@article {9998, title = {Pursuing best performance in research data management by using the Capability Maturity Model and rubrics}, journal = {Journal of eScience Librarianship}, volume = {6}, year = {2017}, pages = {e1113}, abstract = {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{\textemdash}or indeed lack of{\textemdash}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{\textquoteright}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. }, doi = {10.7191/jeslib.2017.1113}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/CMM\%20paper\%20to\%20distribute.pdf}, author = {Jian Qin and Kevin Crowston and Arden Kirkland} } @article {2014, title = {A Capability Maturity Model for Research Data Management}, year = {2014}, institution = {Syracuse University School of Information Studies}, address = {Syracuse, NY}, 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 {\textendash} or lack of {\textendash} 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{\textquoteright}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.

}, url = {https://surface.syr.edu/istpub/184/}, author = {Jian Qin and Kevin Crowston and Arden Kirkland} }