Reproducibility of computational studies is a hallmark of scientific
methodology. It enables researchers to build with confidence on the methods and
findings of others, reuse and extend computational pipelines, and thereby drive
scientific progress. Since many experimental studies rely on computational
analyses, biologists need guidance on how to set up and document reproducible
data analyses or simulations.
In this paper, we address several questions about reproducibility. For
example, what are the technical and non-technical barriers to reproducible
computational studies? What opportunities and challenges do computational
notebooks offer to overcome some of these barriers? What tools are available
and how can they be used effectively?
We have developed a set of rules to serve as a guide to scientists with a
specific focus on computational notebook systems, such as Jupyter Notebooks,
which have become a tool of choice for many applications. Notebooks combine
detailed workflows with narrative text and visualization of results. Combined
with software repositories and open source licensing, notebooks are powerful
tools for transparent, collaborative, reproducible, and reusable data analyses
