Our Educational Objectives include:
Our initial (Phase 1) curriculum focuses on developing material that address reproducibility in four areas:
in order to initiate a set of tools and practices that are reproducibility-enabled. Future curricula will extend the reach of these materials to encompass a broader community of researchers and will feature more training material on the tools developed by the ReproNim project.
Why do we care about reproducibility? Can we do anything to improve the reproducibility of our neuroimaging work? Let's get motivated to change the world!
Shells, version control, package managers, and other tools to embrace "Reproducibility By Design"!
FAIR is a collection of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. We look at ways to ensure that a researcher’s data is properly managed and published in support of reproducible research.
What do we need to know to conduct reproducible analysis? Learn to: Annotate, harmonize, clean, and version data; and Create and maintain reproducible computational environments.
Here we describe some key statistical concepts, and how to use them to make your research more reproducible. Everything you ever wanted to know about power, effect size, P-values, sampling and everything else.