Development and Dissemination of A Capability Maturity Model for Research Data Management Training and Performance Assessment

Type: Report
Author: Jian Qin, Kevin Crowston
Year of Publishing: 2014

The goal of this project was to develop a capability maturity model (CMM) for research data management (RDM). The project was started because the field of research data management lacked a conceptual model upon which practices, policies and performance and impact assessment can be based. Having an overall framework is important to make sense of the breadth and diversity of practices. RDM practices vary greatly depending on the scale, discipline, funding and type of projects. “Big science” research—such as astrophysics, geosciences, climate science, and system biology—generally has established well-defined RDM policies and practices, with supporting data repositories for data curation, discovery and reuse. RDM in these disciplines often has significant funding support for the necessary personnel and technology infrastructure. By contrast, in “small science” research, that is, projects involving a single PI and a few students, RDM is typically less well developed. However, even in these fields, such practices are still needed: the data generated by these projects may be small on an individual level, but they can nevertheless add up to a large volume collectively (Carlson, 2006) and in aggregation can have more complexity and heterogeneity than those generated from big science projects. Research projects need more concrete guidance to analyze and assess the processes of RDM and to set priorities for process improvement. The CMM for RDM addresses this need by providing a framework to support RDM training, performance assessment and improvement of practices to increase the reliability of RDM.