The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from methods like functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI). Additional tools such as tractography and parcellation are discussed in the context of brain connectivity and mental health. The tutorial leads participants through the computation of brain connectomes from fMRI data. 

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

This course contains videos, lectures, and hands-on tutorials as part of INCF's Neuroinformatics Assembly 2023 workshop on developing robust and reproducible research workflows to foster greater collaborative efforts in neuroscience.

This course provides several visual walkthroughs documenting how to execute various processes in brainlife.io, an open-source, free and secure reproducible neuroscience analysis platform. The platform allows to analyze Magnetic Resonance Imaging (MRI), electroencephalography (EEG) and magnetoencephalography (MEG) data. Data can either be uploaded from local computers or imported from public archives such as OpenNeuro.org.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation. 

This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.

The Neurodata Without Borders: Neurophysiology project (NWB:N, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

This short course covers Hypothes.is, an annotation tool that enables users to collaboratively annotate course readings and other internet resources.

Features of Hypothes.is:

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible, Interoperable and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

Get up to speed about the fundamental principles of full brain network modeling using the open-source neuroinformatics platform The Virtual Brain (TVB). This simulation environment enables the biologically realistic modeling of whole-brain network dynamics across different brain scales, using personalized structural connectome-based approach.

The UCSC Genome Browser is an online and downloadable genome browser hosted by the University of California, Santa Cruz (UCSC). It is an interactive website offering access to genome sequence data from a variety of vertebrate and invertebrate species and major model organisms, integrated with a large collection of aligned annotations.

This brief course consists of slides on data science and reproducibility issues from lectures given at Maastricht University. 

In this course, you will learn about working with calcium-imaging data, including image processing to remove background "blur", identifying cells based on threshold spatial contiguity, time-series filtering, and principal component analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.