A Capability Maturity Model for Research Data Management
CMM for RDM » 1. Data Management in General » 1.1 Commitment to Perform

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23 23 Developing user requirements for scientific data management must consider a wide array of factors because differences in disciplinary or research fields and types of research significantly affect the workflows, data flows, and data management and use practices. These differences in turn will affect the user requirements for data management services and tools and result in idiosyncrasies of the systems and services supporting the data management tasks. For example, the requirements for storing and describing real-time stream of data are different than for survey data. In a collaborative data management situation, user requirements must take into consideration the technical standards for data formats, sampling protocols, variable names, data discovery interfaces, among other things (Hale et al., 2003).
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25 -User requirements for scientific data management may be identified through analyzing data flows, workflows, leading data management problems, and researchers’ data practices. These requirements can be represented at a high level in use cases, user scenarios or personas (Cornell University Library, 2007; Lage, Losoff, & Maness, 2011). A key point in this process is that user requirements mean not only clear-cut project objectives but also goals for the data management services to serve a longer term and wider scope of scientific data management.
25 +User requirements for scientific data management may be identified through analyzing data flows, workflows, leading data management problems, and researchers’ data practices. These requirements can be represented at a high level in use cases, user scenarios or personas [[(Cornell University Library, 2007>>CMM for RDM.Bibliography||anchor="Cornell"]]; [[Lage, Losoff, & Maness, 2011>>CMM for RDM.Bibliography||anchor="Lage"]]). A key point in this process is that user requirements mean not only clear-cut project objectives but also goals for the data management services to serve a longer term and wider scope of scientific data management.
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27 27 === 1.1.3 Establish quantitative objectives for data management ===
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