Objective: To support the assessment and improvement of research data management (RDM) practices to increase its reliability, this paper describes the development of a capability maturity model (CMM) for RDM. Improved RDM is now a critical need, but low awareness of – or lack of – data management is still common among research projects.
Methods: A CMM includes four key elements: key practices, key process areas, maturity levels, and generic processes. These elements were determined for RDM by a review and synthesis of the published literature on and best practices for RDM.
Results: The RDM CMM includes five chapters describing five key process areas for research data management: 1) data management in general; 2) data acquisition, processing, and quality assurance; 3) data description and representation; 4) data dissemination; and 5) repository services and preservation. In each chapter, key data management practices are organized into four groups according to the CMM’s generic processes: commitment to perform, ability to perform, tasks performed, and process assessment (combining the original measurement and verification). For each area of practice, the document provides a rubric to help projects or organizations assess their level of maturity in RDM.
Conclusions: By helping organizations identify areas of strength and weakness, the RDM CMM provides guidance on where effort is needed to improve the practice of RDM.
Capability Maturity Model, Research Data Management
This project is supported by the Inter-university Consortium for Political and Social Research / Sloan Foundation Challenge Grant.
Qin, Jian, Kevin Crowston, and Arden Kirkland. 2017. "Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics." Journal of eScience Librarianship 6(2): e1113. https://doi.org/10.7191/jeslib.2017.1113
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