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FAIR principles and methods currently in development for assessing FAIRness.

Difficulty level: Beginner
Duration:
Speaker: : Michel Dumontier

Tutorial describing the basic search and navigation features of the Allen Mouse Brain Atlas

Difficulty level: Beginner
Duration: 6:40
Speaker: : Unknown

Tutorial describing the basic search and navigation features of the Allen Developing Mouse Brain Atlas

Difficulty level: Beginner
Duration: 6:35
Speaker: : Unknown

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 and 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.

Difficulty level: Beginner
Duration: 6:31
Speaker: : Unknown
Course:

The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.

Difficulty level: Beginner
Duration: 55:36
Speaker: : Elissa Chesler

GeneWeaver is a web application for the integrated cross-species analysis of functional genomics data to find convergent evidence from heterogeneous sources. The application consists of a large database of gene sets curated from multiple public data resources and curated submissions, along with a suite of analysis tools designed to allow flexible, customized workflows through web-based interactive analysis or scripted API driven analysis. Gene sets come from multiple widely studied species and include ontology annotations, brain gene expression atlases, systems genetic study results, gene regulatory information, pathway databases, drug interaction databases and many other sources. Users can retrieve, store, analyze and share gene sets through a graded access system. Analysis tools are based on combinatorics and statistical methods for comparing, contrasting and classifying gene sets based on their members.

Difficulty level: Beginner
Duration: 25:53
Speaker: :

Félix-Antoine Fortin from Calcul Québec gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hand-on tutorial.  Though some examples might seem specific to the Calcul Québec, all computing clusters in the Compute Canada network share the same software modules and environments.

 

The lesson was given in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 02:49:34
Speaker: :

The Canadian Open Neuroscience Platform (CONP) Portal is a web interface that facilitates open science for the neuroscience community by simplifying global access to and sharing of datasets and tools. The Portal internalizes the typical cycle of a research project, beginning with data acquisition, followed by data processing with published tools, and ultimately the publication of results with a link to the original dataset.

 

In this video, Samir Das and Tristan Glatard give a short overview of the main features of the CONP Portal.

Difficulty level: Beginner
Duration: 14:03
Speaker: :

Shawn Brown presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance-computing facilities across Canada and around the world.

 

This talk was given in the context of a Ludmer Centre event in 2019.

 

 

Difficulty level: Beginner
Duration: 56:07
Speaker: :

This course will teach you AWS basics right through to advanced cloud computing concepts. There are lots of hands-on exercises using an AWS free tier account to give you practical experience with Amazon Web Services. Visual slides and animations will help you gain a deep understanding of Cloud Computing.

 

This lesson is courtesy of freeCodeCamp.

Difficulty level: Beginner
Duration: 05:27:20
Speaker: :

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

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.  INCF has been leading the way in promoting, defining, and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry, and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been the successes?  What is currently very difficult? Where does neuroscience need to go?

 

This lecture covers FAIR atlases, from their background, their construction, and how they can be created in line with the FAIR principles.

Difficulty level: Beginner
Duration: 14:24
Speaker: : Heidi Kleven

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

Difficulty level: Beginner
Duration: 15:14

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

Difficulty level: Beginner
Duration: 14:19
Speaker: : Michael Denker

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

Difficulty level: Beginner
Duration: 8:11
Speaker: : Thomas Wachtler
Course:

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

 

 

Difficulty level: Beginner
Duration: 38:36
Course:

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.

 

This lecture provides an overview of The Virtual Brain Simulation Platform.

 

Difficulty level: Beginner
Duration: 9:36
Speaker: : Petra Ritter

Demo 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. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.

Difficulty level: Beginner
Duration: 1:27:32
Speaker: : Marcel Stimberg

NeuroFedora is a volunteer driven initiative to provide a ready to use Fedora based Free/Open Source Software platform for neuroscience. We believe that similar to Free Software, science should be free for all to use, share, modify, and study. The use of Free Software also aids reproducibility, data sharing, and collaboration in the research community. By making the tools used in the scientific process easier to use, NeuroFedora aims to take a step to enable this ideal. The CompNeuro Fedora Lab was specially to enable computational neuroscience. It includes everything you will need to get your work done—modelling software, analysis tools, general productivity tools—all well integrated with the modern GNOME platform to give you a complete operating system.

Difficulty level: Beginner
Duration: 1:06:08
Speaker: : Ankur Sinha

neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area. This network model can be used to simulate whole-brain dynamics. 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.

Difficulty level: Beginner
Duration: 1:06:53
Speaker: : Çağlar Çakan