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Creators/Authors contains: "Wenskovitch, John"

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  1. ; ; ; ; ; (, 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))
    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. 
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    Full Text Available 
  2. ; ; ; ; ; ; (, NeurIPS 2020 Human and Model in the Loop Evaluation and Training Strategies (HAMLETS) Workshop)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; (, IEEE Computer Graphics and Applications)
    Full Text Available 
  4. ; ; ; ; ; (, Computer Graphics Forum)
    Abstract There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively athttps://provenance-survey.caleydo.org. 
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