This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

This short walkthrough documents the steps needed to find a dataset in OpenNeuro, a free and open platform for sharing MRI, MEG, EEG, iEEG, ECoG, ASL, and PET data, and import it directly to a brainlife project. 

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 lesson provides a brief visual walkthrough on the necessary steps when copying data from one brainlife project to another. 

This lesson visually documents the process of uploading data to brainlife via the command line interface (CLI). 

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.

This brief video walks you through the steps necessary when creating a project on brainlife.io. 

This brief video rus through how to make an accout on brainlife.io.

This video will demonstrate how to create and launch a pipeline using FreeSurfer on brainlife.io.

This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.

This video will document the process of importing a dataset archived on OpenNeuro from the Datasets tab into a brainlife project.

This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

In this talk, you will learn how brainlife.io works, and how it can be applied to neuroscience data.

As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services. 

This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles. 

This lecture covers structured data, databases, federating neuroscience-relevant databases, and ontologies. 

This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.