About this resource

    Type of resources
    Reading
    Education level(s)
    Graduate / Professional
    Primary user(s)
    student, teacher
    Subject area(s)
    Life Science, Social Science
    Language(s)
    English
    Cluster
    Transparency and Reproducibility in Computation and Analysis, FAIR Data and Materials
    Last updated
    8 November 2026

    Transparency of CHI Research Artifacts: Results of a Self-Reported Survey

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    Level Graduate / Professional · For student, teacher · Language English

    Abstract

    Several fields of science are experiencing a “replication crisis” that has negatively impacted their credibility. Assessing the validity of a contribution via replicability of its experimental evidence and reproducibility of its analyses requires access to relevant study materials, data, and code. Failing to share them limits the ability to scrutinize or build-upon the research, ultimately hindering scientific progress. Understanding how the diverse research artifacts in HCI impact sharing can help produce informed recommendations for individual researchers and policy-makers in HCI. Therefore, we surveyed authors of CHI 2018–2019 papers, asking if they share their papers’ research materials and data, how they share them, and why they do not. The results (N = 460/1356, 34% response rate) show that sharing is uncommon, partly due to misunderstandings about the purpose of sharing and reliable hosting. We conclude with recommendations for fostering open research practices. This paper and all data and materials are freely available at https://osf.io/csy8q

    Keywords Data · Reproducibility