Provide Proactive Reproducible Analysis Transparency with Every Publication

TL;DR

This paper proposes a framework for proactive, comprehensive reproducibility in scientific research, enabling real-time trace generation and sharing of data, methods, and tools to enhance transparency and verification.

Contribution

It introduces a computational reproducibility framework that allows scientists to generate and share complete analysis traces during research, improving reproducibility and transparency.

Findings

Framework supports real-time reproducibility trace generation

Enables sharing of data, methods, and tools with publications

Facilitates verification and re-execution of scientific analyses

Abstract

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.