Research Reproducibility and Open Science

Who is this for?

This guide is intended to be both a self-guided introduction to concepts and skills in Research Reproducibility and Open Science, as well as a reference for more in-depth readings and resources.

How do I get started?

Beyond the general concepts and definitions below, take a look at the [link](Skill Tree) to identify what to learn next, either in your open science journey or because of a research task that you want to accomplish.

For questions (including those that aren't covered by this libguide), please reach out to me, through the profile on the side!

Definitions

• Reproducible research (also computational reproducibility): Authors provide all the necessary data and the computer codes to run the analysis again, re-creating the results.
• Replication: A study that arrives at the same scientific findings as another study, collecting new data (possibly with different methods), and completing new analyses.

from Barba, L.A., 2018. Terminologies for reproducible research. arXiv preprint arXiv:1802.03311

Benefits

• Efficiency - When your research is reproducible, it is easier for you to fix it, make changes, or expand on the work.
• Openness and Transparency - Document the components and steps of your analysis to ensure reproducibility and transparency in approach. documented and shared.
• Accessibility - Sharing your work in a reproducible manner enables others to use it, build on it, and teach with it.
• Impact - When the components of your research (data/code/protocols/etc.) are shared and re-usable, your contributions and work are more findable (and citable!) by other researchers.