A Capability Maturity Model for Research Data Management
CMM for RDM » 1. Data Management in General » 1.2 Ability to Perform

Changes for document 1.2 Ability to Perform

Last modified by Arden Kirkland on 2014/05/18 11:56
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26 26 Staffing needs should be reviewed carefully and each role/position’s responsibilities specified clearly. This is not only important for hiring the right personnel but also important for developing a suitable training program “to ensure that the staff and managers have the knowledge and required skills to fulfill assigned roles” (Paulk et al., 1993).
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29 -=== (% style="font-size: 20px; line-height: 1.2em; color: rgb(72, 92, 90);" %)1.2.3 Develop collaboration and partnership(%%) ===
29 +=== (% style="font-size: 20px; line-height: 1.2em; color: rgb(72, 92, 90);" %)1.2.3 Develop collaborations and partnerships(%%) ===
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31 31 Stakeholder involvement in data management processes often takes the form of collaboration and/or partnership. When resources can be effectively shared, partnerships can reduce hardware and software costs, lead to better data and data products, reduce many technical barriers by agreeing on core data standards and the flow of data (Hale et al., 2003). collaboration and partnership are often a process of community building that, if managed properly, can contribute to sustaining a community of RDM practice.
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61 61 Another type of DMPs is created when a new research project starts or when an institution takes a data management initiative. In the case that a project is funded by a grant from NSF or other funding agency, the DMP submitted with the proposal will need to be expanded with operational specifics for the project staff to follow and execute. The operational DMP for a new research project should specify essential management tasks that may have not included in the proposal DMP, including data storage structures, backup schedules, naming conventions for data files and folders, and procedures for data processing and transformation, in addition to the high-level descriptions in a proposal-stage DMP.
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63 +Resources for DMP development:
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65 +DMP tool at DataOne/U of Virginia Library
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64 64 References for this section:
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66 66 Brown, D.A, Brady, P.R., Dietz, A., Cao, J., Johnson, B., & McNabb, J. (2006). A casestudy on the use of workflow technologies for scientific analysis: Gravitationalwave data analysis, in I.J. Taylor, E. Deelman, D. Gannon, and M.S. Shields(Eds.), Workflows for e-Science, chapter 5, pp. 41–61. Berlin: Springer-Verlag.

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