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
From version 51.1
edited by Arden Kirkland
on 2014/05/09 18:45
To version 52.1
edited by Arden Kirkland
on 2014/05/10 11:09
Change comment: generalizing science to research, minor proofreading

Content changes

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14 14 Another type of data management cost is synthesis and integration of data, and collaboration necessary to support this synthesis ([[Hale et al., 2003>>||anchor="Hale"]]). The creation of metadata using a standardized metadata format is a cost for data that is publically shared beyond the scope of a research project.
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16 -Organizations with missions aimed at disseminating and preserving data budget for data management beyond the timeframe of specific research projects. When data centers are underfunded their focus becomes managing their own data rather than addressing the broader needs of those they serve.
16 +Organizations with missions aimed at disseminating and preserving data budget for data management beyond the timeframe of specific research projects. When data centers are underfunded, their focus becomes managing their own data rather than addressing the broader needs of those they serve.
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18 18 As new data management models emerge, the budget for data management also needs to take the memberships or subscriptions of data repository services into consideration. This has become a trend that, on the one hand, disciplinary data repositories are seeking self-sustainable solutions through devising economic models that will charge institutions for services ([[Sheaffer, 2012>>||anchor="Sheaffer"]]). On the other hand, institutions that are initiating or have established data management services will need funding to start up the RDM services and keep them in operation once they become part of the regular tasks.
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22 22 == 1.2.2 Staffing for data management ==
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24 -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 at [[http:~~/~~/www.data-pass.org>>url:http://www.data-pass.org||rel="__blank"]]) is a broad-based partnership of data archives for acquiring, cataloging, 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>>||anchor="Walters"]]).
24 +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 at [[http:~~/~~/www.data-pass.org>>url:http://www.data-pass.org||rel="__blank"]]) is a broad-based partnership of data archives for acquiring, cataloging, and preserving social sciences data. The partnership involves existing data repositories, academic institutions, and government agencies. As such the communication 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>>||anchor="Walters"]]).
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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 skills required to fulfill their assigned roles” ([[Paulk et al., 1993, p. 12>>||anchor="Paulk"]]).
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28 28 == 1.2.3 Develop collaborations and partnerships ==
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30 -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>>||anchor="Hale"]]). collaboration and partnership are often a process of community building that, if managed properly, can contribute to sustaining a community of RDM practice.
30 +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, and reduce many technical barriers by agreeing on core data standards and the flow of data ([[Hale et al., 2003>>||anchor="Hale"]]). 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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32 32 Collaboration and partnership can be managed by creating agendas and schedules for collaborative activities, documenting issues, and developing recommendations for resolving relevant stakeholder issues. In addition, activities in collaboration and partnership may also include problem solving, information and experience sharing, resource/assets reuse, coordination, visits, and creation of documentations. Over time a community of RDM practice can be built, which in turn will strengthen the collaboration and partnership.
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34 34 == 1.2.4 Train researchers and data management personnel ==
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36 -A key indicator for mature data management processes is that training programs are provided so researchers and staff understand well data management processes and have the capability to perform data management activities. Examples of training program include:\\
36 +A key indicator for mature data management processes is that training programs are provided so researchers and staff understand data management processes well and have the capability to perform data management activities. Examples of training programs include:\\
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38 38 * Providing online guidance and workshops for data management
39 39 * Training in data documentation best practices
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48 48 Research data management tools are software programs that help researchers effectively manage data during a research lifecycle. The nature of research types determines the requirements for such tools. Computational intensive research fields such as astrophysics use workflow management systems to capture metadata for provenance and output management, which is a highly automated process ([[Brown et al., 2006>>||anchor="Brown"]]). Geodynamics data, on the contrast, often reside in spreadsheet files and sometimes are mixed with researchers' annotation text. It will be difficult to manage this type of data with completely automatic tools due to the inconsistent data recording practice ([[Qin, D'Ignazio, & Baldwin, 2011>>||anchor="Qin"]]). Developing RDM tools in a sense is also a process of developing and establishing best practices in RDM.
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50 -Tools for RDM include off-the-shelf kind, such as data repository management systems and metadata editors created for specific standards, and the those developed in-house. Before deciding whether to adopt an off-the-shelf tool or develop one in-house, a comprehensive analysis should be conducted to understand not only the local requirements but also the need for links to community data management infrastructure and standards. This means that tools adopted or developed should consider key functions for immediate data management needs such as storage, annotation, organization, and discovery and at the same time the "staging" functions for effective data deposition and dissemination in community, national, and international data repositories.
50 +Tools for RDM include off-the-shelf applications, such as data repository management systems and metadata editors created for specific standards, along with those developed in-house. Before deciding whether to adopt an off-the-shelf tool or develop one in-house, a comprehensive analysis should be conducted to understand not only the local requirements but also the need for links to community data management infrastructure and standards. This means that tools adopted or developed should consider key functions for immediate data management needs such as storage, annotation, organization, and discovery, and at the same time the "staging" functions for effective data deposition and dissemination in community, national, and international data repositories.
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52 52 More often than not software tools for RDM have been developed ([[Michener, 2006>>||anchor="Michener"]]). Adoption of such tools means adopting the mechanisms to systematically capture the integration process ([[DataONE, 2011b>>||anchor="DataONE-b"]]). RDM projects vary in scope and nature as the data they deal with change from discipline to discipline and from project to project. Whether tools are adopted or developed for ad hoc or long-term needs, support for researchers to use these tools should be an integral part of the tool adoption/development process ([[Mayernik et al., 2011>>||anchor="Mayernik"]]).
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