This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.
This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.
This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.
This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized.
In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte.
This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.
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 lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.
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.
Learn how to create a standard extracellular electrophysiology dataset in NWB using Python.
Learn how to create a standard calcium imaging dataset in NWB using Python.
In this tutorial, you will learn how to create a standard intracellular electrophysiology dataset in NWB using Python.
In this tutorial, you will learn how to use the icephys-metadata extension to enter meta-data detailing your experimental paradigm.