Course:

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.

Difficulty level: Intermediate

Duration: 1:47:22

Speaker: : Erin Dickie and John Griffiths

Course:

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.

Difficulty level: Intermediate

Duration: 1:39:04

Speaker: : Erin Dickie and John Griffiths

Course:

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.

Difficulty level: Intermediate

Duration: 50:44

Speaker: : Caterina Gratton

Course:

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced

Duration: 50:28

Speaker: : Pierre Bellec

This lesson provides an introduction the International Neuroinformatics Coordinating Facility (INCF), its mission towards FAIR neuroscience, and future directions.

Difficulty level: Beginner

Duration: 20:29

Speaker: : Maryann Martone

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 consists of a brief discussion around this sessions previous talks.

Difficulty level: Beginner

Duration: 12:43

This is the third and final lecture of this course on neuroinformatics infrastructure for handling sensitive data.

Difficulty level: Beginner

Duration: 1:11:22

Speaker: : Michael Schirner

In this lecture, you will learn about virtual research environments (VREs) and their technical limitations, (i.e., a computing platform and the software stack behind it) and the security measures which should be considered during implementation.

Difficulty level: Beginner

Duration: 1:06:50

Speaker: : Marc Sacks

This lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop *Research Workflows for Collaborative Neuroscience*.

Difficulty level: Beginner

Duration: 25:33

Speaker: :

This brief talk outlines the obstacles and opportunities involved in striving for more open and reproducible publishing, highlighting the need for investment in the technical and governance sectors of FAIR data and software.

Difficulty level: Beginner

Duration: 8:38

Speaker: : Jean-Babtiste Poline

This lecture gives an introduction to the INCF Short Course: Introduction to Neuroinformatics.

Difficulty level: Beginner

Duration: 34:27

Speaker: : Marja-Leena Linne

Course:

Presented by the OHBM OpenScienceSIG, this lesson covers how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers.

Difficulty level: Beginner

Duration: 01:21:59

Speaker: : Tom Shaw & Steffen Bollmann

Course:

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

Course:

This lesson serves a primer on elementary algebra.

Difficulty level: Beginner

Duration: 3:03

Speaker: : Barton Poulson

Course:

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

Course:

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

Course:

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

Difficulty level: Beginner

Duration: 4:17

Speaker: : Barton Poulson

Course:

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

Difficulty level: Beginner

Duration: 8:43

Speaker: : Barton Poulson

Course:

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

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