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 48.2
edited by Arden Kirkland
on 2014/02/03 17:03
To version 49.1
edited by Arden Kirkland
on 2014/03/11 14:36
Change comment: changed levels of headers

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2 2 {{toc/}}
3 3 {{/box}}
4 4
5 += 1.2 Ability to Perform =
6 +
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5 5 **//Ability to Perform//**// describes the preconditions that must exist in the project or organization to implement data management competently. Ability to Perform typically involves resources, organizational structures, and training.//
6 6
7 -= 1.2.1 Develop and implement a budget =
10 +== 1.2.1 Develop and implement a budget ==
8 8
9 9 Effective data management incurs costs ([[Hale et al, 2003>>||anchor="Hale"]]). Budgeting for data management helps ensure allotment of sufficient financial resources to support data management activities.
10 10 Budget considerations vary with the type, scope, scale, and timeframe of the data management context. Those who collect data need adequate financial resources to manage local data during the life cycle of the project ([[DataOne, 2011a>>||anchor="DataONE-a"]]; [[Hale et al., 2003>>||anchor="Hale"]]). Local data management costs might include data management personnel, database systems, servers, networks, and security for project data that is shared over a network ([[Hale et al., 2003>>||anchor="Hale"]]).
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18 18 Budgeting should include not only allotment of hardware and software, but also near- and long-term RDM service payments and staff with the appropriate technical expertise. In their ethnographic study of data and work practices across three science cyberinfrastructure projects in the environmental sciences Mayernik et al. ([[2011>>||anchor="Mayernik"]]) found that “human support is valuable in the development of data management plans, but is only available in institutions that specifically provide funding for it” (p. 421).
19 19
20 -= 1.2.2 Staffing for data management =
23 +== 1.2.2 Staffing for data management ==
21 21
22 22 (% style="font-size: 14px;" %)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, 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"]]).
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24 24 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"]]).
25 25
26 -= 1.2.3 Develop collaborations and partnerships =
29 +== 1.2.3 Develop collaborations and partnerships ==
27 27
28 28 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.
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30 30 (% style="font-size: 14px;" %)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.
31 31
32 -= 1.2.4 Train researchers and data management personnel =
35 +== 1.2.4 Train researchers and data management personnel ==
33 33
34 34 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:\\
35 35
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42 42 Planning for training typically involves identification of training needs, training topics, requirements and quality standards for training materials, training tasks, roles, and responsibilities, and required resources. Schedules for training activities and their dependencies also need to be laid out in the training program. Training programs may also be offered by conference workshops, professional development events, or educational programs outside of one's institution. These venues are useful for training the trainers who will provide internal training programs and services.
43 43
44 -= 1.2.5 Develop RDM tools =
47 +== 1.2.5 Develop RDM tools ==
45 45
46 46 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 50 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"]]).
51 51
52 -= 1.2.6 Establish a data management plan =
55 +== 1.2.6 Establish a data management plan ==
53 53
54 54 A data management plan (DMP) documents the definitions, procedures, methods, and best practices for a project or organization to maintain a consistent practice of RDM. Careful planning for data management before you begin your research and throughout the data's life cycle is essential ([[DataONE, 2011c>>||anchor="DataONE-c"]]) because it can increase project efficiency and optimize the reliability of the data that are collected by minimizing errors.
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60 60 1. (% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.59375px; text-align: start;" %)Disciplinary-based NSF DMP templates: [[http:~~/~~/dmconsult.library.virginia.edu/dmp-templates/>>url:http://dmconsult.library.virginia.edu/dmp-templates/||rel="__blank" style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.59375px; text-align: start;"]]
61 61 1. (% style="font-family: sans-serif; font-size: 14px; font-style: normal; line-height: 19.59375px; text-align: start;" %)DMP Tool hosted at California Digital Library: [[https:~~/~~/dmp.cdlib.org/>>url:https://dmp.cdlib.org/||rel="__blank"]]
62 62
63 -= References =
66 +== References ==
64 64
65 65 {{id name="Brown"/}}
66 66 Brown, D.A, Brady, P.R., Dietz, A., Cao, J., Johnson, B., & McNabb, J. (2006). A case study 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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