This lecture provides an overview of successful open-access projects aimed at describing complex neuroscientific models, and makes a case for expanded use of resources in support of reproducibility and validation of models against experimental data.
This lesson provides an overview of Neurodata Without Borders (NWB), an ecosystem for neurophysiology data standardization. The lecture also introduces some NWB-enabled tools.
This lesson gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hands-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.
This lesson provides a short overview of the main features of the Canadian Open Neuroscience Platform (CONP) Portal, 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.
This talk 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 lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.
In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle.
This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy.
In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.
This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication.
In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
In this lesson, users can follow along as a spaghetti script written in MATLAB is turned into understandable and reusable code living happily in a powerful GitHub repository.
This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.
This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.
This lesson discusses the need for and approaches to integrating data across the various temporal and spatial scales in which brain activity can be measured.
This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation.
In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data.
This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing.
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.