SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
This work addresses a critical research gap for data scientists and visualization practitioners by systematically analyzing design strategies, though it is incremental as it builds on existing tools and methods.
The study analyzed 163 notebook visualization tools to address the lack of knowledge on appropriate design strategies for interactive visualization in computational notebooks, identifying key design implications and trade-offs, such as leveraging multimodal data and balancing integration, and providing empirical evidence that tools compatible with more notebook platforms have greater impact.
Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed interactive visualization tools that support notebooks, yet little is known about the appropriate design of these tools. To address this critical research gap, we investigate the design strategies in this space by analyzing 163 notebook visualization tools. Our analysis encompasses 64 systems from academic papers and 105 systems sourced from a pool of 55k notebooks containing interactive visualizations that we obtain via scraping 8.6 million notebooks on GitHub. Through this study, we identify key design implications and trade-offs, such as leveraging multimodal data in notebooks as well as balancing the degree of visualization-notebook integration. Furthermore, we provide empirical evidence that tools compatible with more notebook platforms have a greater impact. Finally, we develop SuperNOVA, an open-source interactive browser to help researchers explore existing notebook visualization tools. SuperNOVA is publicly accessible at: https://poloclub.github.io/supernova/.