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 datasets, a granular understanding of the analysis methodology is an essential com...

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Main Authors: 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
Format: Article
Language:English
Published: The Royal Society 2025-03-01
Series:Royal Society Open Science
Subjects:
Online Access:https://royalsocietypublishing.org/doi/10.1098/rsos.241936
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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
author_facet 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
author_sort Paul Meijer
collection DOAJ
description 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 datasets, a granular understanding of the analysis methodology is an essential component of reproducibility. This article 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.
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publishDate 2025-03-01
publisher The Royal Society
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series Royal Society Open Science
spelling doaj-art-20426fc7786b42cf923d4ff604526e9f2025-08-20T02:02:21ZengThe Royal SocietyRoyal Society Open Science2054-57032025-03-0112310.1098/rsos.241936Provide proactive reproducible analysis transparency with every publicationPaul Meijer0Nicole Howard1Jessica Liang2Autumn Kelsey3Sathya Subramanian4Ed Johnson5Paul Mariz6James Harvey7Madeline Ambrose8Vitalii Tereshchenko9Aldan Beaubien10Neelima Inala11Yousef Aggoune12Stark Pister13Anne Vetto14Melissa Kinsey15Tom Bumol16Ananda Goldrath17Xiaojun Li18Troy Torgerson19Peter Skene20Lauren Okada21Christian La France22Zach Thomson23Lucas Graybuck24Allen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAAllen Institute for Immunology, 615 Westlake Avenue N, Seattle, WA 98109, USAThe 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 datasets, a granular understanding of the analysis methodology is an essential component of reproducibility. This article 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.https://royalsocietypublishing.org/doi/10.1098/rsos.241936reproducibility crisisopen scienceequity in sciencedata analysisimmunologylife sciences
spellingShingle 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
Provide proactive reproducible analysis transparency with every publication
Royal Society Open Science
reproducibility crisis
open science
equity in science
data analysis
immunology
life sciences
title Provide proactive reproducible analysis transparency with every publication
title_full Provide proactive reproducible analysis transparency with every publication
title_fullStr Provide proactive reproducible analysis transparency with every publication
title_full_unstemmed Provide proactive reproducible analysis transparency with every publication
title_short Provide proactive reproducible analysis transparency with every publication
title_sort provide proactive reproducible analysis transparency with every publication
topic reproducibility crisis
open science
equity in science
data analysis
immunology
life sciences
url https://royalsocietypublishing.org/doi/10.1098/rsos.241936
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