A Capability Maturity Model for Research Data Management
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Building Capabilities for Sustainable Research Data Management Practices

Last modified by Jian Qin on 2016/07/11 09:49

Jul 11 2016

As more organizations invest in Research Data Management (RDM), it has become increasingly important for institutional administrators, researchers, and managers to be able to evaluate RDM process for sustainability, efficiency, and effectiveness, which requires a baseline for comparison. The goal of this workshop is to raise the awareness of process management and assessment for RDM and to learn how to apply a Capability Maturity Model (CMM) for RDM (Crowston & Qin, 2011) for the purpose of process assessment.

The original CMM was developed at the Software Engineering Institute (SEI) at Carnegie Mellon University to support improvements in the reliability of software development organizations, that is, in their ability to develop quality software on time and within budget. While the organizational maturity levels are the most well-known aspect of the SEI CMM (Paulk et al., 1993), its heart is the description of the key practices clustered in a set of process areas. The structure of a CMM has been applied in a variety of domains for structuring process and performance assessment. RDM practice faces challenges similar to those faced by software engineering organizations, which makes the structure of the CMM suitable for helping structure efforts to improve RDM practices.

As a tool to increase the reliability of RDM, since 2011 we have been developing a CMM for RDM by gathering evidence from literature and empirical observations and identifying and clustering key RDM practices. The current draft CMM for RDM includes five specific RDM practice areas: 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. The first draft of CMM for RDM was completed in 2014 with rubrics for using the CMM for RDM model to assess research data management processes and to understand where the project or institution stands in terms of the maturity level.

In parallel, a Community Capability Model Framework (CCMF) for data-intensive science has been developed as a self-assessment tool for disciplinary researchers. The CCMF tool has eight dimensions located within three broad areas: environmental, human and technical, and has been tested in diverse domains including agronomy and social sciences (Lyon, et al., 2011).

Both tools take a unique perspective to systematically document the key process areas and activities and tie them with the level of capability maturity in RDM. The CMM for RDM framework and CCMF will provide guidelines much needed in data policy making, personnel training, and performance assessment.

This workshop will introduce these methodologies (CMM for RDM document (http://rdm.ischool.syr.edu/xwiki/bin/view/Main/ and the Community Capability Model CCMF https://communitymodel.sharepoint.com/Pages/default.aspx) as the materials for workshop discussion, activities, and input from the participants. The Workshop will engage participants through a series of key research questions:

·       What are the benefits of RDM assessment?

·       What is the most useful approach to benchmarking RDM capability?

·       Which RDM practices are most suited to CMM/CCMF assessment?

·       What are the desirable RDM community norms?

·       How can RDM sustainability be achieved?

The organizers Kevin Crowston and Jian Qin organized a similar workshop a few years ago, which was well attended. Earlier this year Liz Lyon and Christoph Becker organized a Birds of a Feather session at the International Digital Curation Conference (IDCC) on the topic of Community Capability Model for data-intensive research. There was a strong interest in both CMM for RDM and CCMF from the audience. We anticipate that this workshop will raise awareness of research data management process assessment while informing the audience about the best practices and tools available for them to achieve optimal performance in initiating and undertaking RDM projects and delivering RDM services.


  • Jian Qin (Syraucse University)
  • Kevin Crowston (Syracuse University)
  • Liz Lyon (Pittsburgh University)

Length of workshop: half day

Place and time: ASIST Annual Meeting 2016, Copenhagen, Denmark, October 15-18, 2016. Time to be arranged. 


Agenda (draft)

8:30-8:45 Introduction. Goals of the workshop

8:45-9:15 Overview of CMM for RDM and CCMF

9:15-10:00 RDM key practices with examples from the Research Lifecycle

10:00-10:30 CMM/CCMF for RDM rubrics: Individual examples of good/bad RDM practice to illustrate candidate workflows

10:30-10:45 Coffee break

10:45-11:30 Small group discussion to organize RDM practices based on CMM and CCMF (Use sticky notes and poster paper)

11:30-12:00 Open review of group findings

12:00-12:30 Open discussion on RDM assessment and call to action


Crowston, K. & J. Qin. (2011). A capability maturity model for scientific data management: Evidence from the literature. In: Proceedings of the American Society for Information Science and Technology, 48: 1–9. doi: 10.1002/meet.2011.14504801036

Lyon, L., Ball, A., Duke, M., & Day, M. (2011). Community Capability Model Framework. https://communitymodel.sharepoint.com/Documents/CCMDIRWhitePaper-24042012.pdf

Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. (1993). Capability maturity model, Version 1.1. IEEE Software, 10(4): 18–27.

Created by Jian Qin on 2016/07/11 09:40

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