Training

ReproNim produces teaching materials and training programs in reproducible research and aims to reach a broad audience. Our materials and programs address the overall issues that affect the reproducibility of neuroimaging research and consider their applications in a wide variety of experimental and environmental contexts.

What we offer

A modular online curriculum that provides topical training in overarching issues that affect the reproducibility of neuroimaging research (data acquisition and characterization, experimental methods, analyses, record keeping and reporting, reusability, and sharing of data and methods).

The ReproNim/INCF Fellowship Program empowers researchers to teach reproducible methods to others. This program is intentionally customized to support Fellows to identify particular needs and target audiences (at any level) in their home institutions and then create one or more training events (e.g. an academic course, hackathon, or workshop).

We also partner with groups to create tailored training programs that addresses their specific research needs and expand our topical coverage. Our associated training programs include ReproRehab, an NIH-funded training fellowship program (USC) designed to support reproducible research in the physical rehabilitation community under the founding leadership of ReproNim/INCF Fellowship alumna Sook-Lei Liew (USC). We have also partnered with the Adolescent Brain Cognitive Development (ABCD) Study Research Consortium in collaboration with Angela Laird (FIU) to create ABCD-ReproNim, a 12-week online course on reproducible data analyses with ABCD data (big data).

In addition, we and our Fellows have generated a great deal of training materials. An effort is underway to catalog these in our ReproInventory.

Our ReproNim “First Fridays” monthly webinar series features important efforts in reproducibility from both ReproNim and other groups. See our ReproNim YouTube channel to view our entire collection of webinars to date.

Our tutorials address practical challenges in the execution of reproducible neuroimaging across a number of use cases.

Curriculum

Our curriculum focuses on developing material that address reproducibility in five modular areas.

ReproNim introduction

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! Go to the module.

Reproducibility basics

Shells, version control, package managers, and other tools to embrace “reproducibility by design.” Go to the module.

FAIR data

FAIR is a collection of guiding principles to make data findable, accessible, interoperable, and reusable. We look at ways to ensure that a researcher’s data is properly managed and published in support of reproducible research. Go to the module.

Data processing

What do we need to know to conduct reproducible analyses? Learn to annotate, harmonize, clean, and version data and to create and maintain reproducible computational environments. Go to the module.

Statistics

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. Go to the module.