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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.

Difficulty level: Beginner
Duration: 1:16:04

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

In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research. 

Difficulty level: Beginner
Duration: 1:15:12
Speaker: : Abhi Pratap

This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.

Difficulty level: Beginner
Duration: 51:07

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.

Difficulty level: Beginner
Duration: 2:24:35

Introduction of the Foundations of Machine Learning in Python course - Day 01.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Beginner
Duration: 35:24
Speaker: : Elena Trunz

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

Difficulty level: Beginner
Duration: 1:02:29
Speaker: : Nir Giladi

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Difficulty level: Beginner
Duration: 37:51

This lesson provides an introduction to neurons, synaptic transmission, and ion channels.

Difficulty level: Beginner
Duration: 46:07

This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

Difficulty level: Beginner
Duration: 40:32

This lecture covers integrating information within a network, modulating and controlling networks, functions and dysfunctions of hippocampal networks, and the integrative network controlling sleep and arousal.

Difficulty level: Beginner
Duration: 47:05

This lecture focuses on the comprehension of nociception and pain sensation, highlighting how the somatosensory system and different molecular partners are involved in nociception.

Difficulty level: Beginner
Duration: 28:09
Speaker: : Serena Quarta

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

Difficulty level: Beginner
Duration: 18:13
Speaker: : Mojib Javadi

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

Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®

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®

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®

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®

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

Difficulty level: Beginner
Duration: 6:27
Speaker: : MATLAB®

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®

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®