PROJECT SUMMARY {See instructions): This project has two main goals: measuring the impact of research data reuse on diversity and novelty, and identifying synergistic data-method-researcher combinations to spur scientific discovery. Access to scientific data is critical for advancing research quality and efficiency. Yet, bibliometric studies have uncovered biases in publication impact and citation patterns, raising concerns that such disparities might affect how researchers reuse data. Developing robust metrics to identify and rectify these imbalances is the focus of this work. The project will construct networks and graph databases that connect research objects such as publications, analysis code, datasets, and variables. These networks will provide insight into datasets' influence on research diversity, author interactions, and code reuse for scientific advancements. Focusing on biomedical research allows for a varied examination of the types of data, outputs, and potential high-impact findings, like new therapeutics or research methods. Initially, the project will create knowledge graphs from ICPSR and PhysioNet datasets, supplemented with author and code metadata from databases like Dimensions and OpenAlex. Subsequent phases will assess the impact of data through diversity and novelty metrics, refined in consultation with stakeholders. Finally, the project will recommend strategic partnerships to promote equitable data use and groundbreaking reuse applications.