where the connectivity between neurons is
  based on reconstructed neuron morphologies. The touch detection algorithm looks
  for overlaps of axons and dendrites, and places putative synapses where they
@@ -2957,7 +2957,7 @@
  data and prior assumptions on parameter distributions) are stored in a
  structured, human- and machine-readable file format based on SBtab. The toolset
  enables simulations of the same model in simulators with different
--characteristics, e.g., STEPS, NEURON, MATLAB's Simbiology and R via automatic
++characteristics, e.g., STEPS, NEURON, MATLABÕs Simbiology and R via automatic
  code generation. The parameter estimation can include uncertainty
  quantification and is done by optimisation or Bayesian approaches. Model
  analysis includes global sensitivity analysis and functionality for analysing
@@ -2969,7 +2969,7 @@
  <p class=MsoNormal><span lang=en-DE>The Synaptic Events Fitting is a web
  application, implemented in a Jupyter Notebook on EBRAINS that allows users to
--fit synaptic events using data and models from the EBRAINS Knowledge Graph
++fit synaptic events using data and models from the ?EBRAINS Knowledge Graph
  (KG). Select, download and visualise experimental data from the KG and then choose
  the data to be fitted. A mod file is then selected (local or default) together
  with the corresponding configuration file (including protocol and the name of
@@ -2986,9 +2986,9 @@
  application, implemented via a Jupyter Notebook on EBRAINS, which allows to
  configure and test, through an intuitive GUI, different synaptic plasticity
  models and protocols on single cell optimised models, available in the EBRAINS
--Model Catalog. It consists of two tabs: 'Config', where the user can specify
--the plasticity model to use and the synaptic parameters, and 'Sim', where the
--recording location, weight's evolution and number of simulations to run are
++Model Catalog. It consists of two tabs: ÒConfigÓ, where the user can specify
++the plasticity model to use and the synaptic parameters, and ÒSimÓ, where the
++recording location, weightÕs evolution and number of simulations to run are
  defined. The results are plotted at the end of the simulation and the traces
  are available for download.</span></p>
@@ -3039,7 +3039,7 @@
  the simulation tools and adaptors connecting the data, atlas and computing
  services to the rest of the TVB ecosystem and Cloud services available in
  EBRAINS. As such it allows the user to find and fetch relevant datasets through
--the EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
++the ?EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
  models and use the HPC systems to perform parameter exploration, optimisation and
  inference studies. The user can orchestrate the workflow from the Jupyterlab
  interactive computing environment of the EBRAINS Collaboratory or use the
@@ -3065,7 +3065,7 @@
  <p class=MsoNormal><span lang=en-DE>The TVB Inversion package implements the
  machinery required to perform parameter exploration and  inference over
--parameters of The Virtual Brain simulator. It implements Simulation Based
++parameters of ?The Virtual Brain simulator. It implements Simulation Based
  Inference (SBI) which is a Bayesian inference method for complex models, where
  calculation of the likelihood function is either analytically or
  computationally intractable. As such, it allows the user to formulate with
@@ -3085,7 +3085,7 @@
  App uses private/public key cryptography, sandboxing, and access control to
  protect personalised health information contained in digital human brain twins
  while being processed on HPC. Users can upload their connectomes or use
--TVB-ready image-derived data discoverable via the EBRAINS Knowledge Graph.
++TVB-ready image-derived data discoverable via the ?EBRAINS Knowledge Graph.
  Users can also use containerised processing workflows available on EBRAINS to
  render image raw data into simulation-ready formats.</span></p>
@@ -3095,11 +3095,11 @@
  <p class=MsoNormal><span lang=en-DE>In order to support the usability of
  EBRAINS workflows, TVB-widgets has been developed as a set of modular graphic
--components and software solutions, easy to use in the Collaboratory within the
++components and software solutions, easy to use in the ?Collaboratory within the
  JupyterLab. These GUI components are based on and under open source licence,
  supporting open neuroscience and support features like: Setup of models and
  region-specific or cohort simulations. Selection of Data sources and their
--links to models. Querying data from siibra and the EBRAINS Knowledge Graph.
++links to models. Querying data from ?siibra and the ?EBRAINS Knowledge Graph.
  Deployment and monitoring jobs on HPC resources. Analysis and visualisation.
  Visual workflow builder for configuring and launching TVB simulations.</span></p>
@@ -3109,8 +3109,8 @@
  <p class=MsoNormal><span lang=en-DE>TVB-Multiscale is a Python toolbox aimed at
  facilitating the configuration of multiscale brain models and their
--co-simulation with TVB and spiking network simulators (currently NEST,
--NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
++co-simulation with TVB and spiking network simulators (currently ?NEST,
++?NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
  brain model formulated at the coarse scale of networks of tens up to thousands
  of brain regions, and an additional model of networks of spiking neurons
  describing selected brain regions at a finer scale. The toolbox has a
@@ -3139,7 +3139,7 @@
  Explorer are the core of an application suite designed to help scientists to
  explore their data. Vishnu 1.0 is a communication framework that allows them to
  interchange information and cooperate in real time. It provides a unique access
--point to the three applications and manages a database with the users'
++point to the three applications and manages a database with the usersÕ
  datasets. Vishnu was originally designed to integrate data for
  Espina.Whole-brain-scale tools.</span></p>
@@ -3162,7 +3162,7 @@
  <h2><a name="_Toc138932395"><span lang=en-DE>VisuAlign</span></a></h2>
  <p class=MsoNormal><span lang=en-DE>VisuAlign is a tool for user-guided
--nonlinear registration after QuickNII of 2D experimental image data, typically
++nonlinear registration after ?QuickNII of 2D experimental image data, typically
  high resolution microscopic images, to 3D atlas reference space, facilitating
  data integration through standardised coordinate systems. Key features:
  Generate user-defined cut planes through the atlas templates, matching the
@@ -3202,8 +3202,8 @@
  <h2><a name="_Toc138932398"><span lang=en-DE>WebAlign</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>WebAlign is the web version of QuickNII.
--Presently, it is available as a community app in the Collaboratory. Features
++<p class=MsoNormal><span lang=en-DE>WebAlign is the web version of ?QuickNII.
++Presently, it is available as a community app in the ?Collaboratory. Features
  include: Spatial registration of sectional image data. Generation of customised
  atlas maps for your sectional image data.</span></p>
@@ -3225,8 +3225,8 @@
  <h2><a name="_Toc138932400"><span lang=en-DE>WebWarp</span></a></h2>
--<p class=MsoNormal><span lang=en-DE>WebWarp is the web version of VisuAlign.
--Presently, it is available as a community app in the Collaboratory. Features
++<p class=MsoNormal><span lang=en-DE>WebWarp is the web version of ?VisuAlign.
++Presently, it is available as a community app in the ?Collaboratory. Features
  include: Nonlinear refinements of atlas registration by WebAlign of sectional
  image data. Generation of customised atlas maps for your sectional image data.</span></p>

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