Toronto Data Workshop on Reproducibility - 25-26 February 2021
"I shared the code and data of my last scientific article, does it mean that it is reproducible?"
One might think that having access to the research data and the code used to analyze that data would be enough to reproduce published results, but often this is much more involved.
Is reproducibility dependent on the reviewer's knowledge? What things do we not usually think about can affect reproducibility? Can the choice of how to capture the computational environment influence the experience of the reviewer?
In this talk, we are going to think together some of the necessary steps that make someone else able to reproduce a scientific article or project. I will share some thoughts from my experience in ReproHack and show you how reviewing is a great practice to learn about reproducibility.
Reprohack is a hackathon-style event focused on the reproducibility of research results. These hackathons provide a low-pressure sandbox environment for practicing reproducible research:
- Authors can practice producing reproducible research and receive friendly feedback and appreciation of their efforts
- Participants can practice reviewing, learn about reproducibility best practices as well as common pitfalls from working with real-life materials rather than just dummy. They also get inspired and grow confidence in working more openly themselves.
The research Community benefits from:
- Evaluating what best practice is in practice
- More practice in both developing and reviewing materials
The Turing Way Community, & Scriberia. (2020, March 3). Illustrations from the Turing Way book dashes. Zenodo. http://doi.org/10.5281/zenodo.3695300