A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis-generation

  1. Daniel S Quintana  Is a corresponding author
  1. University of Oslo, Norway


Open research data provides considerable scientific, societal, and economic benefits. However, disclosure risks can sometimes limit the sharing of open data, especially in datasets that include sensitive details or information from individuals with rare disorders. This article introduces the concept of synthetic datasets, which is an emerging method originally developed to permit the sharing of confidential census data. Synthetic datasets mimic real datasets by preserving their statistical properties and the relationships between variables. Importantly, this method also reduces disclosure risk to essentially nil as no record in the synthetic dataset represents a real individual. This practical guide with accompanying R script enables biobehavioural researchers to create synthetic datasets and assess their utility via the synthpop R package. By sharing synthetic datasets that mimic original datasets that could not otherwise be made open, researchers can ensure the reproducibility of their results and facilitate data exploration while maintaining participant privacy.

Data availability

Data and analysis scripts are available at the article's Open Science Framework webpage https://osf.io/z524n/

The following previously published data sets were used
    1. Jones BC
    2. DeBruine L
    (2019) Sociosexuality and self-rated attractiveness
    Open Science Framework, DOI: 10.17605/OSF.IO/6BK3W.

Article and author information

Author details

  1. Daniel S Quintana

    Institute of Clinical Medicine, University of Oslo, Oslo, Norway
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2876-0004


Novo Nordisk Foundation (Excellence grant NNF16OC0019856)

  • Daniel S Quintana

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Publication history

  1. Received: November 1, 2019
  2. Accepted: March 11, 2020
  3. Accepted Manuscript published: March 11, 2020 (version 1)
  4. Version of Record published: April 1, 2020 (version 2)


© 2020, Quintana

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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  1. Daniel S Quintana
A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis-generation
eLife 9:e53275.

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