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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43 43 The purpose of training programs is two-fold: for researchers, the training program is to develop the skills and knowledge of individuals so that they can adopt the best practices in managing their data; and for data managers, the training program will build the institutional capability by having capable personnel to perform infrastructural and technical services for data management.
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45 -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.
45 +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.
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47 47 === 1.2.5 Develop RDM tools ===
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49 -*adopt mechanisms to systematically capture the integration process (Source #32)
50 -*software tools have been developed (Source #70)
51 -In Mayernik et al.’s (2011) study of science cyberinfrastructure projects with ad hoc, irregular, and permanent types of metadata institutionalization, they found that in the project with the most developed infrastructure “Tools to support metadata work are developed by technical experts, who must then provide support for researchers and information managers” (422).
49 +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). 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). Developing RDM tools in a sense is also a process of developing and establishing best practices in RDM.
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51 +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.
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53 +More often than not software tools for RDM have been developed ((% style="font-size: 14px; line-height: normal;" %)Source #70). Adoption of such tools means adopting the(% style="font-size: 14px;" %) mechanisms to systematically capture the integration process (Source #32). 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).
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53 53 === 1.2.6 Establish a data management plan ===
54 54
55 -Planning for data management increases project efficiency and optimizes the reliability of the data that are collected by minimizing errors.
56 -• Careful planning for data management before you begin your research and throughout the data's life cycle is essential (Source #50)
57 +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 (Source #50) because it can increase project efficiency and optimize the reliability of the data that are collected by minimizing errors.
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59 +DMPs come in two types. The first type is prepared as part of a grant proposal because of the mandate from funding agencies such as the (% style="font-size: 14px; line-height: normal;" %)U.S.(% style="font-size: 14px;" %)National Science Foundation (NSF) and the Institute for Museum and Library Services (IMLS). Examples of this type of DMPs can be found from funding agencies' website as well as many research universities' websites, e.g., the Research Cyberinfrastructure (RCI) at UC San Diego provides a list of DMP samples for major NSF disciplinaries ([[http:~~/~~/rci.ucsd.edu/dmp/examples.html>>url:http://rci.ucsd.edu/dmp/examples.html]]).
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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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59 59 References for this section:
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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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62 62 Identify staffing needs. Roles and responsibilities should be clearly defined (DataOne)
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70 +Qin, J., D’Ignazio, J., & Baldwin, S. (2011). A workflow-based knowledge management architecture for geodynamics data. A White paper submitted to NSF GEO/OCI EarchCube Charrette meeting: [[http:~~/~~/jqin.mysite.syr.edu/pubs/ EarthCube_white_paper_workflow-based_KM_architecture.pdf>>url:http://jqin.mysite.syr.edu/pubs/%20EarthCube_white_paper_workflow-based_KM_architecture.pdf%20]]. Also available from:[[http:~~/~~/earthcube.ning.com/page/whitepapers>>url:http://earthcube.ning.com/page/whitepapers]]
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64 64 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>>http://www.data-archive.ac.uk/media/2894/managingsharing.pdf]]
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66 66 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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