This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.

This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

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

In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research. 

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

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

This lecture covers various learning theories.

This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.