The Center for Reproducible Neuroscience produces a variety of software resources (such as fmriprep and mriqc). In this tutorial, you will learn about some of them.
An overview of Docker and other containerization technologies: what containers are, why they’re useful, how to install them, and how to use them. Slides and materials available on GitHub: https://github.com/neurohackweek/docker-for-scientists.
A tutorial on version control using git and GitHub.
Setting the stage, and explaining what will happen in the next couple of weeks.
A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. An interactive Jupyter Notebook (which also doubles as the slides) is available here.
This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org. Outline Main Goal: To introduce the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. Definition of machine learning Data representation in scikit-learn […]
In this tutorial, we will work through setting up a scientific Python package. This will provide an opinionated introduction to some of the ins and outs of Python packaging and package distribution.
A review of barriers to reproducible neuroimaging research, and some potential solutions.
How to practice and promote an inclusionary, welcoming open science. Slides can be found here.
An introduction to software testing for scientific code. Materials available here; source code for all materials here.