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

Difficulty level: Beginner
Duration: 14:24
Speaker: : Heidi Kleven

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

Difficulty level: Beginner
Duration: 32:18
Speaker: : Ariel Rokem

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

Difficulty level: Beginner
Duration: 1:16:36
Speaker: : Tal Yarkoni

This lecture gives an introduction to the FAIR (findability, accessibility, interoperability, and reusability) science principles and examples of their application in neuroscience research. 

Difficulty level: Beginner
Duration: 55:57

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

Difficulty level: Beginner
Duration: 1:25:17
Speaker: : Fernando Perez

This lecture covers the description and brief history of data science and its use in neuroinformatics.

Difficulty level: Beginner
Duration: 11:15
Speaker: : Ariel Rokem

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

Difficulty level: Beginner
Duration: 10:14
Speaker: : Franco Pestilli
Course:

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

Difficulty level: Beginner
Duration: 1:01:39
Speaker: : Thomas Mock

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. 

Difficulty level: Beginner
Duration: 52:26

This lecture provides an overview of successful open-access projects aimed at describing complex neuroscientific models, and makes a case for expanded use of resources in support of reproducibility and validation of models against experimental data.

Difficulty level: Beginner
Duration: 1:00:39
Speaker: : Sharon Crook

This lecture covers the biomedical researcher's perspective on FAIR data sharing and the importance of finding better ways to manage large datasets.

Difficulty level: Beginner
Duration: 10:51
Speaker: : Adam Ferguson

This lecture covers multiple aspects of FAIR neuroscience data: what makes it unique, the challenges to making it FAIR, the importance of overcoming these challenges, and how data governance comes into play.

Difficulty level: Beginner
Duration: 14:56
Speaker: : Damian Eke

This lecture covers the processes, benefits, and challenges involved in designing, collecting, and sharing FAIR neuroscience datasets.

Difficulty level: Beginner
Duration: 11:35

This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

Difficulty level: Beginner
Duration: 11:20
Speaker: : Elizabeth DuPre

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.

Difficulty level: Beginner
Duration: 12:05
Speaker: : Joanne Kamens

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

Difficulty level: Beginner
Duration: 3:58
Speaker: : Kenji Doya

This session covers the framework of the International Brain Lab (IBL) and the data architecture used for this project.

Difficulty level: Beginner
Duration: 23:37
Speaker: : Kenneth Harris

The Open Science Framework (OSF) provides avenues for researchers to design a study, as well as collect, analyze, and store data, manage collaborators, and publish research materials. In this webinar, attendees will learn about the many features of the OSF and develop strategies for using the tool within the context of their own research projects. The discussion will be framed around how to best utilize the OSF while also implementing data management and open science best practices.

Difficulty level: Beginner
Duration: 1:09:33
Speaker: : Kevin Read