Provide Proactive Reproducible Analysis Transparency with Every Publication
Computational Engineering, Finance, and Science
2024-08-20 v1
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.
Cite
@article{arxiv.2408.09103,
title = {Provide Proactive Reproducible Analysis Transparency with Every Publication},
author = {Paul Meijer and Nicole Howard and Jessica Liang and Autumn Kelsey and Sathya Subramanian and Ed Johnson and Paul Mariz and James Harvey and Madeline Ambrose and Vitalii Tereshchenko and Aldan Beaubien and Neelima Inala and Yousef Aggoune and Stark Pister and Anne Vetto and Melissa Kinsey and Tom Bumol and Ananda Goldrath and Xiaojun Li and Troy Torgerson and Peter Skene and Lauren Okada and Christian La France and Zach Thomson and Lucas Graybuck},
journal= {arXiv preprint arXiv:2408.09103},
year = {2024}
}