A Capability Maturity Model for Research Data Management
CMM for RDM » 0. Introduction » 0.1 Research Lifecycle and Data Management Lifecycle
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1 1 = 0.1 Research Lifecycle and Data Management Lifecycle =
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3 -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. 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.
3 +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 the domain for which 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. 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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5 5 [[image:figure1_research_types.png||style="display: block; margin-left: auto; margin-right: auto;"]]
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13 -For example, in the United States, national research centers such as [[National Center for Atmospheric Research>>url:http://ncar.ucar.edu/||rel="__blank"]] (NCAR, [[http:~~/~~/ncar.ucar.edu/>>url:http://ncar.ucar.edu/||rel="__blank"]]) and [[National Oceanic and Atmospheric Administration>>url:http://www.noaa.gov/||rel="__blank"]] (NOAA, [[http:~~/~~/www.noaa.gov/>>url:http://www.noaa.gov/||rel="__blank"]]) regularly collect data about the global ecosystems and process them into data products for scientific research and learning. The research lifecycle and data management lifecycle at this level will be different from those at the individual project level where teams of scientists have specific goals to solve specific problems. The scale of data and kinds of requirements for data management will vary along the stages of the whole research lifecycle. National research centers are publicly funded agencies and have the obligation of preserving and providing access to ecosystems data they collected. Hence generating data products and providing ways to discover and obtain data is crucial for them. Another example is the type of research projects carried out at academic institutions. These research projects may be funded by federal funding agencies or private foundations and can be collaborative among institutions or within a department/college of an institution. The data collected and generated from these projects are specialized and subject to the control and regulation of different data policies and compliance, which creates a different set of issues and requirements for data management and use/reuse from those generated by the national research centers.
13 +For example, in the United States, national research centers such as the [[National Center for Atmospheric Research>>url:http://ncar.ucar.edu/||rel="__blank"]] (NCAR, [[http:~~/~~/ncar.ucar.edu/>>url:http://ncar.ucar.edu/||rel="__blank"]]) and the [[National Oceanic and Atmospheric Administration>>url:http://www.noaa.gov/||rel="__blank"]] (NOAA, [[http:~~/~~/www.noaa.gov/>>url:http://www.noaa.gov/||rel="__blank"]]) regularly collect data about the global ecosystems and process them into data products for scientific research and learning. The research lifecycle and data management lifecycle at this level will be different from those at the individual project level where teams of scientists have specific goals to solve specific problems. The scale of data and kinds of requirements for data management will vary along the stages of the whole research lifecycle. National research centers are publicly funded agencies and have the obligation of preserving and providing access to ecosystems data they collected. Hence generating data products and providing ways to discover and obtain data is crucial for them. Another example is the type of research projects carried out at academic institutions. These research projects may be funded by federal funding agencies or private foundations and can be collaborative among institutions or within a department/college of an institution. The data collected and generated from these projects are specialized and subject to the control and regulation of different data policies and compliance, which creates a different set of issues and requirements for data management and use/reuse from those generated by the national research centers.
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15 -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.
15 +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 both in technology and in 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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