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A large-scale study on research code quality and execution.

Ana Trisovic1, Matthew K Lau2, Thomas Pasquier3

  • 1Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA. anatrisovic@g.harvard.edu.

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Summary
This summary is machine-generated.

Researchers struggle with executing shared research code, with 74% of R files failing initially. Improving coding practices and repository policies can enhance research reproducibility and code reuse.

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Area of Science:

  • Computer Science
  • Data Science
  • Scientific Computing

Background:

  • Research code is crucial for transparency and reproducibility in scientific publications.
  • Publicly available replication datasets aim to facilitate verification of research findings.
  • Assessing the quality and executability of shared research code is vital for the scientific community.

Purpose of the Study:

  • To evaluate the quality and execution success rate of research code from replication datasets.
  • To identify common errors in research code and assess the impact of code cleaning.
  • To analyze the influence of journal policies on code re-execution and propose improvements.

Main Methods:

  • Analysis of over 2000 replication datasets containing more than 9000 R files from the Harvard Dataverse repository (2010-2020).
  • Execution of R code in a clean runtime environment to assess reusability and identify errors.
  • Application of automatic code cleaning techniques to mitigate execution issues.

Main Results:

  • 74% of R files failed to execute successfully on initial run.
  • 56% of R files still failed after applying automatic code cleaning, indicating persistent errors.
  • Significant variation in code re-execution rates was observed across different journal collections, correlating with policy strictness.

Conclusions:

  • Many common coding errors can be prevented through adherence to good coding practices.
  • Current research code quality and execution rates pose challenges to scientific reproducibility.
  • Recommendations are proposed for researchers, journals, and repositories to improve code dissemination and reusability.