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.

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.

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.

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 FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools. Rather than rigid distinctions, in the data science of neuroinformatics, these activities and approaches intersect and interact in dynamic ways. Together with a panel of cutting-edge neuro-data-scientist speakers, we will explore these dynamics

This lecture covers how brainlife.io works, and how it can be applied to neuroscience data.

As a part of NeuroHackademy 2020, Tara Madhyastha (University of Washington), Andrew Crabb (AWS), and Ariel Rokem (University of Washington) give a lecture on Cloud Computing, focusing on Amazon Web Services. 

This video is provided by the University of Washington eScience Institute.

In this presentation by the OHBM OpenScienceSIG, Tom Shaw and Steffen Bollmann cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. They demonstrate how to build docker containers from scratch, using Neurodocker, and cover how to use containers on an HPC with singularity.

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.

This module explores sensation in the brain: what organs are involved, sensory pathways, processing centers, and theories of integration. We cover sensory transduction, vision, audition olfaction, gustation, and somatosensation.

This module covers how the brain interacts with the world through motor movements. Motor movements underlie so much of our functioning, our speech, the opening and closing of our eyes, and the beating of our hearts. We’ll learn about areas of the brain involved in movement and some of its pathways.

This module covers the structure and function of the neuron, its components and mechanisms, action potentials, and the many glial cells that support it.

This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information. We will also learn about physiological phenomena of the brain such as synchronicity that gives rise to brain waves.

This lecture provides an introduction to the study of eye-tracking in humans. 

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

How genetics can contribute to our understanding of psychiatric phenotypes.

The landscape of scientific research is changing. Today’s researchers need to participate in large-scale collaborations, obtain and manage funding, share data, publish, and undertake knowledge translation activities in order to be successful. As per these increasing demands, Science Management is now a vital piece of the environment.

Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives. This session will focus on the question of FAIRness in neuroimaging research touching on each of the FAIR elements through brief vignettes of ongoing research and challenges faced by the community to enact these principles. This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives. This session will focus on the question of FAIRness in neuroimaging research touching on each of the FAIR elements through brief vignettes of ongoing research and challenges faced by the community to enact these principles. This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives.

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.