UC Davis Clinical and Translational Science Center

项目总结/文摘

PROJECT SUMMARY/ABSTRACT The Clinical and Translational Science Center (CTSC) at UC Davis serves as a catalyst to support biomedical research through services and resources to investigators, staff, scholars, trainees, and community partners. The CTSC partners with all UC Davis schools and colleges engaged in life science and translational research to support collaborative projects with the potential to improve health care. It provides a centralized resource dedicated to facilitating biomedical research through innovation, collaboration, and expertise. Specifically, the CTSC: 1. serves as a hub for clinical and translational research resources, which will serve as an invaluable resource for the success of this grant. (2) expands and refines a collaborative and innovative training and education environment, (3) integrates community partners into the translational research enterprise, (4) leverages crosscutting research resources, and (5) functions as a catalyst across the translational research spectrum. The focus of the CTSC informatics, bioethics and community engagement resources of the CTSC are to advance the efficient, scalable and ethical design and implementation of clinical research resources and data and collaborate with other CTSA hubs to leverage and share methods, practices and guidelines in this domain. The Research Data Ethics Maturity Model (README) project will seek build on the CTSCs commitment to training the next generation of translational data scientists, and identify the maturity of ethical frameworks used within translational data-science generating projects, capture gaps and classifications from project, stakeholder, tool and impact perspectives, and partner with new research development to provide enhanced use of ethical frameworks and resources throughout development data lifecycles. The project will generate a data ethics maturity framework for generalization to other environments, and new training and resources surrounding each stage of data science design, curation, modeling, and application.