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 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 tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.
This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).
This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.
This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.
This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.
This video will document the process of creating a pipeline rule for batch processing on brainlife.
This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.
This brief video walks you through the steps necessary when creating a project on brainlife.io.
This brief video rus through how to make an accout on brainlife.io.
This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.
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.
As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services.
This lecture provides an introduction to the study of eye-tracking in humans.
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 lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.