A capability maturity model for scientific data management: Evidence from the literature

Abstract

In this paper, we propose a capability maturity model (CMM) for scientific data management (SDM) practices, with the goal of supporting assessment and improvement of these practices. The model describes key process areas and practices necessary for effective SDM. Appropriate SDM practices were identified by content analysis of papers about SDM and include both those specific SDM practices and generic process management practices. The CMM further characterizes organizations by the level of maturity of these processes, meaning the organizational capability to reliably perform SDM processes. The model may be useful to organizations in evaluating and planning improvements to their SDM practices. KeywordsScientific data management, capability maturity model.

Developments in research data management in academic libraries: Towards an understanding of research data service maturity

“…Discussions about developing RDM services and capabilities have sometimes used the concept of “maturity.” Maturity in this context is tied in with the notion that as knowledge about, and services in, a particular area reach a full or complete level of development, they are “mature.” The concept has been explored in software engineering (Paulk, Curtis, Chrissis, & Weber, ), digital preservation (Kenney & McGovern, ), and data intensive research (Lyon, Ball, Duke, & Day, ). Maturity models have also been applied in the RDM space, within institutions (ANDS, ), and within research projects (Crowston & Qin, ). In these models different levels of maturity are proposed according to different levels of services or infrastructure offered.…”

Data Practices and Curation Vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes

“…The idealized scientific research activity lifecycle model (I2S2 Project, ) presents the scientist's perspective on the research and publication process more generally and includes an overarching view of administrative and archiving activities. Additional data practices are framed in the capability maturity model for scientific data management (Crowston & Qin, ), which is based on an analysis of literature on data management, data curation, and data science. Although these two models have different scopes (the research process vs. data management tasks), there is significant overlap in the terms used to describe data activities and practices.…”

Research Infrastructure for Collaborative Team Science: Challenges in Technology-Supported Workflows in and Across Laboratories, Institutions, and Geographies

“…Two-way bridges are needed between collaborating researchers and technologists and, by extension, relevant organizational services and the decision makers who set priorities and allocate resources. By and large, these bridges are underdeveloped [11]. This article has looked at one dimension of building bridges – developing an awareness of researchers’ workflows and corresponding challenges.…”