This video will document the process of importing a dataset archived on OpenNeuro from the Datasets tab into a brainlife project.

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.

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

In this talk, you will learn how brainlife.io works, and how it can be applied to neuroscience data.

As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services. 

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 continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This talk describes the relevance and power of using brain atlases as part of one's data integration pipeline. 

In this lesson, you will learn how to understand data management plans and why data sharing is important. 

This quick visual walkthrough presents the steps required in uploading data into a brainlife project using the graphical user interface (GUI). 

This lesson describes and shows four different ways one may upload their data to brainlife.io.

This lecture covers why data sharing and other collaborative practices are important, how these practices are developed, and the challenges involved in their development and implementation.

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.

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

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.

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 brief video provides an introduction to the third session of INCF's Neuroinformatics Assembly 2023, focusing on how to streamling cross-platform data integration in a neuroscientific context. 

This talk describes the challenges to sustained operability and success of consortia, why many of these groups flounder after just a few years, and what steps can be taken to mitigate such outcomes. 

This talk discusses the BRAIN Initiative Cell Atlas Network (BICAN), taking a look specifically at how this network approaches the design, development, and maintenance of specimen and sequencing library portals. 

In this talk, you will hear about the challenges and costs of being FAIR in the many scientific fields, as well as opportunities to transform the ecology of the academic crediting system.