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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. 

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. 

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. 

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. 

Representing branching and comparative analyses in computational notebooks is complicated by the 1-dimensional (1D), top-down list arrangement of cells. Given the ubiquity of these and other non-linear features, their importance to analysis and narrative, and the struggles current 1D computational notebooks have, enabling organization of computational notebook cells in 2 dimensions (2D) may prove valuable. We investigated whether and how users would organize cells in such a “2D Computational Notebook” through a user study and gathered feedback from participants through a follow-up survey and optional interviews. Through the user study, we found 3 main design patterns for arranging notebook cells in 2D: Linear, Multi-Column, and Workboard. Through the survey and interviews, we found that users see potential value in 2D Computational Notebooks for branching and comparative analyses, but the expansion from 1D to 2D may necessitate additional navigational and organizational aids.