Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This module covers some basic anatomy such as the brain’s major divisions (brainstem, cerebellum, cerebrum), the cerebral lobes (frontal, temporal, parietal, and occipital), the central and peripheral nervous systems, theories of cognition, and brain orientation terms.

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. 

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

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

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

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

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

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.

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. 

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.

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

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.

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

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

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