The idea of reproducible articles is to enrich the conventional text-based narrative of research manuscripts with code, data and interactive figures that can be executed by the reader. This gives direct insight into the methods, algorithms and data used in the research, increasing transparency and reproducibility. While researchers generally build upon each other’s findings, focusing on reproducibility is a great way to make sure that conclusions can be derived from the research performed.

Why reproducibility?

If you write a reproducible article, you enable peers and other researchers to use your input data and see if they can get the same results. Over the past decades, technological (research) possibilities greatly improved and made it possible to work with increasingly complicated data-sets and methods. Reproducibility allows us to check (or correct) technological limitations of the past that may have been of influence on research results.

How to

More about:

• Introducing eLife’s first computationally reproducible article (introduction and example of reproducible articles)
• Nature Webinar: how to make your research reproducible (60 minute webinar on making your research reproducible)
• Reproducibility vs. Replicability (a brief history of reproducible articles)

Tools:

• R markdown (file format for making dynamic and fully reproducible documents with R, code is embedded in the text)
• papaja: Reproducible APA manuscripts with R Markdown (R package for creating reproducible articles in APA format)
• Jupyter notebooks (reproducible documents, combining Markdown text and code in different programming languages, including Python and R)