Reproducibility in Scientific Publications

Reproducibility is one of the fundamentals of scientific results, yet we sometimes don't follow agreed-upon best practices. In the early 2010s, this led to a reproducibility crisis that shook the core of psychology and medicine and led to a much stronger awareness and - in some fields - regulation. We will take a closer look on how reproducibility is achieved when data and code are involved and how we can implement best practices in our own publications.

We will cover different topics on publishing results based on data and code, with a strong emphasis on repeatability, sustainability, reuse, and FAIR. The workshop will be hands-on, and we will have time to work on our publications in the second half.

This workshop is aimed at natural scientists, but scientists from other disciplines are also welcome. We will work with examples in the R programming language. We expect prior contact with R, Python or another programming language. No in-depth programming knowledge is required. Participants will need a local R installation or a free RStudio Cloud account (https://rstudio.cloud/).

Contents

• Repeatable vs. Reproducible vs. Replicable
• Preprint, Postprint, Publishers PDF
• arXiv.org, OSF, Zenodo
• DOIs
• Order and Structure
• FAIR data principle
• Metadata
• Documentation
• Licenses
• Code and Data Citations
• Sustainable File Formats