A Conceptual Enterprise Framework for Managing Scientific Data Stewardship

• Main Authors: Ge Peng, Jeffrey L. Privette, Curt Tilmes, Sky Bristol, Tom Maycock, John J. Bates, Scott Hausman, Otis Brown, Edward J. Kearns
• Format: Article
• Language: English
• Published: Ubiquity Press 2018-06-01
• Series: Data Science Journal
• Subjects: Scientific Data Stewardship; Information Management; Enterprise Framework; Maturity Matrix; PDCA-cycle; Research Data; Open Data; Domain Stewards
• Online Access: https://datascience.codata.org/articles/749

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.