This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience.  FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible,  Interoperable and Reusable.  But FAIR is not a specification;  it leaves many of the specifics up to individual scientific disciplines to define.  INCF has been leading the way in promoting, defining and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been successes?  What is currently very difficult? Where does neuroscience need to go?

This lecture will provide an overview of Addgene, a tool that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of Addgene, their mission, and available resources.

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community. This lecture provides an overview and demo of the Canadian Open Neuroscience Platform (CONP).

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community

This lecture covers the IBI Data Standards and Sharing Working Group, including its history, aims, and projects.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community. This session covers the framework of the International Brain Lab (IBL) and the data architecture used for this project.

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.

This lecture provides an overview of The Virtual Brain Simulation Platform.

Demo of the BRIAN Simulator. BRIAN is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.

NeuroFedora is a volunteer driven initiative to provide a ready to use Fedora based Free/Open Source Software platform for neuroscience. We believe that similar to Free Software, science should be free for all to use, share, modify, and study. The use of Free Software also aids reproducibility, data sharing, and collaboration in the research community. By making the tools used in the scientific process easier to use, NeuroFedora aims to take a step to enable this ideal. The CompNeuro Fedora Lab was specially to enable computational neuroscience. It includes everything you will need to get your work done—modelling software, analysis tools, general productivity tools—all well integrated with the modern GNOME platform to give you a complete operating system.

neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area. This network model can be used to simulate whole-brain dynamics. neurolib provides a simulation and optimization framework which allows you to easily implement your own neural mass model, simulate fMRI BOLD activity, analyse the results and fit your model to empirical data.

GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users.

This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

This video introduces the key principles for data organisation and explains how you could make your data FAIR for data sharing on EBRAINS.

This video explains what metadata is, why it is important, and how you can organise your metadata to increase the FAIRness of your data on EBRAINS.

This video introduces the importance of writing a Data Descriptor to accompany your dataset on EBRAINS. It gives concrete examples on what information to include and highlights how this makes your data more FAIR.

This video demonstrates how to find, access, and download data on EBRAINS.