Abstract
This chapter explores the development of university-wide research data management conversations and collaborations involving key stakeholders at a Research 1 (R1) higher education institution in the southeastern United States of America. Research data management conversations led to collaborations of several key stakeholders across campus (i.e., university, office of research, research compliance office, information technology [IT], researchers, academic units, library) resulting in the development of an inaugural Supporting Data Management at University of Florida (UF) Proposal to the Office of Research draft document in 2019. Funding agencies, research foundations, and research associations increasingly request plans outlining how scientific data from funded research will be annotated, managed, shared, and stored. The National Institutes of Health (NIH) new Policy on Data Management and Sharing becomes effective January 25, 2023. The authors employed a participatory action research method to explore evolving aspects of the data science ecosystem, including research data management.
TopIntroduction
Innovations in technology and computational availability have allowed researchers to generate an unprecedented volume of health-related data. Thus, research data management (RDM) through all stages of the research cycle has become increasingly important to improve discoverability of existing resources and improving data within these resources. Researchers that create research data must add context and meaning (metadata), undertake early data management (file naming, data organization, location), and follow applicable standards for data creation, description, and representation. Even with many online RDM training resources, there is still a need for more personalized findable, accessible, interoperable, reusable (FAIR) data training (ANDS, 20217) and RDM training for researchers, PIs, and trainees. Academic libraries as partners in the data science ecosystem can provide RDM training support via research projects.
Key Terms in this Chapter
Metadata: Metadata is structured, descriptive information about analog and digital objects.
Reproducibility: An approach to sharing methods and workflows to repeat scientific research.
Digital Preservation: A data lifecycle management process of maintaining the authenticity, integrity, and security of curated research data within a standards-based repository or digital content management system for long-term archival preservation.
Data Management Planning: A data lifecycle management process comprised of departmental, institutional, or organization policies and procedures governing the creation, organization, dissemination, preservation, and comprehensive lifecycle management of research data and information in accordance with relevant standards, best practices, and guidelines. Data management planning includes data curation, digital curation, and digital preservation. Data management and curation services include data management planning.
Data Curation: A data lifecycle management process of providing descriptive, annotative, and representative information for research data through metadata.
Provenance: The documentation describing the origin, lineage, and ownership of research data.
Data Format: A package of information stored as a data file.
Open Science: An approach to the sharing and transparency of scientific knowledge processes.