Recognizing, reporting and reducing the data curation debt of cohort studies

Butters, Oliver W ORCID: 0000-0003-0354-8461, Wilson, Rebecca C ORCID: 0000-0003-2294-593X and Burton, Paul R (2020) Recognizing, reporting and reducing the data curation debt of cohort studies. International Journal of Epidemiology, 49 (4). pp. 1067-1074.

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Official URL: http://dx.doi.org/10.1093/ije/dyaa087

Abstract

<jats:title>Abstract</jats:title> <jats:p>Good data curation is integral to cohort studies, but it is not always done to a level necessary to ensure the longevity of the data a study holds. In this opinion paper, we introduce the concept of data curation debt—the data curation equivalent to the software engineering principle of technical debt. Using the context of UK cohort studies, we define data curation debt—describing examples and their potential impact. We highlight that accruing this debt can make it more difficult to use the data in the future. Additionally, the long-running nature of cohort studies means that interest is accrued on this debt and compounded over time—increasing the impact a debt could have on a study and its stakeholders. Primary causes of data curation debt are discussed across three categories: longevity of hardware, software and data formats; funding; and skills shortages. Based on cross-domain best practice, strategies to reduce the debt and preventive measures are proposed—with importance given to the recognition and transparent reporting of data curation debt. Describing the debt in this way, we encapsulate a multi-faceted issue in simple terms understandable by all cohort study stakeholders. Data curation debt is not only confined to the UK, but is an issue the international community must be aware of and address. This paper aims to stimulate a discussion between cohort studies and their stakeholders on how to address the issue of data curation debt. If data curation debt is left unchecked it could become impossible to use highly valued cohort study data, and ultimately represents an existential risk to studies themselves.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Data curation, data management, cohort studies
Depositing User:	Symplectic Admin
Date Deposited:	03 Jul 2020 10:30
Last Modified:	18 Jan 2023 23:47
DOI:	10.1093/ije/dyaa087
Open Access URL:	https://doi.org/10.1093/ije/dyaa087
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3092654