Hierarchical Event Descriptors (HED) fill a major gap in the neuroinformatics standards toolkit, namely the specification of the nature(s) of events and time-limited conditions recorded as having occurred during time series recordings (EEG, MEG, iEEG, fMRI, etc.). Here, the HED Working Group presents an online INCF workshop on the need for, structure of, tools for, and use of HED annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. 

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.

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

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

This lesson consists of a brief discussion around this sessions previous talks. 

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

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. 

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

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. 

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

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.

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

This lesson serves a primer on elementary algebra.

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

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

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

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

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.

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

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.