Peng_Etal_CODATA_20190919_Figshare.pdf (6.7 MB)
CODATA2019Beijing - A Holistic Framework for Supporting Evidence-Based Institutional Research Data Management
presentation
posted on 2019-09-24, 07:33 authored by Ge PengGe Peng, Jeffery L. Privette, Ed Kearns, Nancy Ritchey, Otis Brown, Curt Tilmes, Sky BristolSky Bristol, Hampapuram RamapriyanHampapuram Ramapriyan, Tom MaycockThe 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 increased and evolving stewardship requirements for federally or publicly funded research data imposed by governmental and/or funding agencies, and guidelines and commitments from scientific societies and scholarly publishers. The multi-perspective and multi-dimensional nature of data and information quality also contributes to the key challenges of institutional research data management (RDM) and stewardship.
The quality of data products and services is important in decision-making regarding data-use. Associated information helps enable data use and reuse. To unlock and improve the value of their data, data centers are seeking to improve their data management and stewardship capability.
We present a data-centric, holistic, iterative framework to help institutions address the challenges of managing their scientific data. This framework:
● is based on the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept,
● provides a tool for addressing all stewardship functions as a consistent, integrated system,
● includes application of maturity assessment models,
● allows for quantitative evaluation of how organizations manage their stewardship activities, and
● supports informed decision-making and continual improvement towards compliance with federal, agency, and user requirements.
Intended users of this framework include organizations seeking to ensure or improve their institutional RDM, to support stewardship compliance verification and reporting, and to endure or improve the quality, including FAIRness, of individual datasets of their data holdings.
