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

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 explains what metadata is, why it is important, and how you can organize your metadata to increase the FAIRness of your data on EBRAINS.

This video introduces the importance of writing a Data Descriptor to accompany your dataset on EBRAINS. It gives concrete examples on what information to include and highlights how this makes your data more FAIR.

In this lesson, users will learn about the importance of proper citation of software resources and tools used in neuroscientific research. 

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.

This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

This lesson recaps why math, in a number of ways, is extremely useful in data science.

This lesson provides an introduction to the lessons in this course that deal with statistics and why they are useful for data science.

In this lesson, users will learn about the importance of exploratory analysis, as well as how statistics enables one to become familiar with and understand one's data. 

This lesson goes over graphical data exploration, including motivations for its use as well as practical examples of visualizing data distributions. 

In this lesson, users learn about exploratory statistics, and are introduced to various methods for numerical data exploration.

This lesson overview some simple descriptions of statistical data.

This lesson covers the basics of hypothesis testing.

In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.

This lesson describes the Neuroscience Gateway , which facilitates access and use of National Science Foundation High Performance Computing resources by neuroscientists.

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.