This presentation by the OHBM OpenScienceSIG covers common scenarios where Git can be extremely valuable. The essentials covered include cloning a repository and keeping it up to date, how to create and use your own repository, and how to contribute to other projects via forking and pull requests.

DataLad is a versatile data management and data publication multi-tool. In this session, you can learn the basic concepts and commands for version control and reproducible data analysis. You’ll get to see, create, and install DataLad datasets of many shapes and sizes, master local version workflows and provenance-captured analysis-execution, and you will get ideas for your next data analysis project.

This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields. 

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

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 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. 

This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data. 

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

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. 

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.

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

This lesson is part 1 of 2 of a tutorial on statistical models for neural data.

What is the difference between attention and consciousness? This lecture describes the scientific meaning of consciousness, journeys on the search for neural correlates of visual consciousness, and explores the possibility of consciousness in other beings and even non-biological structures.