Ten Simple Rules for Reproducible Research in Jupyter Notebooks

TL;DR

This paper provides ten practical guidelines for ensuring reproducibility in computational research using Jupyter Notebooks, emphasizing transparency, documentation, and effective use of available tools.

Contribution

It introduces a set of ten rules tailored for reproducible research with Jupyter Notebooks, addressing technical and non-technical barriers and promoting best practices.

Findings

Notebooks enhance transparency and collaboration.

Guidelines improve reproducibility and reuse.

Tools and practices facilitate better documentation.

Abstract

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.