We produce teaching materials and training programs in reproducible research, and aim to reach a broad audience. Our materials and programs address the overall issues that affect the reproducibility of neuroimaging research (modules, webinars) 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 Train-the-Trainer Fellowship Program, to empower researchers to teach reproducible methods to others. This program is intentionally customized to support Fellows to a) identify particular needs and target audiences (at any level) in their home institutions, and b) then create one or more training events (for example, an academic course, hackathon, or workshop).
We also partner with groups to create tailored training programs that address 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 (2020-2021 class). 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 which are being catalogued in our ReproInventory.
Our ReproNim âFirst Fridaysâ monthly Webinar Series features important efforts in reproducibility, from both ReproNim and others. See our ReproNim 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 case scenarios.
Our Curriculum focuses on developing material that address reproducibility in six modular areas:
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 Reusable. We look at ways to ensure that a researcherâs 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.
Here is the repository containing the template used for all aforementioned modules. Make your own training material based on that template for a uniform appearance and features.