Abstract
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.
Highlights
Research in science, social science, and the humanities is increasingly data-intensive, highly collaborative, and highly computational at a large scale
Improved research data management (RDM) was recognized as a critical area almost a decade ago (Gray 2009) with action needed across the data lifecycle
To support assessment and improvement of RDM practices that increase its reliability, we developed a capability maturity model for RDM (CMM for RDM)
Summary
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. 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
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