IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v50y2024i43.html
   My bibliography  Save this article

Open science practices in demographic research: An appraisal

Author

Listed:
  • Ugofilippo Basellini

    (Max-Planck-Institut für Demografische Forschung)

Abstract

Background: In the light of recent concerns about the reliability of scientific research, the open science movement has attracted considerable attention and interest from a variety of sources, including researchers, research institutions, the business sector, intergovernmental organisations, the media, and the public. However, the current extent of openness in demographic research remains unknown. Methods: All relevant publications in four leading journals of anglophone demography – Demography, Population and Development Review, Population Studies, and Demographic Research – over the last decade are analysed. Using a text-search algorithm, two quantitative metrics of open scientific knowledge are estimated: the share of publications that can be openly accessed, and the share of publications providing open software codes for reproducibility or replicability purposes. Results: Two contrasting patterns emerge from these indicators. Access to demographic research papers is increasingly available to everyone, with more than 90% of open-access publications in 2023. Conversely, the provision of open software codes has been and still remains considerably low, with only small signs of improvement over time. Over the last three years, on average 31% of articles in Demographic Research provided these materials and only about 12% in the other journals. Contribution: This reflection provides the first assessment of the adoption of some open science practices in demographic research and their evolution over the last decade. An urgent change is needed in the sharing of software codes (along with the data used, where possible) to contribute to the advancement of demographic research. Some recommendations for promoting this change are discussed.

Suggested Citation

  • Ugofilippo Basellini, 2024. "Open science practices in demographic research: An appraisal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(43), pages 1265-1280.
  • Handle: RePEc:dem:demres:v:50:y:2024:i:43
    DOI: 10.4054/DemRes.2024.50.43
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol50/43/50-43.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.4054/DemRes.2024.50.43?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Holly Else & Richard Van Noorden, 2021. "The fight against fake-paper factories that churn out sham science," Nature, Nature, vol. 591(7851), pages 516-519, March.
    2. Richard Van Noorden, 2023. "More than 10,000 research papers were retracted in 2023 — a new record," Nature, Nature, vol. 624(7992), pages 479-481, December.
    3. Freese, Jeremy & Peterson, David, 2017. "Replication in Social Science," SocArXiv 5bck9, Center for Open Science.
    4. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    5. Vicente-Saez, Ruben & Martinez-Fuentes, Clara, 2018. "Open Science now: A systematic literature review for an integrated definition," Journal of Business Research, Elsevier, vol. 88(C), pages 428-436.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Basellini, Ugofilippo, 2023. "Open science practices in demographic research: an appraisal," SocArXiv vrcdh, Center for Open Science.
    2. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    3. Turcan Nelly & Rusu Andrei & Cujba Rodica, 2019. "Study on the Mapping of Research Data in the Republic of Moldova in the Context of Open Science," International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, Sciendo, vol. 9(1), pages 11-22, June.
    4. Segurado, Pedro & Gutiérrez-Cánovas, Cayetano & Ferreira, Teresa & Branco, Paulo, 2022. "Stressor gradient coverage affects interaction identification," Ecological Modelling, Elsevier, vol. 472(C).
    5. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
    6. Oliver Schilke & Sheen S. Levine & Olenka Kacperczyk & Lynne G. Zucker, 2019. "Call for Papers-Special Issue on Experiments in Organizational Theory," Organization Science, INFORMS, vol. 30(1), pages 232-234, February.
    7. Lopez, Belen & Rangel, Celia & Fernández, Manuel, 2022. "The impact of corporate social responsibility strategy on the management and governance axis for sustainable growth," Journal of Business Research, Elsevier, vol. 150(C), pages 690-698.
    8. Michaelides, Michael, 2021. "Large sample size bias in empirical finance," Finance Research Letters, Elsevier, vol. 41(C).
    9. Margarida Rodrigues & Cidália Oliveira & MárioFranco & Ana Daniel, 2024. "A Bibliometric Study About the Rural Creative Class: Proposal of a Conceptual Framework and Future Agenda," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 15278-15303, September.
    10. Kelter, Riko, 2022. "Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    11. Xian Jin Xie, 2019. "Research Reproducibility and p-value Threshold," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 22(5), pages 16934-16936, November.
    12. Arandjelović, Ognjen, 2023. "A Case for `Killer Robots': Why in the Long Run Martial AI May Be Good for Peace," SocArXiv 9kja8, Center for Open Science.
    13. Chatelain, Jean-Bernard & Ralf, Kirsten, 2021. "Inference on time-invariant variables using panel data: A pretest estimator," Economic Modelling, Elsevier, vol. 97(C), pages 157-166.
    14. Karmakar, Bisheswar & Pal, Sucharita & Gopikrishna, Konga & Tiwari, Onkar Nath & Halder, Gopinath, 2022. "Injection of superheated C1 and C3 alcohols in non-edible Pongamia pinnata oil for semi-continuous uncatalyzed biodiesel synthesis," Renewable Energy, Elsevier, vol. 185(C), pages 850-861.
    15. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    16. Franz Neuberger & Tobias Rüttenauer & Martin Bujard, 2022. "Where does public childcare boost female labor force participation? Exploring geographical heterogeneity across Germany 2007–2017," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(24), pages 693-722.
    17. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
    18. David Spiegelhalter, 2017. "Trust in numbers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 948-965, October.
    19. Eszter Czibor & David Jimenez‐Gomez & John A. List, 2019. "The Dozen Things Experimental Economists Should Do (More of)," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 371-432, October.
    20. Haas Franz, 2016. "Reappraisal of Austrian Business Confidence Survey 2015 for Mainland China," Proceedings of FIKUSZ 2016, in: Regina Zsuzsánna Reicher (ed.),Proceedings of FIKUSZ '16, pages 57-64, Óbuda University, Keleti Faculty of Business and Management.

    More about this item

    Keywords

    open access data; reproducibility; replicability; demography; population studies;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dem:demres:v:50:y:2024:i:43. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.