This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines. 

This video will document the process of uploading data into a brainlife project using ezBIDS.

This brief video walks you through the steps necessary when creating a project on brainlife.io. 

This quick video presents some of the various visualizers available on brainlife.io

This brief video rus through how to make an accout on brainlife.io.

This lesson consists of a demonstration of the BRIAN Simulator. BRIAN is a free, open-source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms, and is designed to be easy to learn and use, highly flexible, and easily extensible.

This lesson provides a demonstration of NeuroFedora, a volunteer-driven initiative to provide a ready-to-use Fedora-based free and open-source software platform for neuroscience. By making the tools used in the scientific process easier to use, NeuroFedora aims to aid reproducibility, data sharing, and collaboration in the research community.The CompNeuro Fedora Lab was specially to enable computational neuroscience.

This lesson provides an introduction and live demonstration of neurolib, a computational framework for simulating coupled neural mass models written in Python. Neurolib provides a simulation and optimization framework which allows you to easily implement your own neural mass model, simulate fMRI BOLD activity, analyse the results and fit your model to empirical data.

In this lesson, you will learn about the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks. GeNN is an open-source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users.

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

This video demonstrates how to find, access, and download data on EBRAINS.

This lesson gives a tour of how popular virtualization tools like Docker and Singularity are playing a crucial role in improving reproducibility and enabling high-performance computing in neuroscience.

This lesson gives a demonstration of how to use SciUnit, a Pythonic framework for data-driven unit testing that separates the interface from the implementation, respecting the diversity of conventions for modeling and data collection.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

The Allen Mouse Brain Atlas is a genome-wide, high-resolution atlas of gene expression throughout the adult mouse brain. This tutorial describes the basic search and navigation features of the Allen Mouse Brain Atlas.

The Allen Developing Mouse Brain Atlas is a detailed atlas of gene expression across mouse brain development. This tutorial describes the basic search and navigation features of the Allen Developing Mouse Brain Atlas.

This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain, as well as to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.

This lesson provides a demonstration of GeneWeaver, a system for the integration and analysis of heterogeneous functional genomics data.

This tutorial shows how to use the UCSC genome browser to find a list of genes in a given genomic region.