This course is currently under construction but will coming soon.  It will give an overview of the world of scientific publishing, spanning from traditional formats, to open to access, to open, interactive, reproducible, and 'living' publications with modifiable and executable code.

This course consists of brief tutorials on OpenNeuro.org, a free and open platform for analyzing and sharing neuroimaging data. During this course, you will learn how to deal with your neuroscientific datasets using OpenNeuro.org for operations such as uploading and version control, as well as how to analyze and share your data. 

This course includes two tutorials on R, a programming language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

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 corresponds to the first session of talks given at INCF's Neuroinformatics Assembly 2023. The sessions consists of several lectures, focusing on using the principles of FAIR (findability, accessibility, interoperability, and reusability) to inform future directions in neuroscience and neuroinformatics. In particular, these talks deal with the development of knowledge graphs and ontologies. 

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.

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

This module is intended to provide a foundation in energy-based models. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course), Parameter Sharing (module 2 of the course),

Ethical conduct of science, good governance of data, and accelerated translation to the clinic are key to high-calibre open neuroscience.  Everyday practitioners of science must be sensitized to a range of ethical considerations in their research, some having especially to do with open data-sharing. The lessons included in this course introduce a number of these topics and end with concrete guidance for participant consent and de-identification of data.

This course corresponds to the first session of talks given at INCF's Neuroinformatics Assembly 2023. The sessions consists of several lectures, focusing on using the principles of FAIR (findability, accessibility, interoperability, and reusability) to inform future directions in neuroscience and neuroinformatics. In particular, these talks deal with the development of knowledge graphs and ontologies. 

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 provides basic knowledge on personalized brain network modeling using the open-source simulation platform The Virtual Brain (TVB). Participants will gain theoretical knowledge and apply this knowledge to construct brain models, process multimodal neuroimaging data for reconstructing individual brains, run simulations, and use supporting neuroinformatics tools such as collaboratories, pipelines, workflows, and data repositories.

The Neurodata Without Borders: Neurophysiology project (NWB, 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 course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.

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.

Neurohackademy is a two-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute. Participants learn about technologies used to analyze human neuroscience data, and to make analyses and results shareable and reproducible.

The Virtual Brain EduPack provides didactic use cases for The Virtual Brain (TVB). Typically a use case consists of a jupyter notebook and a didactic video. EduPack use cases help the user to reproduce TVB-based publications or to get started quickly with TVB.

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.

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.