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. However, little is known about the appropriate design of visual analytics (VA) tools in notebooks. To bridge this critical research gap, we investigate the design strategies in this space by analyzing 159 notebook VA tools and their users' feedback. Our analysis encompasses 62 systems from academic papers and 103 systems sourced from a pool of 55k notebooks containing interactive visualizations that we obtain via scraping 8.6 million notebooks on GitHub. We also examine findings from 15 user studies and user feedback in 379 GitHub issues. Through this work, we identify unique design opportunities and considerations for future notebook VA tools, such as using and manipulating multimodal data in notebooks as well as balancing the degree of visualization-notebook integration. Finally, we develop SuperNOVA, an open-source interactive tool to help researchers explore existing notebook VA tools and search for related work.

本文分析了159个计算机笔记本可视化工具和他们用户的反馈，来源于学术论文和GitHub上8.6百万的笔记本。经过分析，作者识别出多模态数据的应用和设计注意事项是未来笔记本可视化工具的关键设计机会，最终开发了一个名为SuperNOVA的开源交互式工具来帮助研究人员探索现有的笔记本可视化工具和查找相关工作。

计算笔记本中交互式可视化的设计策略与机会