# Eliciting Best Practices for Collaboration with Computational Notebooks

arXiv: https://arxiv.org/abs/2202.07233 · HTML: https://mlsanity.com/paper/2202.07233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 76 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Do Code Clones Matter?](https://mlsanity.com/paper/1701.05472/) (2017)
- [Ten Simple Rules for Reproducible Research in Jupyter Notebooks](https://mlsanity.com/paper/1810.08055/) (2018)
- [Code Duplication and Reuse in Jupyter Notebooks](https://mlsanity.com/paper/2005.13709/) (2020)
- [Benefitting from the Grey Literature in Software Engineering Research](https://mlsanity.com/paper/1911.12038/) (2019)
- [Agile Collaboration for Distributed Teams](https://mlsanity.com/paper/1901.06881/) (2019)

## Built on by (most cited first, 4 of 4)

- [Pynblint: a Static Analyzer for Python Jupyter Notebooks](https://mlsanity.com/paper/2205.11934/) (2022)
- [Teaching MLOps in Higher Education through Project-Based Learning](https://mlsanity.com/paper/2302.01048/) (2023)
- [Analysing Python Machine Learning Notebooks with Moose](https://mlsanity.com/paper/2509.11748/) (2025)
- [JupOtter: Cell-Level Bug Detection in Jupyter Notebooks](https://mlsanity.com/paper/2606.23877/) (2026)

## Abstract (arXiv)

Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks. With this aim, we first look for best practices through a multivocal literature review. Then, we conduct interviews with professional data scientists to assess their awareness of these best practices. Finally, we assess the adoption of best practices through the analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform. Findings reveal that experts are mostly aware of the best practices and tend to adopt them in their daily work. Nonetheless, they do not consistently follow all the recommendations as, depending on specific contexts, some are deemed unfeasible or counterproductive due to the lack of proper tool support. As such, we envision the design of notebook solutions that allow data scientists not to have to prioritize exploration and rapid prototyping over writing code of quality.