Making Provenance Work for You

R J. Pub Date : 2023-02-10 DOI:10.32614/rj-2023-003
Barbara Lerner, E. Boose, O. Brand, Aaron M. Ellison, E. Fong, Matthew K. Lau, K. Ngo, Thomas Pasquier, Luis A. Perez, M. Seltzer, Rose Sheehan, J. Wonsil
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引用次数: 1

Abstract

To be useful, scientific results must be reproducible and trustworthy. Data provenance—the history of data and how it was computed—underlies reproducibility of, and trust in, data analyses. Our work focuses on collecting data provenance from R scripts and providing tools that use the provenance to increase the reproducibility of and trust in analyses done in R. Specifically, our “End-to-end provenance tools” (“E2ETools”) use data provenance to: document the computing environment and inputs and outputs of a script’s execution; support script debugging and exploration; and explain differences in behavior across repeated executions of the same script. Use of these tools can help both the original author and later users of a script reproduce and trust its results.
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要想有用,科学结果必须是可重复的和值得信赖的。数据来源——数据的历史和计算方式——是数据分析可重复性和可信度的基础。我们的工作重点是从R脚本中收集数据来源,并提供使用这些来源的工具来增加R中分析的可重复性和可信度。具体来说,我们的“端到端来源工具”(“E2ETools”)使用数据来源来记录计算环境和脚本执行的输入和输出;支持脚本调试和探索;并解释重复执行同一脚本时的行为差异。使用这些工具可以帮助脚本的原作者和后来的用户重现并信任其结果。
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