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Journal Articles Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Year : 2022

FAIR data pipeline: provenance-driven data management for traceable scientific workflows

Andrew Lahiff
Nathan Cummings
Bram Boskamp
Dennis Reddyhoff
Antony Wilson
Bruno Viola
Martin Burke
Blair Archibald
Paul Bessell
Lisa Boden
Alys Brett
Ian Hinder
Martin Knight
Vino Mano
  • Function : Author
Aaron Reeves
Jeremy Walton

Abstract

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’.
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hal-03752208 , version 1 (20-03-2024)

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Sonia Natalie Mitchell, Andrew Lahiff, Nathan Cummings, Jonathan Hollocombe, Bram Boskamp, et al.. FAIR data pipeline: provenance-driven data management for traceable scientific workflows. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380 (2233), pp.20210300. ⟨10.1098/rsta.2021.0300⟩. ⟨hal-03752208⟩
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