Eliciting Best Practices for Collaboration with Computational Notebooks
This work identifies collaboration challenges for data scientists using computational notebooks, but it is incremental as it builds on existing knowledge without introducing new methods.
The study addressed the lack of known best practices for collaboration in computational notebooks by eliciting a catalog through literature review, interviews with data scientists, and analysis of 1,380 Jupyter notebooks from Kaggle, finding that experts are mostly aware of but do not consistently follow all practices due to tool limitations.
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.