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

Ana Cecilia Quiroga Gutierrez (), Daniel J. Lindegger, Ala Taji Heravi, Thomas Stojanov, Martin Sykora, Suzanne Elayan, Stephen J. Mooney, John A. Naslund, Marta Fadda and Oliver Gruebner
Additional contact information
Ana Cecilia Quiroga Gutierrez: Department of Health Sciences and Medicine, University of Lucerne, 6002 Luzern, Switzerland
Daniel J. Lindegger: Institute of Global Health, University of Geneva, 1211 Geneva, Switzerland
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

IJERPH, 2023, vol. 20, issue 2, 1-15

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.

Keywords: reproducibility; big data; digital epidemiology; urban public health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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