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Despite the widespread adoption of computational notebooks, little is knownabout best practices for their usage in collaborative contexts. In this paper,we fill this gap by eliciting a catalog of best practices for collaborativedata science with computational notebooks. With this aim, we first look forbest practices through a multivocal literature review. Then, we conductinterviews with professional data scientists to assess their awareness of thesebest practices. Finally, we assess the adoption of best practices through theanalysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform.Findings reveal that experts are mostly aware of the best practices and tend toadopt them in their daily work. Nonetheless, they do not consistently followall the recommendations as, depending on specific contexts, some are deemedunfeasible or counterproductive due to the lack of proper tool support. Assuch, we envision the design of notebook solutions that allow data scientistsnot to have to prioritize exploration and rapid prototyping over writing codeof quality.

Eliciting Best Practices for Collaboration with Computational Notebooks