This lesson gives a description of the BrainHealth Databank, a repository of many types of health-related data, whose aim is to accelerate research, improve care, and to help better understand and diagnose mental illness, as well as develop new treatments and prevention strategies. 

This lesson corresponds to slides 46-78 of the PDF below. 

This lesson describes not only the need for precision medicine, but also the current state of the methods, pharmacogenetic approaches, utility and implementation of such care today.

This lesson corresponds to slides 1-50 of the PowerPoint below. 

This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

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 launching a Jupyter Notebook for group-level analyses directly from brainlife.

This lesson consists of a talk about the history and future of academic publishing and the need for transparency, as well as a live demo of an alpha version of NeuroLibre, a preprint server that goes beyond the PDF to complement research articles. This video was part of a virutal QBIN SciComm seminar.

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. 

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

This lecture explains the need for data federation in medicine and how it can be achieved.