Rclean: A Tool for Writing Cleaner, More Transparent Code

Matthew K. Lau, Thomas Pasquier, Margo Seltzer

Research output: Contribution to journalArticle (Academic Journal)

34 Downloads (Pure)

Abstract

The growth of programming in the sciences has been explosive in the last decade. This has facilitated the rapid advancement of science through the agile development of computational tools. However, concerns have begun to surface about the reproducibility of scientific research in general (Baker, 2016) and the potential issues stemming from issues with analytical software (Stodden, Seiler, & Ma, 2018). Specifically, there is a growing recognition across disciplines that simply making data and software “available” is not enough and that there is a need to improve the transparency and stability of scientific software (Pasquier et al., 2018).
Original languageEnglish
Article number1312
Number of pages4
JournalJournal of Open Source Software
Volume5
Issue number46
DOIs
Publication statusPublished - 17 Feb 2020

Keywords

  • reproducibility
  • transparency
  • code cleaning
  • data provenance

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