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Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development.
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This content will become publicly available on April 27, 2026
How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and Opportunities
Computational notebooks are intended to prioritize the needs of scientists, but little is known about how scientists interact with notebooks, what requirements drive scientists’ software development processes, or what tactics scientists use to meet their requirements. We conducted an observational study of 20 scientists using Jupyter notebooks for their day-to-day tasks, finding that scientists prioritize different quality attributes depending on their goals. A qualitative analysis of their usage shows (1) a collection of goals scientists pursue with Jupyter notebooks, (2) a set of quality attributes that scientists value when they write software, and (3) tactics that scientists leverage to promote quality. In addition, we identify ways scientists incorporated AI tools into their notebook work. From our observations, we derive design recommendations for improving computational notebooks and future programming systems for scientists. Key opportunities pertain to helping scientists create and manage state, dependencies, and abstractions in their software, enabling more effective reuse of clearly-defined components.
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- Award ID(s):
- 2107397
- PAR ID:
- 10633688
- Publisher / Repository:
- 2025 IEEE/ACM 47th International Conference on Software Engineering
- Date Published:
- ISSN:
- 0270-5257
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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