Developing a Gold Standard Framework for Data Reproducibility in Biomedical and Health Research - Reproducibility is a cornerstone of scientific integrity, yet biomedical and health research continues to face substantial challenges in ensuring that published findings can be independently verified, reproduced, and translated into practice. Increasing complexity in research data, computational workflows, statistical methods, and emerging artificial intelligence (AI) applications has created new demands for transparent, standardized, and measurable approaches to evaluating reproducibility. Although existing principles such as FAIR (Findable, Accessible, Interoperable, and Reusable) data practices and reporting guidelines have advanced research transparency, there remains a critical gap: the absence of a comprehensive, empirically validated framework that defines measurable standards for assessing reproducibility across the entire research lifecycle. This project will develop and validate a Gold Standard Reproducibility Instrument (GSRI)—a comprehensive framework designed to assess reproducibility practices in biomedical and health research. The GSRI will establish evidence-based standards that integrate traditional reproducibility domains with emerging requirements related to artificial intelligence, machine learning, and computational research. The project is guided by principles of research integrity, transparency, methodological rigor, and responsible data stewardship. The project will be conducted through a multi-phase mixed-methods approach. First, the research team will conduct a comprehensive literature review and stakeholder consultation to identify essential reproducibility domains and develop an initial pool of assessment items. The preliminary GSRI framework will include seven core domains: (1) Data Documentation and Stewardship, (2) Data Processing Transparency, (3) Statistical Reproducibility, (4) Computational Reproducibility, (5) Transparency and Reporting, (6) Verification and Research Integrity, and (7) Research Integrity and Reproducibility Culture. AI-related reproducibility considerations will be embedded within each domain to address the rapidly evolving role of AI-enabled research methods. Second, the proposed GSRI items will undergo systematic expert evaluation using established content validity approaches. A national panel of biomedical and health research experts, including statisticians, methodologists, data scientists, and research integrity leaders, will evaluate each item’s importance, feasibility, and priority. Content validity will be assessed using Lawshe’s Content Validity Ratio (CVR) method and Content Validity Index (CVI) procedures. Items meeting predefined consensus thresholds will be retained, while items requiring refinement will be revised based on expert feedback. The project team brings extensive expertise in research methodology, data management, biomedical informatics, statistical analysis, artificial intelligence, and large-scale health data research. The investigators have extensive experience developing evidence-based data management standards, conducting national professional surveys, applying rigorous psychometric methods, and translating methodological frameworks into practical research tools. Lessons learned from prior Office of Research Integrity-supported Data Management Standard Operating Procedures (DMSOP) Toolkit development will inform the successful implementation and dissemination of the GSRI framework. The expected outcome of this project is a rigorously developed and validated reproducibility assessment framework that can be adopted by researchers, institutions, journals, funding agencies, and research oversight organizations. The GSRI will provide a practical mechanism for identifying strengths and gaps in reproducibility practices, promoting responsible research conduct, improving transparency, reducing irreproducibility, and strengthening public trust in biomedical and health research.
