The EBRAINS Collaboratory offers researchers and developers a secure environment to work with others. You control the level of collaboration by sharing your projects with specific users, teams or all of the Internet. Many researchers are sharing their work already; several services, tools, datasets, and other resources are publicly available, and many more are available for registered users.
The COMP Superscalar (COMPSs) framework is mainly composed of a task-based programming model which aims to ease the development of parallel applications for distributed infrastructures, such as Clusters, Clouds and containerized platforms, and a runtime system that exploits the inherent parallelism of applications at execution time. The framework is complemented by a set of tools for facilitating the development, execution monitoring and post-mortem performance analysis.
To better understand the relationships between interindividual variability in brain regions’ connectivity and behavioural phenotypes, we are developing a connectivity-based psychometric prediction framework (CBPP). Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general connectivity-based psychometric prediction (CBPP) framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations (3 types of preprocessing x 4 parcellation granularity x 2 connectivity methods x 3 regression methods = 72 combinations) in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach offer a framework that optimises both, prediction performance and neurobiological validity (and hence interpretability) for studying brain-behaviour relationships.
CoreNeuron supports a reduced set of the functionalities offered by the open source simulator NEURON. The software aims at supporting an efficient and scalable simulation of the electrical activity of neuronal networks that include morphologically detailed neurons. CoreNeuron has been implemented with the goal of minimising memory footprint and obtaining optimal performance, relying on the use of a single MPI process per node and 64 OpenMP threads on IBM BlueGene/Q systems.
Cube, which is used as performance report explorer for Scalasca and Score-P, is a generic tool for displaying a multi-dimensional performance space consisting of the dimensions (i) performance metric, (ii) call path, and (iii) system resource. Each dimension can be represented as a tree, where non-leaf nodes of the tree can be collapsed or expanded to achieve the desired level of granularity. In addition, Cube can display multi-dimensional Cartesian process topologies. The Cube 4.x series report explorer and the associated Cube4 data format is provided for Cube files produced with the Score-P performance instrumentation and measurement infrastructure or the Scalasca version 2.x trace analyzer (and other compatible tools). However, for backwards compatibility, Cube 4.x can also read and display Cube 3.x data.
CxSystem2 is a simulation framework for cortical networks, which operates on personal computers. It is implemented in Python on top of the popular Brian2 simulator, and runs on Linux, Windows and MacOS. There is also a web-based version available via the Human Brain Project Brain Simulation Platform (BSP). CxSystem2 embraces the main goal of Brian – minimizing development time – by providing the user with a simplified interface. While many simple models can be written in pure Brian code, more complex models can get hard to manage due to the large number of biological details. We currently provide two interfaces for constructing networks: a browser-based interface (locally or via the BSP), and a file-based interface (json or csv). Before incorporating neuron models into a network, the user can explore their behavior using the Neurodynlib submodule. Spike output and 3D structure of network simulations can be visualized using ViSimpl, a visualization tool developed by the GMRV Lab.
PostgreSQL relational database schema for tracking the provenance of data for all software components and files involved in the data transformation process. This project provides a Docker container including Alembic and a Python model of the Data Catalog schema to setup and migrate this schema in a target database.
DC Explorer focuses on statistical analysis of data subsets. In this regard, it provides a treemap visualization to facilitate the subset definition. Treemapping is used to visualize the filtering operations that define each subset by grouping the data in different compartments, color coding each item and sorting them by their value. Once the subsets have been defined different statistical tests are automatically performed in order to analyze relationship between the selected subsets.
DeepSlice is a python library which automatically aligns coronal mouse histology section images with the Allen brain atlas common coordinate framework. The alignments are viewable, and refinable, using the QuickNII software package.
Demics is a library for the Python programming language, adding support for distributed computational operations for very large, multi-dimensional arrays and matrices. Such operations include Deep Learning inference models from the libraries TensorFlow and PyTorch. The software focuses on the segmentation and detection of objects in particularly large image data.
deNEST is a Python library for specifying networks and running simulations using the NEST simulator. deNEST allows the user to concisely specify large-scale networks and simulations in hierarchically-organized declarative parameter files. From these parameter files, a network is instantiated in NEST (layers of neurons and stimulation devices, their connections, and recorder devices), and a simulation is run in sequential steps ("sessions"), during which the network parameters can be modified and the network can be stimulated, recorded, etc. Some advantages of the declarative approach: Parameters and code are separated Simulations are easier to reason about, reuse, and modify Parameters are more readable and succinct Parameter files can be easily version controlled and diffs are smaller and more interpretable Clean separation between the specification of the "network" (the simulated neuronal system) and the "simulation" (structured stimulation and recording of the network), which facilitates running different experiments using the same network Parameter exploration is more easily automated The complexity of interacting with NEST is hidden, which makes some tricky operations (such as connecting a weight_recorder) easy.
Distance-Fluctuation (DF) Analysis is a Python tool that performs analysis of the results of a MD simulation using the Distance Fluctuation matrices (DF), based on the Coordination Propensity (CP) hypothesis. Specifically, low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way. The script can analyze a MD trajectory and identify the coordinated motions between residues. It can then filter the output matrix based on the distance to identify long-range coordinated motions.
Distance-Fluctuation (DF) Analysis is a Python tool that performs analysis of the results of a MD simulation using the Distance Fluctuation matrices (DF), based on the Coordination Propensity (CP) hypothesis. Specifically, low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way. The script can analyze a MD trajectory and identify the coordinated motions between residues. It can then filter the output matrix based on the distance to identify long-range coordinated motions.
Library for the simulation of rate-based neuron models with conductance-based synapses in feedforward architectures. Supports synaptic plasticity. Both models with instantaneous membrane response and dynamic models are available. Dynamic models are integrated with Runge-Kutta 2/3 to increase stability for larger timesteps. For theoretical details see https://arxiv.org/abs/2104.13238.
The EBRAINS Collaboratory (initially known as Collaboratory 2.0) offers researchers and developers an environment to work in teams and share their work with users, teams or all of the Internet. Workspaces in the Collaboratory are known as collabs. The Collaboratory is composed of a collection of software for web services. The IAM service of the Collaboratory manages user identification and team management for EBRAINS services. Users can be grouped into units, groups and collab teams for simpler management. The Wiki service of the Collaboratory hosts the main interface to access all other Collaboratory services. It also offers a handy way of documenting your work with a simple wiki user interface. The Drive service offers each collab its own storage space for files. The drive provides easy access to files from Jupyter Notebooks. All files are under version control. The Drive is intended for smaller files that change more often. The Lab service provides a JupyterLab environment for your notebooks with official releases of EBRAINS tools pre-installed. It’s a great way of programming interactively and of sharing your notebooks with other users. The Office service handles Office documents (Word, PowerPoint or Excel) which can be edited collaboratively online. Whether it is for taking live minutes in a meeting or to finalize/review a report, the collaborative mode is very handy. The Bucket service offers each collab its own storage space for large files. The bucket provides programmatic access to files from Jupyter Notebooks via the bucket API. Datasets, videos and other files too large for the Drive should be stored here. The Chat service offers instant messaging with all users that have an EBRAINS account and that have entered the chat at least once. The chat offers channels, discussions and direct messaging. Client apps are available for desktop and mobile devices. Users that are not active in the Chat also receive notifications by email.
In this form, you will be asked to provide some information about yourself and your dataset, model, or software. This should take around 15 min. Please note that all information provided can be adjusted later on.
We built the EBRAINS Knowledge Graph (KG) to help you find and share the data you need to make your next discovery. We also built it to connect you to the software and hardware tools which will help you analyse the data you have and the data you find. The EBRAINS Knowledge Graph supports rich terminologies, ontologies and controlled vocabularies. The system is built by design to support iterative elaborations of common standards and supports these by probabilistic suggestion and review systems. The EBRAINS Knowledge Graph is a multi-modal metadata store which brings together information from different fields on brain research. At the core of the EBRAINS Knowledge Graph, a graph database tracks the linkage between experimental data and neuroscientific data science supporting more extensive data reuse and complex computational research than would be possible otherwise.
In this form, you can describe key aspects of your dataset so that other researchers will be able to find, reuse and cite your work. You can use the navigation bar above to explore the different sections and categories of metadata collected through this form.
Results: 37 - 54 of 252
EBRAINS is open and free. Sign up now for complete access to our tools and services.