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An Integrated Framework for Supporting Evidence-Based Institutional Research Data Management and Stewardship (link to poster)Introduced in the session: Implementing the Federal Data Strategy
Ge Peng (CISESS/NCEI), Jeff Privette (NCEI), Scott Hausman (NCEI), Edward Kearns (First Street Foundation), Otis Brown (CISESS), and Thomas Maycock (CISESS)
The rapidly increasing quantity and importance of digital research data make this an exciting but challenging time for organizations responsible for producing, managing, and stewarding data, especially for publicly funded digital research data. Key challenges include evolving stewardship requirements for federally and publicly funded research data and new mandates and guidelines from scientific societies and publishers. The multi-dimensional nature of data and information quality also contributes to the challenges of managing and stewarding institutional research data.
For U.S. federal agencies, important quality attributes include accessibility, accountability, integrity, interoperability, transparency, traceability, usability, and utility, while scientific societies and scholarly publishers tend to emphasize findability, accessibility, interoperability, and reuse, aka, FAIR. Quantifiably assessing how science data centers manage their data holdings, including the practices applied to individual datasets, is another big challenge. Gaps between requirements and implementations may need to be bridged, which may require integrating knowledge and expertise across multiple disciplines.
The latest U.S. OPEN Government Data Act calls for an evidence-based policy-making support process. We present a data-centric, holistic, iterative framework to help institutions address the challenges of managing their digital scientific data. Intended users of this framework include organizations seeking to ensure or improve their institutional research data management, to support stewardship compliance verification and reporting, and to ensure or improve the quality, including FAIRness, of individual datasets.
