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This summary is machine-generated.

Researchers developed adapr, an R package for accountable data analysis. This system enhances reproducibility and transparency in scientific computing by tracking data provenance through accountable units.

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Area of Science:

  • Computational Science
  • Data Science
  • Reproducible Research

Background:

  • Producing transparent and reproducible analyses presents challenges for both novice and experienced researchers.
  • Existing computational systems often struggle to meet stringent reproducibility and accountability standards.

Purpose of the Study:

  • To introduce adapr (Accountable Data Analysis Process in R), a novel system designed to streamline reproducible and accountable data analysis.
  • To provide researchers with tools that facilitate the tracking of data provenance and enhance collaborative workflows.

Main Methods:

  • Development of an R package and R Shiny application named adapr.
  • Implementation of the 'accountable unit' principle, where data files are linked to their creation provenance (how, when, who).
  • Utilizing file hashes and version control systems for tracking and verification, distinct from watermarking or public repositories.

Main Results:

  • adapr enables the creation of version-controlled, sharable, and traceable data files (statistics, tables, graphics).
  • The system simplifies the determination of data provenance through efficient file hash and version control searches.
  • Accountable units offer a practical approach to managing provenance in complex collaborative projects.

Conclusions:

  • adapr provides an efficient solution for achieving reproducible and accountable data analysis in R.
  • The system supports collaborative research by simplifying the tracking and verification of computational results.
  • Accountable units offer a robust alternative for provenance tracking compared to existing methods like verifiable computational results (VCRs).