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.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

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.

Difficulty level: Beginner

Duration: 52:26

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.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

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.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

This tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.

This lesson corresponds to slides 158-187 of the PDF below.

Difficulty level: Advanced

Duration: 1:22:10

Speaker: : Peter Bedford, Povilas Karvelis

Course:

This demonstration walks through how to import your data into MATLAB.

Difficulty level: Beginner

Duration: 6:10

Speaker: : MATLAB®

Course:

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.

Difficulty level: Beginner

Duration: 15:10

Speaker: : MATLAB®

Course:

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

Difficulty level: Beginner

Duration: 2:49

Speaker: : MATLAB®

Course:

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

Difficulty level: Beginner

Duration: 6:10

Speaker: : MATLAB®

Course:

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

Difficulty level: Beginner

Duration: 6:27

Speaker: : MATLAB®

Course:

This brief tutorial goes over how you can easily work with big data as you would with any size of data.

Difficulty level: Beginner

Duration: 3:55

Speaker: : MATLAB®

Course:

In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

Difficulty level: Beginner

Duration: 3:52

Speaker: : MATLAB®

Course:

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

Difficulty level: Beginner

Duration: 1:01:39

Speaker: : Thomas Mock

This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem.

Difficulty level: Beginner

Duration: 32:58

Speaker: : Jil Meier

Course:

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.

Difficulty level: Intermediate

Duration: 50:44

Speaker: : Caterina Gratton

Course:

In this lesson, you will learn about the Python project Nipype, an open-source, community-developed initiative under the umbrella of NiPy. Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow.

Difficulty level: Intermediate

Duration: 1:25:05

Speaker: : Satrajit Ghosh

- Bayesian networks (2)
- Cognitive neuroinformatics (1)
- (-) Neuroimaging (17)
- Machine learning (2)
- Standards and best practices (4)
- Tools (1)
- Repositories and science gateways (1)
- (-) General neuroscience (6)
- Computational neuroscience (20)
- Statistics (4)
- Computer Science (1)
- Genomics (5)
- (-) Data science (10)
- (-) Open science (4)