revisit: a Workflow Tool for Data Science

TL;DR

The revisit package enhances reproducibility in data science by creating a transparent, replayable record of statistical analyses, including error warnings, to address the scientific reproducibility crisis.

Contribution

It introduces a tool that generates a reproducible software paper trail of statistical operations, aiding verification and methodological transparency.

Findings

Provides a replayable record of data analysis steps

Issues warnings for potential statistical errors

Improves transparency and reproducibility in scientific research

Abstract

In recent years there has been widespread concern in the scientific community over a reproducibility crisis. Among the major causes that have been identified is statistical: In many scientific research the statistical analysis (including data preparation) suffers from a lack of transparency and methodological problems, major obstructions to reproducibility. The revisit package aims toward remedying this problem, by generating a "software paper trail" of the statistical operations applied to a dataset. This record can be "replayed" for verification purposes, as well as be modified to enable alternative analyses. The software also issues warnings of certain kinds of potential errors in statistical methodology, again related to the reproducibility issue.