This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

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 covers visualizing extracellular neurotransmitter dynamics

This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information.

This lesson provides an overview of Neurodata Without Borders (NWB), an ecosystem for neurophysiology data standardization. The lecture also introduces some NWB-enabled tools. 

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

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

This lecture provides an introduction to the study of eye-tracking in humans. 

This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

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 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 lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas.