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Enhancing (publications on) data quality: Deeper data minding and fuller data confession

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  • Xiao‐Li Meng

Abstract

Statistics typically treats data as inputs for analysis, whereas the broader data science enterprise deals with the entire data life cycle, including the phases that output data. This commentary argues that it would benefit statistics and (data) science if we statisticians were also to treat data as products in and of themselves, and accordingly subject them to data minding, a stringent quality inspection process that scrutinizes data conceptualization, data pre‐processing, data curation and data provenance, in addition to data collection, the traditional objective of our emphasis before data analysis. A concrete step in promoting deeper data minding is to encourage fuller data confession in (statistical) publications, that is, to entice—or at least not to disincentivize—the authors into providing more details on the genealogy of a given body of data, including an account of its deliberations, especially with respect to sources of adverse influence on data quality. The collection of articles in this special issue (on data science for societies) provides both the inspiration and aspiration for deeper data minding and fuller data confession.

Suggested Citation

  • Xiao‐Li Meng, 2021. "Enhancing (publications on) data quality: Deeper data minding and fuller data confession," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1161-1175, October.
    • Handle: RePEc:bla:jorssa:v:184:y:2021:i:4:p:1161-1175
    DOI: 10.1111/rssa.12762
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    1. Seppo Virtanen & Mark Girolami, 2021. "Spatio‐temporal mixed membership models for criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1220-1244, October.
    2. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    3. Marvin N. Wright & Sasmita Kusumastuti & Laust H. Mortensen & Rudi G. J. Westendorp & Thomas A. Gerds, 2021. "Personalised need of care in an ageing society: The making of a prediction tool based on register data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1199-1219, October.
    4. Angelika Geroldinger & Milan Hronsky & Florian Endel & Gottfried Endel & Rainer Oberbauer & Georg Heinze, 2021. "Estimation of the prevalence of chronic kidney disease in people with diabetes by combining information from multiple routine data collections," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1260-1282, October.
    5. David Bolin & Vilhelm Verendel & Meta Berghauser Pont & Ioanna Stavroulaki & Oscar Ivarsson & Erik Håkansson, 2021. "Functional ANOVA modelling of pedestrian counts on streets in three European cities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1176-1198, October.
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    7. S. O. Tickle & I. A. Eckley & P. Fearnhead, 2021. "A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1303-1325, October.
    8. Jingxian You & Paul Expert & Céire Costelloe, 2021. "Using text mining to track outbreak trends in global surveillance of emerging diseases: ProMED‐mail," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1245-1259, October.
    9. Pierre Masselot & Fateh Chebana & Céline Campagna & Éric Lavigne & Taha B.M.J. Ouarda & Pierre Gosselin, 2021. "Machine learning approaches to identify thresholds in a heat‐health warning system context," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1326-1346, October.
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