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Computational notebooks such as Jupyter Notebook have become data scientists'de facto programming environments. Many visualization researchers andpractitioners have developed interactive visualization tools that supportnotebooks. However, little is known about the appropriate design of visualanalytics (VA) tools in notebooks. To bridge this critical research gap, weinvestigate the design strategies in this space by analyzing 159 notebook VAtools and their users' feedback. Our analysis encompasses 62 systems fromacademic papers and 103 systems sourced from a pool of 55k notebooks containinginteractive visualizations that we obtain via scraping 8.6 million notebooks onGitHub. We also examine findings from 15 user studies and user feedback in 379GitHub issues. Through this work, we identify unique design opportunities andconsiderations for future notebook VA tools, such as using and manipulatingmultimodal data in notebooks as well as balancing the degree ofvisualization-notebook integration. Finally, we develop SuperNOVA, anopen-source interactive tool to help researchers explore existing notebook VAtools and search for related work.

SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks