A Capability Maturity Model for Research Data Management
CMM for RDM » 0. Introduction » 0.3 Research Data Management Maturity Levels
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1 -Perhaps the most well-known aspect of the CMM is the maturity levels, which describe the level of development of the practices in a particular organization. RDM practices as carried out in scientific projects similarly range from //ad hoc// to well-planned and well-managed processes ([[D’Ignazio & Qin, 2008>>||anchor="Dignazio"]]; [[Steinhart et al., 2008>>||anchor="Steinhart"]]). The generic practices described above provide a basis for mapping these maturity levels into the context of RDM, as illustrated in Figure 1 and described below.
1 +Perhaps the most well-known aspect of the CMM is five levels of //process or capability maturity//, which describe the level of development of the practices in a particular organization(% style="font-size: 14px;" %), representing the “degree of process improvement across a predefined set of process areas” and corresponding to the generic goals listed in the previous section. The initial level describes an organization with no defined processes: software is developed (i.e., the specific software related goals are achieved), but in an //ad hoc// and unrepeatable way, making it impossible to plan or predict the results of the next development project. As the organization increases in maturity, processes become more refined, institutionalized and standardized, achieving the higher numbered generic processes. The CMM thus described an evolutionary improvement path from //ad hoc//, immature processes to disciplined, mature processes with improved software quality and organizational effectiveness ([[CMMI Product Team, 2006, p. 535>>url:http://rdm.ischool.syr.edu/xwiki/resources/js/xwiki/wysiwyg/xwe/866E762AC0011916A1A6FBA06D489D8B.cache.html#CMMIProductTeam||style="font-size: 14px;"]]).
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3 +(% style="font-size: 14px;" %)Our goal in this document is to lay out a similar path for the improvement of research data management. RDM practices as carried out in scientific projects similarly range from //ad hoc// to well-planned and well-managed processes ([[(%%)D’Ignazio & Qin, 2008>>||anchor="Dignazio" style="font-size: 14px;"]](% style="font-size: 14px;" %); [[(%%)Steinhart et al., 2008>>||anchor="Steinhart" style="font-size: 14px;"]](% style="font-size: 14px;" %)). The generic practices described above provide a basis for mapping these maturity levels into the context of RDM, as illustrated in Figure 1 and described below.
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3 3 [[image:Research Data Management Maturity Levels@maturity-level.png||height="250" width="349"]]
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5 5 Figure 1. Capability maturity levels for research data management
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6 6
7 7 == Level 1: Initial ==
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9 -The initial level of the CMM describes an organization with no defined or stable processes. Paulk //et al//. describe this level thusly: “In an immature organization,… processes are generally improvised by practitioners and their managers during a project” ([[1993, p. 19>>||anchor="Paulk"]]). At this level, RDM is needs-based, //ad hoc// in nature and tends to be done intuitively. Rather than documented processes, the effectiveness of RDM relies on competent people and heroic efforts. The knowledge of the field and skills of the individuals involved (often graduate students working with little input) limits the effectiveness of data management. When those individuals move on or focus elsewhere, there is a danger that RDM will not be sustained; these changes in personnel will have a great impact on the outcomes (e.g., the data collection process will change depending on the person doing it).
11 +The initial level of the CMM describes an organization with no defined or stable processes. Paulk //et al//. describe this level thusly: “In an immature organization,… processes are generally improvised by practitioners and their managers during a project” ([[1993, p. 19>>||anchor="Paulk"]]). At this level, RDM is needs-based, //ad hoc// in nature and tends to be done intuitively. Rather than documented processes, the effectiveness of RDM relies on competent people and heroic efforts. The knowledge of the field and skills of the individuals involved (often graduate students working with little input) limits the effectiveness of data management. When those individuals move on or focus elsewhere, there is a danger that RDM will not be sustained; these changes in personnel will have a great impact on the outcomes (e.g., the data collection process will change depending on the person doing it), rendering the data management process unreliable.
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11 11 == Level 2: Managed ==
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