Recommended practices for the open publication of epidemiological research data and reports
DOI:
https://doi.org/10.56294/dm2023108Keywords:
Epidemiology, Open Publishing, Transparent Data, Open Access, Public HealthAbstract
Introduction: epidemiology plays a fundamental role in public health by providing evidence for decision making. However, the lack of access to data limits the evaluation and replicability of epidemiological studies.
Objective: establish recommended practices for the open publication of epidemiological research data and reports, in order to maximize their value and accessibility.
Method: a systematic review of open publication guidelines was conducted. Good practices were identified in the stages of collection, storage, publication and dissemination of epidemiological information.
Results: consensus was found on the importance of using standardized instruments, documenting metadata, storing data in repositories with open licenses, assigning digital identifiers and publishing in open access journals.
Conclusions: the adoption of these recommended practices will substantially improve the quality, replicability and use of epidemiological research. This will strengthen transparency, scientific collaboration and evidence-based decision making
References
1. Mata-Orozco M, López-Caldera M, Meza Palma D, Labrador Parra A, Sarco Lira J, Pérez H, et al. Enfoques emergentes de la epidemiología: una mirada desde la transdisciplinariedad. Comunidad y Salud 2016; 14:50–62.
2. Rodríguez E, Arias Gómez A, Sifontes S, Luna H, Gaiti J. Epidemiología. Archivos Venezolanos de Puericultura y Pediatría 2010; 73:028–34.
3. Savage CJ, Vickers AJ. Empirical Study of Data Sharing by Authors Publishing in PLoS Journals. PLoS ONE 2009; 4:e7078. https://doi.org/10.1371/journal.pone.0007078.
4. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd Edition. Philadelphia, USA: Lippincott Williams & Wilkins; 2008.
5. Ioannidis JPA, Boyack KW, Small H, Sorensen AA, Klavans R. Bibliometrics: Is your most cited work your best? Nature 2014; 514:561–2. https://doi.org/10.1038/514561a.
6. Rothman KJ, Gallacher JE, Hatch EE. Why representativeness should be avoided. International Journal of Epidemiology 2013; 42:1012–4. https://doi.org/10.1093/ije/dys223.
7. Vallejo RG. Metaverse and translation studies: analysis of machine translation. Metaverse Basic and Applied Research 2023; 2:38. https://doi.org/10.56294/mr202338.
8. Van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R, Herbst AJ, et al. A systematic review of barriers to data sharing in public health. BMC Public Health 2014; 14:1144. https://doi.org/10.1186/1471-2458-14-1144.
9. Enago Academy. Who Benefits From Open Access? Enago Academy 2017. https://www.enago.com/academy/benefits-open-access/.
10. Birkhead GS, Klompas M, Shah NR. Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health. Annu Rev Public Health 2015; 36:345–59. https://doi.org/10.1146/annurev-publhealth-031914-122747.
11. Montano M de las NV, Álvarez MK. The educational and pedagogical intervention in scientific research. Community and Interculturality in Dialogue 2023; 3:70. https://doi.org/10.56294/cid202370.
12. Vandenbroucke JP, Von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. International Journal of Surgery 2014; 12:1500–24. https://doi.org/10.1016/j.ijsu.2014.07.014.
13. Castillo-Gonzalez W. ChatGPT and the future of scientific communication. Metaverse Basic and Applied Research 2022; 1:8. https://doi.org/10.56294/mr20228.
14. Crichton GE, Murphy KJ, Bryan J. Dairy intake and cognitive health in middle-aged South Australians. Asia Pac J Clin Nutr 2010;19:161–71.
15. Wallis JC, Rolando E, Borgman CL. If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE 2013; 8:e67332. https://doi.org/10.1371/journal.pone.0067332.
16. Castillo-González W. The importance of human supervision in the use of ChatGPT as a support tool in scientific writing. Metaverse Basic and Applied Research 2023; 2:29–29. https://doi.org/10.56294/mr202329.
17. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. The Lancet 2020; 395:795–808. https://doi.org/10.1016/S0140-6736(19)32008-2.
18. Gonzalez-Argote J. Navigating the Labyrinth of Communities and Interculturality. Community and Interculturality in Dialogue 2021; 1:1–1. https://doi.org/10.56294/cid20211.
19. Kristman V, Manno M, Côté P. Loss to Follow-Up in Cohort Studies: How Much is Too Much? Eur J Epidemiol 2003; 19:751–60. https://doi.org/10.1023/B:EJEP.0000036568.02655.f8.
20. Leveau CM. Difusión espacio-temporal de muertes por COVID-19 en Argentina. Revista Panamericana de Salud Pública 2021; 45:1. https://doi.org/10.26633/RPSP.2021.3.
21. Moher D, Liberati A, Tetzlaff J, Altman D. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Ann Intern Med 2009;151:264. https://doi.org/10.7326/0003-4819-151-4-200908180-00135.
22. Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the Reporting of Observational Studies in Epidemiology - nutritional epidemiology (STROBE-nut): An extension of the STROBE statement. Nutr Bull 2016; 41:240–51. https://doi.org/10.1111/nbu.12217.
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Copyright (c) 2023 Lucio Arnulfo Ferrer Peñaranda, Lindomira Castro Llaja , Mercedes Lulilea Ferrer Mejía, Zoila Rosa Díaz Tavera, Ramirez Wong , Leonardo Velarde Dávila , Roberto Carlos Dávila Morán (Author)
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