A Capability Maturity Model for Research Data Management
CMM for RDM » 3. Data description and representation » 3.1 Commitment to Perform

3.1 Commitment to Perform

Last modified by Arden Kirkland on 2014/06/03 17:10

3.1 Commitment to Perform

Commitment to Perform describes the actions the organization must take to ensure that the process is established and will endure. Commitment to Perform typically involves establishing organizational policies and senior management sponsorship.

For data description and representation, the commitment to perform includes committing to documenting project activities to facilitate replication, generating standard-compliant metadata specifications and schemas, and using controlled vocabularies to facilitate discovery.

3.1.1 Develop metadata policies

Metadata policies support the creation of metadata that fits the data and conforms to the standards and best practices of the relevant research community (Riley, 2014). An example of a national level metadata policy is the National Science Foundation’s suggestion that data management plans include “standards to be used for data and metadata format and content.”

It is clear that not every stage of a research lifecycle, hence the data lifecycle as well, requires comprehensive metadata descriptions. Metadata policies should provide guidelines on when to create metadata descriptions and what types of metadata are mandated or optional. The content of these guidelines may vary widely depending on the scope of a research project and the nature of data. For example, at the project level, the metadata policy would focus more on workflows and procedures, while at the institutional level, the policies can become more general and function as guidelines for what should be done rather than how it should be done.

There are also differences between data documentation and metadata descriptions. Raw data files and intermediary data files, for example, may not have formal metadata descriptions but documentation should be provided for data creation/collection processes, errors or issues identified, etc. so that users can have sufficient information to decide whether the data is suitable for their research. Metadata is considered as a "subset of data documentation, which provide standardized, structured information explaining the purpose, origin, time references, geographic location, creating author, access conditions, and terms of use of a data collection" (Corti et al., 2014, p. 38). 

Most research data is not currently described with metadata that meets an authoritative standard. Tenopir et al. (2011) found that 78 percent of researchers either do not use metadata schema at all, or use an ad hoc, homegrown metadata format to describe their data. The limitation of not describing a study’s data using an authoritative standard is that opportunities for discovery and reuse are diminished.

Commitment to metadata can occur on the part of institutions that support research, and in a more grassroots way by researchers themselves. However, there is a relationship between institutional commitment to metadata and default researcher metadata practices (Mayernik et al., 2011). When there is a permanent or semi-permanent institutional commitment to metadata “researchers themselves may or may not have experience and expertise in creating and working with formal metadata, but will likely have experts… to provide help and support in making data available to wider audiences. This human support is valuable in the development of data plans, but is only available in institutions that specifically provide funding for it" (Mayernik et al., 2011, p.421).


Rubric for 3.1 - Commitment to Perform
Level 0
 This process or practice is not being observed
No steps have been taken to establish organizational policies or senior management sponsorship regarding metadata development
Level 1: Initial
 Data are managed intuitively at project level without clear goals and practices
Metadata development has been considered minimally by individual team members, but nothing has been quantified or included in organizational policies or senior management sponsorship
Level 2: Managed
 DM process is characterized for projects and often reactive
Metadata development policies have been recorded for this project, but have not taken wider community needs or standards into account and have not resulted in organizational policies or senior management sponsorship
Level 3: Defined
 DM is characterized for the organization/community and proactive
The project follows approaches to metadata development that have been defined for the entire community or institution, as codified in organizational policies with senior management sponsorship
Level 4: Quantitatively Managed
 DM is measured and controlled
Quantitative quality goals have been established regarding metadata development, and are codified in organizational policies with senior management sponsorship; data are systematically measured for quality
Level 5: Optimizing
 Focus on process improvement
Processes regarding metadata development are evaluated on a regular basis, as codified in organizational policies with senior management sponsorship, and necessary improvements are implemented


Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2014). Managing and Sharing Research Data: A Guide to Good Practice. Los Angeles, CA: SAGE. 

Mayernik, M. S., Batcheller, A. L., & Borgman, C. L. (2011). How Institutional Factors Influence the Creation of Scientific Metadata. Paper presented at the iConference '11, Seattle.

Riley, Jenn. (2014). Metadata services. In J. Ray (Ed.), Research Data Management: Practical Strategies for Information Professionals. West Lafayette, Indiana: Purdue University Press.

Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., Manoff, M., Frame, M. (2011). Data Sharing by Scientists: Practices and Perceptions. PLoS ONE, 6(6), e21101. doi:10.1371/journal.pone.0021101. Retrieved from http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021101

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