# Ten Simple Rules for Reproducible Research in Jupyter Notebooks

arXiv: https://arxiv.org/abs/1810.08055 · HTML: https://mlsanity.com/paper/1810.08055/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [On the Reproducibility of Neural Network Predictions](https://mlsanity.com/paper/2102.03349/) (2021)
- [Predictive Churn with the Set of Good Models](https://mlsanity.com/paper/2402.07745/) (2024)
- [Eliciting Best Practices for Collaboration with Computational Notebooks](https://mlsanity.com/paper/2202.07233/) (2022)
- [A Flexible Cell Classification for ML Projects in Jupyter Notebooks](https://mlsanity.com/paper/2403.07562/) (2024)
- [Ten Quick Tips for Deep Learning in Biology](https://mlsanity.com/paper/2105.14372/) (2021)
- [Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production](https://mlsanity.com/paper/2205.11941/) (2022)

## Abstract (arXiv)

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.