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 contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

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

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

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

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 explains what metadata is, why it is important, and how you can organize 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.

In this lesson, users will learn about the importance of proper citation of software resources and tools used in neuroscientific research. 

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.

This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).

This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.

This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.

This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.