TY - JOUR
AU - Mitchell, Sonia Natalie
AU - Lahiff, Andrew
AU - Cummings, Nathan
AU - Hollocombe, Jonathan
AU - Boskamp, Bram
AU - Field, Ryan
AU - Reddyhoff, Dennis
AU - Zarębski, Kristian
AU - Wilson, Antony
AU - Viola, Bruno
AU - Burke, Martin
AU - Archibald, Blair
AU - Bessell, Paul R.
AU - Blackwell, Richard
AU - Boden, Lisa A.
AU - Brett, Alys
AU - Brett, Sam
AU - Dundas, Ruth
AU - Enright, Jessica
AU - Gonzalez-Beltran, Alejandra
AU - Harris, Claire
AU - Hinder, Ian
AU - Hughes, Christopher David
AU - Knight, Martin
AU - Mano, Vino
AU - McMonagle, Ciaran
AU - Mellor, Dominic
AU - Mohr, Sibylle
AU - Marion, Glenn
AU - Matthews, Louise
AU - McKendrick, Iain J.
AU - Pooley, Christopher Mark
AU - Porphyre, Thibaud
AU - Reeves, Aaron
AU - Townsend, Edward
AU - Turner, Robert
AU - Walton, Jeremy
AU - Reeve, Richard
PY - 2022
DA - October
TI - FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
VL - 380
IS - 2233
DO - https://dx.doi.org/10.1098/rsta.2021.0300
AB - 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.
PB - The Royal Society
UR - http://eprints.gla.ac.uk/273243/
KW - Coronavirus (COVID-19)
KW - Research methods
ER