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

TL;DR

This paper analyzes the design strategies of 163 interactive visualization tools in computational notebooks, identifies key design trade-offs, and introduces SuperNOVA, an open-source browser for exploring these tools.

Contribution

It provides a comprehensive analysis of notebook visualization tools, offers design insights, and introduces SuperNOVA for exploring existing tools.

Findings

Tools compatible with multiple notebook platforms have greater impact.

Balancing visualization-notebook integration involves key trade-offs.

Multimodal data can enhance notebook visualizations.

Abstract

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/.