%0 Journal Article %J Journal of eScience Librarianship %D 2017 %T Pursuing best performance in research data management by using the Capability Maturity Model and rubrics %A Jian Qin %A Kevin Crowston %A Arden Kirkland %X OBJECTIVE: To support assessment and improvement of research data management (RDM) practices to increase the reliability of RDM, 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 indeed 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 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 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. %B Journal of eScience Librarianship %V 6 %P e1113 %G eng %N 2 %R 10.7191/jeslib.2017.1113 %> https://crowston.syr.edu/sites/crowston.syr.edu/files/CMM%20paper%20to%20distribute.pdf %0 Report %D 2014 %T A Capability Maturity Model for Research Data Management %A Jian Qin %A Kevin Crowston %A Arden Kirkland %X

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.

%I Syracuse University School of Information Studies %C Syracuse, NY %G eng %U https://surface.syr.edu/istpub/184/ %0 Conference Proceedings %B American Society for Information Science and Technology Annual Meeting %D 2011 %T A capability maturity model for scientific data management: Evidence from the literature %A Kevin Crowston %A Jian Qin %B American Society for Information Science and Technology Annual Meeting %C New Orleans, LA %8 10/2011 %G eng %> https://crowston.syr.edu/sites/crowston.syr.edu/files/110718%20CMM%20ASISTpaper.pdf %0 Generic %D 2010 %T A Capability Maturity Model for Scientific Data Management %A Kevin Crowston %A Jian Qin %K Data Management %K eScience %X In this paper, we propose a capability maturity model (CMM) for scientific data management (SDM) practices, with the goal of supporting assessment and improvement of these practices. The CMM describes key process areas and practices necessary for effective SDM. The CMM further characterizes organizations by the level of maturity of these processes, meaning the organizational capability to reliably perform the processes. We suggest that this framework will be useful to organizations in evaluating and planning improvements to their SDM practices. %B American Society for Information Science and Technology (ASIST) Annual Conference %C Pittsburgh, PA %8 10/2010 %9 Working Paper %> https://crowston.syr.edu/sites/crowston.syr.edu/files/CMM%20for%20DM%20to%20share.pdf %> https://crowston.syr.edu/sites/crowston.syr.edu/files/100714%20ASIST%20Poster%20final_0.pdf