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

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22 22 === 1.2.2 Staff data management activities ===
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26 +Staffing for data management refers to identifying the levels and types of expertise needed for achieving immediate and/or near-term data management objectives. A data management lifecycle involves different tasks at different stages that demand a combination of varying levels and types of expertise and skills. For example, the Data Preservation Alliance for the Social Sciences (DATA-PASS) is a broad-based partnership of data archives for acquiring, cataloguing, and preserving social sciences data. The partnership involves existing data repositories, academic institutions, and government agencies. As such the communications among partners, technical system architecture, and policies are inherently complicated. Having a capable staff will be extremely important to meet the constantly shifting data curation activities (Walters & Skinner, 2011).
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28 +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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24 24 Identify staffing needs
25 25 Roles and responsibilities should be clearly defined (DataOne)
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27 27 Van den Eynden, V., Corti, L., Woollard, M. & Bishop, L. (2011). Managing and Sharing Data: A Best Practice Guide for Researchers. Last updated May 2011. http://www.data-archive.ac.uk/media/2894/managingsharing.pdf
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39 +Walters, T. & Skinner, K. (2011). New roles for new times: Digital curation for preservation. [[http:~~/~~/www.arl.org/rtl/plan/nrnt/>>url:http://www.arl.org/rtl/plan/nrnt/]].
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29 29 === 1.2.3 Develop collaboration and partnership ===
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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).

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