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This lecture focuses on where and how Jupyter notebooks can be used most effectively for education.

Difficulty level: Beginner
Duration: 34:53
Speaker: : Thomas Kluyver

JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.

Difficulty level: Beginner
Duration: 1:36:27
Speaker: : Thomas Kluyver

This lesson introduces the practical usage of The Virtual Brain (TVB) in its graphical user interface and via python scripts. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator, and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of TVB. 

Difficulty level: Beginner
Duration: 1:12:24
Speaker: : Paul Triebkorn

This tutorial covers the fundamentals of collaborating with Git and GitHub.

Difficulty level: Intermediate
Duration: 2:15:50
Speaker: : Elizabeth DuPre

This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter. 

Difficulty level: Intermediate
Duration: 39:05
Speaker: : Jil Meier

This lecture provides an general introduction to epilepsy, as well as why and how TVB can prove useful in building and testing epileptic models. 

Difficulty level: Intermediate
Duration: 37:12
Speaker: : Julie Courtiol

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

Difficulty level: Intermediate
Duration: 17:08
Speaker: : Ari Ercole

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.

Difficulty level: Intermediate
Duration: 15:05

The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms.  Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.

Difficulty level: Intermediate
Duration: 20:26
Speaker: : Jason Skellariou

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®

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. 

Difficulty level: Intermediate
Duration: 50:28
Speaker: : Elizabeth DuPre

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

Difficulty level: Beginner
Duration: 1:25:17
Speaker: : Fernando Perez

This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.

Difficulty level: Beginner
Duration: 25:50
Speaker: : Eva Dyer

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

Difficulty level: Beginner
Duration: 17:32
Speaker: : Ida Aasebø