Full-Length Paper

Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

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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.

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.

Keywords: Capability Maturity Model, Research Data Management

How to Cite:

Qin, J., Crowston, K. & Kirkland, A., (2017) “Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics”, Journal of eScience Librarianship 6(2): 3. doi: https://doi.org/10.7191/jeslib.2017.1113

Rights: Copyright Qin et al. © 2017