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Related Experiment Video

Updated: Feb 24, 2026

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
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A Pragmatic Approach for Reproducible Research With Sensitive Data.

Bryan E Shepherd, Meridith Blevins Peratikos, Peter F Rebeiro

    American Journal of Epidemiology
    |August 24, 2017
    PubMed
    Summary

    Researchers can make sensitive data studies quasi-reproducible by sharing analysis code, simulated data, and results. This improves transparency and evaluation when original data sharing is not possible.

    Keywords:
    HIVde-identificationobservational datareproducible research

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    Area of Science:

    • Data Science
    • Research Methodology
    • Scientific Integrity

    Background:

    • Reproducible research is crucial for validating scientific findings and sharing methodologies.
    • Sharing original study data is a key requirement for reproducibility.
    • Sensitive data in certain research settings poses challenges to data sharing and thus reproducibility.

    Purpose of the Study:

    • To propose a pragmatic approach for achieving quasi-reproducibility in studies involving sensitive data.
    • To enhance transparency and critical evaluation of research where direct data sharing is not feasible.

    Main Methods:

    • Posting analysis code from the published study on a public website.
    • Generating and posting simulated data relevant to the study.
    • Applying the original analysis code to the simulated data to generate and post results.

    Main Results:

    • The proposed method allows for the transparent evaluation of research analyses.
    • This approach offers a significant improvement over current practices for studies with data limitations.

    Conclusions:

    • Quasi-reproducibility is an achievable and valuable goal for research with sensitive data.
    • Sharing code, simulated data, and derived results enhances research integrity and dissemination.