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This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles. 

Difficulty level: Beginner
Duration: 25:44
Speaker: : Fahim Imam

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

Difficulty level: Intermediate
Duration: 33:41

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

Difficulty level: Intermediate
Duration: 50:18
Speaker: : Jeff Grethe
Course:

This lecture covers structured data, databases, federating neuroscience-relevant databases, and ontologies. 

Difficulty level: Beginner
Duration: 1:30:45
Speaker: : Maryann Martone

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 lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate
Duration: 21:54

This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.

Difficulty level: Beginner
Duration: 2:53
Speaker: : Barton Poulson

This lesson serves a primer on elementary algebra.

Difficulty level: Beginner
Duration: 3:03
Speaker: : Barton Poulson

This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science. 

Difficulty level: Beginner
Duration: 5:38
Speaker: : Barton Poulson

In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.

Difficulty level: Beginner
Duration: 5:24
Speaker: : Barton Poulson

This talk gives a primer on calculus, emphasizing its role in data science.

Difficulty level: Beginner
Duration: 4:17
Speaker: : Barton Poulson

This lesson clarifies how calculus relates to optimization in a data science context. 

Difficulty level: Beginner
Duration: 8:43
Speaker: : Barton Poulson

This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.

Difficulty level: Beginner
Duration: 5:19
Speaker: : Barton Poulson

This lesson serves as a primer on the fundamental concepts underlying probability. 

Difficulty level: Beginner
Duration: 7:33
Speaker: : Barton Poulson

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.

Difficulty level: Beginner
Duration: 1:00:07
Speaker: : Shawn Grooms

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.

Difficulty level: Beginner
Duration: 1:21:30
Speaker: :

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

Difficulty level: Beginner
Duration: 1:02:29
Speaker: : Nir Giladi

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 is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized. 

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : John Griffiths

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

 

This lesson corresponds to slides 65-90 of the PDF below. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke