Exploration and Explanation in Computational Notebooks

By Adam Rule, Aurélien Tabard, James Hollan. International Conference on Human Factors in Computing Systems, 2018

377 citations recorded by the source corpus.

Computational notebooks combine code, visualizations, and text in a single document. Researchers, data analysts, and even journalists are rapidly adopting this new medium. We present three studies of how they are using notebooks to document and share exploratory data analyses. In the first, we analyzed over 1 million computational notebooks on GitHub, finding that one in four had no explanatory text but consisted entirely of visualizations or code. In a second study, we examined over 200 academic computational notebooks, finding that although the vast majority described methods, only a minority discussed reasoning or results. In a third study, we interviewed 15 academic data analysts, finding that most considered computational notebooks personal, exploratory, and messy. Importantly, they typically used other media to share analyses. These studies demonstrate a tension between exploration and explanation in constructing and sharing computational notebooks. We conclude with opportunities to encourage explanation in computational media without hindering exploration.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/fcc86317d6a0e5f67968d2e84d20dd5ba0524cf2

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Citations in the corpus

How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, methodology): “And from empirical studies, we know how individual data scientists are using these tools [49, 50, 81], and what features could be added to improve the tools for users working alone [92].”
Human-AI Collaboration in Data Science (2019, background): “Rule et al. showed that people make less use of the textual description features of Notebooks, characterizing the problem as exploration vs. explanation [59].”
Questioning the AI: Informing Design Practices for Explainable AI User Experiences (2020, background): “We join a growing group of scholars studying the work of industry practitioners who create AI products [7, 15, 44, 45, 66, 77].”