This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

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

This lesson introduces the EEGLAB toolbox, as well as motivations for its use.

In this lesson, you will learn about the biological activity which generates and is measured by the EEG signal.

This lesson goes over the characteristics of EEG signals when analyzed in source space (as opposed to sensor space). 

This lesson describes the development of EEGLAB as well as to what extent it is used by the research community.

This lesson provides instruction as to how to build a processing pipeline in EEGLAB for a single participant. 

Whereas the previous lesson of this course outlined how to build a processing pipeline for a single participant, this lesson discusses analysis pipelines for multiple participants simultaneously. 

In addition to outlining the motivations behind preprocessing EEG data in general, this lesson covers the first step in preprocessing data with EEGLAB, importing raw data. 

Continuing along the EEGLAB preprocessing pipeline, this tutorial walks users through how to import data events as well as EEG channel locations.

This tutorial instructs users how to visually inspect partially pre-processed neuroimaging data in EEGLAB, specifically how to use the data browser to investigate specific channels, epochs, or events for removable artifacts, biological (e.g., eye blinks, muscle movements, heartbeat) or otherwise (e.g., corrupt channel, line noise). 

This tutorial provides instruction on how to use EEGLAB to further preprocess EEG datasets by identifying and discarding bad channels which, if left unaddressed, can corrupt and confound subsequent analysis steps. 

Users following this tutorial will learn how to identify and discard bad EEG data segments using the MATLAB toolbox EEGLAB. 

This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.  

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 provides a short reel on who we are, what we're doing and why we're doing it.

In this webinar, educators currently implementing collaborative annotation in their classrooms discuss their experiences with collaborative annotation and using Hythothes.is and Canvas App.

This tutorial provides an overview of how to use the feature of Hypothes.is.

This lesson gives a brief overview of the Hypothes.is functionality from an end user's perspective.