The Genome Browser in the Cloud (GBiC) program is a convenient tool that automates the setup of a UCSC Genome Browser mirror​ on a cloud instance or a dedicated physical server.

This tutorial gives a demonstration of species/genome assembly selection page (Gateway) on the UCSC Genome Browser.

This tutorial demonstrates how to get the coordinates and sequences of exons using the UCSC Genome Browser.

This tutorial will demonstrate how to locate amino acid numbers for coding genes using the UCSC Genome Browser.

This tutorial will demonstrate how to find the tables in the UCSC database that are associated with the data tracks in the Genome Browser graphical viewer.

This tutorial shows how to navigate between exons of a gene using the UCSC Genome Browser.

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

This video will document the process of uploading data into a brainlife project using ezBIDS.

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 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 short video shows how a brainlife.io publication can be opened from the Data Deposition page of the journal Nature Scientific Data.

In this tutorial, you will learn the basic features of uploading and versioning your data within OpenNeuro.org.

This tutorial shows how to share your data in OpenNeuro.org.

Following the previous two tutorials on uploading and sharing data with OpenNeuro.org, this tutorial briefly covers how to run various analyses on your datasets.

This video introduces the key principles for data organization 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 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.

This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem. 

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.