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.

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

In this tutorial, you will learn how to use TVB-NEST toolbox on your local computer.

This tutorial provides instruction on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform.

This lesson consists of a talk about the history and future of academic publishing and the need for transparency, as well as a live demo of an alpha version of NeuroLibre, a preprint server that goes beyond the PDF to complement research articles. This video was part of a virutal QBIN SciComm seminar.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

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

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

High-Performance Computing and Analytics Lab, University of Bonn

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

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

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

This lesson gives an introduction to simple spiking neuron models.

This lesson provides an introduction to simple spiking neuron models.

This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.

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

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

This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded Q&A sessions.

This lecture formalizes modeling as a decision process that is constrained by a precise problem statement and specific model goals. We provide real-life examples on how model building is usually less linear than presented in Modeling Practice I.