Research Resource Identifiers (RRIDs) are ID numbers assigned to help researchers cite key resources (e.g., antibodies, model organisms, and software projects) in biomedical literature to improve the transparency of research methods.
This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.
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 tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.
The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).
In this tutorial, you will learn how to run a typical TVB simulation.
This tutorial introduces The Virtual Mouse Brain (TVMB), walking users through the necessary steps for performing simulation operations on animal brain data.
In this tutorial, you will learn the necessary steps in modeling the brain of one of the most commonly studied animals among non-human primates, the macaque.
This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.
In this lecture, you will learn about various neuroinformatic resources which allow for 3D reconstruction of brain models.
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.