Provide Proactive Reproducible Analysis Transparency with Every Publication
<|reference_start|>Provide Proactive Reproducible Analysis Transparency with Every Publication: 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.<|reference_end|>
@article{meijer2024provide,
    title={Provide Proactive Reproducible Analysis Transparency with Every
  Publication},
    author={Paul Meijer, Nicole Howard, Jessica Liang, Autumn Kelsey, Sathya
  Subramanian, Ed Johnson, Paul Mariz, James Harvey, Madeline Ambrose, Vitalii
  Tereshchenko, Aldan Beaubien, Neelima Inala, Yousef Aggoune, Stark Pister,
  Anne Vetto, Melissa Kinsey, Tom Bumol, Ananda Goldrath, Xiaojun Li, Troy
  Torgerson, Peter Skene, Lauren Okada, Christian La France, Zach Thomson,
  Lucas Graybuck},
    journal={arXiv preprint arXiv:2408.09103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09103},
    primaryClass={cs.CE}
}