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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13 13 Explicit identification of stakeholders is important because scientific data management processes are increasingly complex and so involve entities with different roles, specializing in different aspects of data management. For example, data managers are responsible for data storage, management, backup, and access. Research team members need to document data collection and processing methods and parameter, validate and verify data quality, and maintain information on workflows and data flows for provenance and quality control purpose. Technology staff needs to assure that the infrastructure services are in good order to support the data management activities. However, organizations may not have all of these stakeholders and responsibilities can be differently distributed.
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15 -Furthermore, the tasks and interests in data management of these different groups may or may not cross with one another. For example, Mullins ([[2007>>||anchor="Mullins"]]) reported that, after extensive interviews with scientist in biology, earth and atmospheric science, astronomy, chemistry, chemical engineering, plant science, ecological sciences, it became clear that no single method or process would suffice the needs for data management cross all disciplines. Their extensive conversations with stakeholders led them to identify the need to foster collaboration between domain scientists as well as librarians/archivists, computer scientists, infrastructure technologists. In addition to project level stakeholders, three types of data sharing intermediaries may have a role in supporting data management at various stages of the research data life cycle: data archives (all stages), institutional repositories (end of research life cycle), and virtual organizations (analyzing and visualizing data ([[Faniel et al., 2011>>||anchor="Faniel"]]).
15 +Furthermore, the tasks and interests in data management of these different groups may or may not cross with one another. For example, Mullins ([[2007>>||anchor="Mullins"]]) reported that, after extensive interviews with scientist in biology, earth and atmospheric science, astronomy, chemistry, chemical engineering, plant science, ecological sciences, it became clear that no single method or process would suffice the needs for data management cross all disciplines. Their extensive conversations with stakeholders led them to identify the need to foster collaboration between domain scientists as well as librarians/archivists, computer scientists, infrastructure technologists. In addition to project level stakeholders, three types of data sharing intermediaries may have a role in supporting data management at various stages of the research data life cycle: data archives (all stages), institutional repositories (end of research life cycle), and virtual organizations.
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17 -As a result, explicit identification of stakeholders is necessary to ensure that the design of the processes meets their different needs and to ensure implementation efficiency and usefulness of data management. As in Mullins ([[2007>>||anchor="Mullins"]]) identification of stakeholders may start with discussion with key informants, such as researchers or sponsored program office staff and then use snowball sampling to identify additional stakeholders. The results of these efforts may be confirmed by a follow up survey.
17 +As a result, explicit identification of stakeholders is necessary to ensure that the design of the processes meets their different needs and to ensure implementation efficiency and usefulness of data management. As in Mullins ([[2007>>||anchor="Mullins"]]) identification of stakeholders may start with discussion with key informants, such as researchers or sponsored program office staff and then use snowball sampling to identify additional stakeholders. The results of these efforts may be confirmed by a follow-up survey.
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19 19 == 1.1.2 Develop user requirements ==
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46 46 == References ==
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49 49 {{id name="Cornell"/}}
50 50 Cornell University Library. (2007). Cornell University Library personas. Retrieved from [[http:~~/~~/hdl.handle.net/1813/8302>>url:http://hdl.handle.net/1813/8302||rel="__blank"]]
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52 52 {{id name="DataONE"/}}
53 53 DataONE. (n.d.). Best Practices. Retrieved from [[https:~~/~~/www.dataone.org/best-practices>>url:https://www.dataone.org/best-practices||rel="__blank"]]
54 54
55 -{{id name="Faniel"/}}
56 -__Faniel __et al, 2011
57 -
58 58 {{id name="Hale"/}}
59 59 Hale, S. S., Miglarese, A. H., Bradley, M. P., Belton, T. J., Cooper, L. D., Frame, M. T., … Peterjohn, B. G. (2003). Managing Troubled Data: Coastal Data Partnerships Smooth Data Integration. //Environmental Monitoring and Assessment//, //81//(1-3), 133–148. doi:10.1023/A:1021372923589. Retrieved from [[http:~~/~~/link.springer.com/article/10.1023%2FA%3A1021372923589>>url:http://link.springer.com/article/10.1023%2FA%3A1021372923589||rel="__blank"]]
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62 62 Lage, K., Losoff, B., & Maness, J. (2011). Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder. Portal: Libraries and the Academy, 11(4): 915-937. doi:10.1353/pla.2011.0049. Retrieved from [[http:~~/~~/www.press.jhu.edu/journals/portal_libraries_and_the_academy/portal_pre_print/current/articles/11.4lage.pdf>>url:http://www.press.jhu.edu/journals/portal_libraries_and_the_academy/portal_pre_print/current/articles/11.4lage.pdf||rel="__blank"]]
63 63
64 64 {{id name="Mullins"/}}
65 -__Mullins__ (2007).
61 +Mullins, James. (2007). Enabling international access to scientific data sets: Creation of the Distributed Data Curation Center (D2C2). Purdue University, Purdue E-Pubs. Retrieved from http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1100&context=lib_research
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67 67 (% style="text-align:justify" %)[[<~~-~~-Previous Page>>doc:1\. Data Management in General]] [[Next Page ~~-~~->>>doc:1\.2 Ability to Perform]]

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