The role of metadata in reproducible computational research.
The role of metadata in reproducible computational research.
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DOI：
10.1016/j.patter.2021.100322
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发表时间：
2021-09-10
期刊：
影响因子：
--
通讯作者：
Greenberg J
中科院分区：
文献类型：
--
作者：
Leipzig J;Nüst D;Hoyt CT;Ram K;Greenberg J
可重复计算研究(RCR)是用于电子分析的科学方法的基石，它将原始数据转换为已发表的结果。除了在研究完整性方面的作用外，提高科学研究的再现性还可以加速评估和再利用。这种潜力和对公平原则的广泛支持激发了人们对支持可再现性的元数据标准的兴趣。元数据为原始数据和方法提供上下文和出处，对于发现和验证都是必不可少的。尽管与科学数据有这种共同的联系，但很少有研究明确描述元数据如何使可重复的计算研究成为可能。本审查采用功能性内容分析来确定支持跨分析堆栈的可重复性的元数据标准，分析堆栈由输入数据、工具、笔记本、管道和出版物组成。我们的审查提供了背景背景，探索了差距，并发现了嵌入性和方法论权重的组成部分趋势，我们从这些趋势中得出了对未来工作的建议。最近的技术融合使科学家能够有效地转移可运行的分析，解决了可重复研究的长期挑战。电子计算机分析中的可重复研究的实施需要大量的元数据来描述科学概念和基本的计算环境。这篇综述涵盖了与可重复计算研究相关的广泛的元数据标准，包括输入数据、工具、报告、管道和出版物组成的“分析栈”。遗留元数据和尖端元数据支持广泛的数据注释、分析方法和几乎所有科学学科的解释。这项审查旨在为元数据和可复制的研究社区架起桥梁。我们确定嵌入和连接的元数据的相互竞争的方法，讨论差距，并提出对期刊和同行审查的未来有影响的建议。莱比锡等人。审查输入数据、工具、报告、管道和出版物的各种元数据标准，这些标准使可重复的计算分析成为可能，并对期刊和同行审查的未来产生影响。
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work. A recent confluence of technologies has enabled scientists to effectively transfer runnable analyses, addressing a long-standing challenge of reproducible research. The implementation of reproducible research for in silico analyses requires extensive metadata to describe both scientific concepts and the underlying computing environment. This review covers the wide range of metadata standards relevant to reproducible computational research across an “analytic stack” consisting of input data, tools, reports, pipelines, and publications. Legacy and cutting-edge metadata support a wide range of data annotations, analytic approaches, and interpretation across virtually all scientific disciplines. This review is designed to bridge the metadata and reproducible research communities. We identify competing approaches of embedded and connected metadata, discuss gaps, and make recommendations with implications for the future of journals and peer review. Leipzig et al. review the wide range of metadata standards for input data, tools, reports, pipelines, and publications that are enabling reproducible computational analyses with implications for the future of journals and peer review.
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影响因子：
9.2
作者：
Aranguren ME;Wilkinson MD
通讯作者：
Wilkinson MD
影响因子：
12.3
作者：
Andersson L;Archibald AL;Bottema CD;Brauning R;Burgess SC;Burt DW;Casas E;Cheng HH;Clarke L;Couldrey C;Dalrymple BP;Elsik CG;Foissac S;Giuffra E;Groenen MA;Hayes BJ;Huang LS;Khatib H;Kijas JW;Kim H;Lunney JK;McCarthy FM;McEwan JC;Moore S;Nanduri B;Notredame C;Palti Y;Plastow GS;Reecy JM;Rohrer GA;Sarropoulou E;Schmidt CJ;Silverstein J;Tellam RL;Tixier-Boichard M;Tosser-Klopp G;Tuggle CK;Vilkki J;White SN;Zhao S;Zhou H;FAANG Consortium
通讯作者：
FAANG Consortium
影响因子：
--
作者：
Anzt H;Bach F;Druskat S;Löffler F;Loewe A;Renard BY;Seemann G;Struck A;Achhammer E;Aggarwal P;Appel F;Bader M;Brusch L;Busse C;Chourdakis G;Dabrowski PW;Ebert P;Flemisch B;Friedl S;Fritzsch B;Funk MD;Gast V;Goth F;Grad JN;Hegewald J;Hermann S;Hohmann F;Janosch S;Kutra D;Linxweiler J;Muth T;Peters-Kottig W;Rack F;Raters FHC;Rave S;Reina G;Reißig M;Ropinski T;Schaarschmidt J;Seibold H;Thiele JP;Uekermann B;Unger S;Weeber R
通讯作者：
Weeber R
影响因子：
9.2
作者：
Alter, George;Gonzalez-Beltran, Alejandra;Rocca-Serra, Philippe
通讯作者：
Rocca-Serra, Philippe
影响因子：
9.8
作者：
Alterovitz, Gil;Dean, Dennis;Mazumder, Raja
通讯作者：
Mazumder, Raja