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. 

This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain. 

This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases. 

This lesson gives an introduction to simple spiking neuron models.

This lesson provides an introduction to simple spiking neuron models.

This presentation accompanies the paper entitled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data (see link below to download publication). 

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

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

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

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

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

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

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

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.