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Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re‐run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes. We implement our approach as a JupyterLab extension that supports tracking interactions in Vega‐Altair plots and in a data table view. Persist can re‐execute interaction provenance when a notebook or a cell is re‐executed, enabling reproducibility and re‐use. We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code‐based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.
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PRUNE: A preserving run environment for reproducible scientific computing
Computing as a whole suffers from a crisis of reproducibility. Programs executed in one context are aston- ishingly hard to reproduce in another context, resulting in wasted effort by people and general distrust of results produced by computer. The root of the problem lies in the fact that every program has implicit dependencies on data and execution environment which are rarely understood by the end user. To address this problem, we present PRUNE, the Preserving Run Environment. In PRUNE, every task to be executed is wrapped in a functional interface and coupled with a strictly defined environment. The task is then executed by PRUNE rather than the user to ensure reproducibility. As a scientific workflow evolves in PRUNE, a growing but immutable tree of derived data is created. The provenance of every item in the system can be precisely described, facilitating sharing and modification between collaborating researchers, along with efficient management of limited storage space. We present the user interface and the initial prototype of PRUNE, and demonstrate its application in matching records and comparing surnames in U.S. Censuses
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- Award ID(s):
- 1642409
- PAR ID:
- 10047189
- Date Published:
- Journal Name:
- IEEE Conference on e-Science
- Page Range / eLocation ID:
- 61 to 70
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/eScience.2016.7870886
