A Capability Maturity Model for Research Data Management
CMM for RDM » 0. Introduction » 0.1 Research Lifecycle and Data Management Lifecycle
Last modified by Arden Kirkland on 2014/06/30 09:26
From version 8.3
edited by Arden Kirkland
on 2014/03/11 15:40
To version 9.1
edited by Arden Kirkland
on 2014/03/12 18:10
Change comment: changed "science" to "research" DM

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1 -(% style="font-size: 14px;" %)Lifecycle is a term frequently used in our technology-driven society. Examples include information systems lifecycle, information transfer lifecycle, and many other variations depending on for which domain the term lifecycle is used. In the science data management domain, this term is used in several contexts: research lifecycle, data lifecycle, data curation lifecycle, and data management lifecycle. Each version has a different emphasis but they are often related or overlap in one way or the other. A research lifecycle generally includes study concept and design, data collection, data processing, data access and dissemination, and analysis [3]. As a research project progresses along the stages, different data will be collected, processed, calibrated, transformed, segmented or merged. Data at these stages go through one state to the next after certain processing or condition is performed on them. Some of these data are in the active state and may be changed frequently while others such as raw data and analysis-ready datasets will be tagged with metadata for discovery and reuse. At each stage of this lifecycle, the context and type of research (Figure 1) can directly affect the types of data generated and requirements for how the data will be processed, stored, managed, and preserved.
1 +(% style="font-size: 14px;" %)Lifecycle is a term frequently used in our technology-driven society. Examples include information systems lifecycle, information transfer lifecycle, and many other variations depending on for which domain the term lifecycle is used. In the research data management domain, this term is used in several contexts: research lifecycle, data lifecycle, data curation lifecycle, and data management lifecycle. Each version has a different emphasis but they are often related or overlap in one way or the other. A research lifecycle generally includes study concept and design, data collection, data processing, data access and dissemination, and analysis [3]. As a research project progresses along the stages, different data will be collected, processed, calibrated, transformed, segmented or merged. Data at these stages go through one state to the next after certain processing or condition is performed on them. Some of these data are in the active state and may be changed frequently while others such as raw data and analysis-ready datasets will be tagged with metadata for discovery and reuse. At each stage of this lifecycle, the context and type of research (Figure 1) can directly affect the types of data generated and requirements for how the data will be processed, stored, managed, and preserved.
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3 3 [[image:figure1_research_types.png||style="display: block; margin-left: auto; margin-right: auto;"]]
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13 13 Regardless of the context and nature of research, research data need to be stored, organized, documented, preserved (or discarded), and made discoverable and (re)usable. The amount of work and time involved in these processes is daunting, both intellectually intensive and costly. The personnel performing these tasks must be highly trained in technology and subject fields and able to effectively communicate between different stakeholders. In this sense, the lifecycle of research and data management is not only a technical domain but also a domain requiring effective management and communication. To be able to manage research data at community, institution, and project levels without reinventing-the-wheel, it is critical to build technical, communication, personnel, and policy capabilities at project and institutional levels and gradually evolve the maturity levels.
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16 16 [[<~~-~~-Previous Page>>doc:Introduction]] / [[Next Page ~~-~~->>>doc:Background of the Capability Maturity Model]]

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