Whereas analysts previously kept code, documentation, and results in separate files, they increasingly use computational notebooks such as Jupyter Notebooks and R Notebooks to both perform analyses and combine code, results, and descriptive text in a single “computational narrative” to be read and rerun by others. Achieving even this minimum standard typically requires both machine-readable descriptions of the data, software, dependencies, and computational environment involved (for example, hardware or cloud configuration), as well as human-readable documentation describing how all these pieces fit together.

Reproducibility, the scientific standard that others should be able to recreate your results, requires at a minimum that “data and the computer code used to analyze data be made available to others”. We aim to augment this existing wellspring of advice by addressing the unique challenges and opportunities that arise when using computational notebooks, especially Jupyter Notebooks, for research. Numerous papers, including several in the Ten Simple Rules collection, have highlighted the need for robust and reproducible analyses in computational research, described the difficulty of achieving these standards, and enumerated best practices. As studies grow in scale and complexity, it has become increasingly difficult to provide clear descriptions and open access to the methods and data needed to understand and reproduce computational research.

Jupyter notebook online use library