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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. 

     

    Difficulty level: Beginner
    Duration: 03:37:42
    Speaker: :

    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 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

    This brief video provides a welcome and short introduction to the outline of the INCF Short Course in Neuroinformatics, held Seattle, Washington in October 2023, in coordination with the West Big Data Hub and the University of Washington. 

    Difficulty level: Beginner
    Duration: 4:58
    Speaker: : Ariel Rokem

    This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience. 

    Difficulty level: Beginner
    Duration: 1:19:14
    Speaker: : Maryann Martone

    This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

    Difficulty level: Beginner
    Duration: 59:21
    Speaker: : Alla Borisyuk

    This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

    Difficulty level: Beginner
    Duration: 54:58
    Speaker: : Franco Pestilli

    This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results. 

    Difficulty level: Beginner
    Duration: 44:41

    Introduction of the Foundations of Machine Learning in Python course - Day 01.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Beginner
    Duration: 35:24
    Speaker: : Elena Trunz

    Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 34:52
    Speaker: : Moritz Wolter

    Linear Algebra for Machine Learning - Day 03 lecture of the Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 57.45
    Speaker: : Moritz Wolter

    Support Vector Machines -  Day 06 lecture of the  Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 53.39
    Speaker: : Elena Trunz

    Decision Trees and Random Forests -  Day 07 lecture of the  Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 1:15:39
    Speaker: : Elena Trunz

    Clustering and Density Estimation -  Day 08 lecture of the  Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 59:35
    Speaker: : Elena Trunz