A Capability Maturity Model for Research Data Management

Welcome to CMM for RDM

Last modified by Arden Kirkland on 2014/04/20 13:48


The broad goals of this project are to document, foster and promulgate best practices in research data management (RDM), practices that support research transparency and the replication of scientific results. We do so in order to cultivate a new generation of researchers and data managers who are both the best practice beneficiaries and contributors. Furthermore, as more organizations invest in RDM, it has become increasingly important for administrators, researchers, and managers to be able to evaluate RDM process for sustainability, efficiency, and effectiveness, which requires a baseline for comparison.

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Our Team Members

Find out more about our team members by linking to their profiles:

Jian Qin, PI

Kevin Crowston, Co-PI

Charlotte Flynn, Doctoral RA

Arden Kirkland, Masters RA

Latest News


Building Capabilities for Sustainable Research Data Management Practices

Research data management (RDM) requires careful planning and sustained support and engagement from administration and community to achieve optimal performance. This workshop will introduce the Capability Maturity Model Framework (CMMF) for RDM and the Community Capability Model   and use these methodologies together with rubrics as the materials for workshop discussion, activities, and input from the participants. The Workshop will engage participants through a series of key research questions such as benefits of RDM assessment, benchmarking RDM capability, and key practices in CMM/CCMF. ...

Infrastructure, Standards, and Policies for  Research Data Management 

Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work.  ...

A Rubric for the CMM4RDM

Last fall I joined the team for the CMM4RDM as a research assistant . . . Once the model was fully developed, I then found it useful to take the ideas from each practice and turn them into a rubric that looks at what each maturity level would look like for each practice area. At the end of the page for each practice area within a chapter, a table displays the rubric for that section. The full rubric is also available as a PDF . . .  ...

Recent Changes

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05 Oct 2014

04 Aug 2014

02 Jul 2014

Created by Administrator on 2013/05/30 10:55

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