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Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

Author

Listed:
  • Ana Cecilia Quiroga Gutierrez

    (Department of Health Sciences and Medicine, University of Lucerne, 6002 Luzern, Switzerland
    These authors contributed equally to this work.)

  • Daniel J. Lindegger

    (Institute of Global Health, University of Geneva, 1211 Geneva, Switzerland
    These authors contributed equally to this work.)

  • Ala Taji Heravi

    (CLEAR Methods Center, Department of Clinical Research, Division of Clinical Epidemiology, University Hospital Basel and University of Basel, 4031 Basel, Switzerland)

  • Thomas Stojanov

    (Department of Orthopaedic Surgery and Traumatology, University Hospital of Basel, 4031 Basel, Switzerland)

  • Martin Sykora

    (School of Business and Economics, Centre for Information Management, Loughborough University, Loughborough LE11 3TU, UK)

  • Suzanne Elayan

    (School of Business and Economics, Centre for Information Management, Loughborough University, Loughborough LE11 3TU, UK)

  • Stephen J. Mooney

    (Department of Epidemiology, University of Washington, Seattle, WA 98195, USA)

  • John A. Naslund

    (Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA)

  • Marta Fadda

    (Institute of Public Health, Università Della Svizzera Italiana, 6900 Lugano, Switzerland)

  • Oliver Gruebner

    (Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland
    Department of Geography, University of Zurich, 8057 Zurich, Switzerland)

Abstract

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

Suggested Citation

  • Ana Cecilia Quiroga Gutierrez & Daniel J. Lindegger & Ala Taji Heravi & Thomas Stojanov & Martin Sykora & Suzanne Elayan & Stephen J. Mooney & John A. Naslund & Marta Fadda & Oliver Gruebner, 2023. "Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1473-:d:1034751
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    References listed on IDEAS

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