FAIR data pipeline: provenance-driven data management for traceable scientific workflows.
FAIR data pipeline: provenance-driven data management for traceable scientific workflows.
复制标题
FAIR数据管道：溯源驱动的数据管理，用于可追溯的科学工作流程。
DOI：
10.1098/rsta.2021.0300
复制
发表时间：
2022-10-03
期刊：
影响因子：
5
通讯作者：
Reeve, Richard
中科院分区：
文献类型：
--
作者：
Mitchell, Sonia Natalie;Lahiff, Andrew;Cummings, Nathan;Hollocombe, Jonathan;Boskamp, Bram;Field, Ryan;Reddyhoff, Dennis;Zarebski, Kristian;Wilson, Antony;Viola, Bruno;Burke, Martin;Archibald, Blair;Bessell, Paul;Blackwell, Richard;Boden, Lisa A. A.;Brett, Alys;Brett, Sam;Dundas, Ruth;Enright, Jessica;Gonzalez-Beltran, Alejandra N. N.;Harris, Claire;Hinder, Ian;Hughes, Christopher David;Knight, Martin;Mano, Vino;McMonagle, Ciaran;Mellor, Dominic;Mohr, Sibylle;Marion, Glenn;Matthews, Louise;McKendrick, Iain J. J.;Pooley, Christopher Mark;Porphyre, Thibaud;Reeves, Aaron;Townsend, Edward;Turner, Robert;Walton, Jeremy;Reeve, Richard
为了解和防止疾病传播而进行的现代流行病学分析关键取决于数据的获取和使用。快速变化的数据，例如在疾病爆发期间发生变化的数据流，尤其具有挑战性。由于数据在使用时被不精确地识别，数据管理进一步复杂化。公众对这种分析所产生的政策决定的信任很容易受到损害，而且往往很低，在没有附带证据的情况下声称“遵循科学”时，就会产生愤世嫉俗的情绪。通过开放软件追溯此类决策的出处到原始数据将澄清这些证据，提高决策过程的透明度。在这里，我们展示了一个可查找、可扩展、可互操作和可重用（FAIR）的数据管道。虽然它是在COVID-19大流行期间开发的，但它允许在分析消耗任何数据时轻松注释任何数据，或者相反地通过分析或建模源代码追溯科学输出的来源到原始数据。这一工具为公众和科学家同行提供了一种机制，通过检查科学证据的出处来更好地评估科学证据，同时允许科学家支持政策制定者公开证明他们的决定是正确的。我们认为，应在面向政策的研究的所有领域推广使用此类工具。这篇文章是“模拟现实生活中的流行病的技术挑战和克服这些挑战的例子”主题的一部分。
Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of ‘following the science’ are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
影响因子：
4.3
作者：
Cox SJD;Gonzalez-Beltran AN;Magagna B;Marinescu MC
通讯作者：
Marinescu MC
DOI：
10.1016/s1473-3099(20)30984-1
发表时间：
2021-04
期刊：
The Lancet. Infectious diseases
影响因子：
--
作者：
Davies NG;Barnard RC;Jarvis CI;Russell TW;Semple MG;Jit M;Edmunds WJ;Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group;ISARIC4C investigators
通讯作者：
ISARIC4C investigators
影响因子：
14.9
作者：
Afgan E;Baker D;Batut B;van den Beek M;Bouvier D;Cech M;Chilton J;Clements D;Coraor N;Grüning BA;Guerler A;Hillman-Jackson J;Hiltemann S;Jalili V;Rasche H;Soranzo N;Goecks J;Taylor J;Nekrutenko A;Blankenberg D
通讯作者：
Blankenberg D