A Capability Maturity Model for Research Data Management
CMM for RDM » 1. Data Management in General » 1.3 Activities Performed

Changes for document 1.3 Activities Performed

Last modified by Arden Kirkland on 2014/06/06 12:52
From version 19.2
edited by Arden Kirkland
on 2014/02/03 17:03
To version 20.1
edited by Arden Kirkland
on 2014/03/11 14:38
Change comment: changed levels of headers

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1 += 1.3 Activities Performed =
1 1 {{box cssClass="floatinginfobox" title="**Contents**"}}
2 2 {{toc/}}
3 3 {{/box}}
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5 5 //**Activities Performed** describes the roles and procedures necessary to implement a key process area. Activities Performed typically involve establishing plans and procedures, performing the work, tracking it, and taking corrective actions as necessary. ([[Paulk et al., 1993, p. 38>>||anchor="Paulk"]])//
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7 7 In the general data management process area the activities performed involve turning the requirements, collaborations/partnerships, plans, and procedures into written documents that state shared consensus and understanding of the goals and actionable plans within an institution or a research group. Different kinds of activities performed will reflect different levels of capability maturity in research data management.
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9 -= 1.3.1 Manage RDM Requirements =
11 +== 1.3.1 Manage RDM Requirements ==
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11 11 Two aspects of RDM requirements are crucial for RDM. The user aspect of RDM requirements focuses on the functionalities that an RDM system or platform can offer for researchers to perform their data management tasks throughout the research lifecycle, so that they can save time while achieving the RDM goals. The technical aspect of RDM requirements refers to the technologies and organizational support that make these functionalities possible. RDM requirements may change over time as new projects, new data emerge. Documenting RDM requirements and keeping them updated will establish a common understanding between researchers and RDM processes. This agreement with researchers is the basis for planning and managing the RDM processes.
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19 19 * //**Validation and negotiation**//: documented requirements are validated against predefined criteria and negotiated with stakeholders.
20 20 * //**Management**//: validated requirements are properly structured and prepared so that they can be used by different roles, to maintain consistency after changes, and to ensure their implementation ([[Pohl & Rupp, 2011>>||anchor="Pohl"]]).
21 21
22 -= 1.3.2 Manage Collaborations and Partnerships =
24 +== 1.3.2 Manage Collaborations and Partnerships ==
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24 24 Collaborations and partnerships in RDM may take place at all organizational levels and among any number of community members. Large-scale collaborations and partnerships include examples such as DataONE ([[https:~~/~~/www.dataone.org/>>url:https://www.dataone.org/||rel="__blank"]]) and the Laser Interferometer Gravitational-Wave Observatory (LIGO, [[http:~~/~~/www.ligo.caltech.edu/>>url:http://www.ligo.caltech.edu/||rel="__blank"]]). There are also regional, disciplinary-based collaborations (e.g., the Hubbard Brook Ecosystem Study, [[http:~~/~~/hubbardbrook.org/>>url:http://hubbardbrook.org/||rel="__blank"]]) and many within-institutional-units collaborations for research data management (e.g., Cornell University's Research Data Management Service Group, [[https:~~/~~/confluence.cornell.edu/display/rdmsgweb/Home>>url:https://confluence.cornell.edu/display/rdmsgweb/Home||rel="__blank"]]). The goals of collaboration and partnership management are to keep the collaborators and partners aware of the shared purpose, gain consensus on problem solving, engage them in the process, and make sure sharing between the parties involved.
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26 26 Maintaining communication policies (described in [[1.1.4 Develop communication policies>>url:http://rdm.ischool.syr.edu/xwiki/bin/view/CMM+for+RDM/1.1+#H1.1.4Developcommunicationpolicies||rel="__blank"]]) is crucial in managing collaborations and partnerships. Regular meetings should be held and other communication methods used for awareness, sharing, motivating, and engaging purposes. Whether collaboration scale is large or small, decisions reached and notes taken during meetings or through asynchronous channels should be carefully documented and shared among collaborators and partners.
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28 -= 1.3.3 Create Actionable RDM Plans =
30 +== 1.3.3 Create Actionable RDM Plans ==
29 29
30 30 Data management plan as part of the activities performed refers to the one that is operational and 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-stage 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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32 -= 1.3.4 Develop Workflows and Procedures =
34 +== 1.3.4 Develop Workflows and Procedures ==
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34 34 A workflow is defined as a "set of tasks involved in a procedure along with their interdependencies and their inputs and outputs" ([[Ailamaki, Ioannidis, & Livny, 1998, p. 1>>||anchor="Ailamaki"]]). Data management workflows consist of tasks to be performed and procedures that ensures the consistent performance of the tasks. For example, the objective of file naming convention is to establish patterns of file names for (% style="font-size: 14px; line-height: normal;" %)searching and identifying data input (% style="font-size: 14px;" %)and managing data output. A workflow for data input and output will involve defining naming conventions, assigning names to output data, depositing them to appropriate file locations, and creating appropriate annotations. These tasks should follow standard procedures so that data output is managed with consistency, upon which scientific experiments or computational runs will depend to obtain the input data.
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39 39
40 -= References =
42 +== References ==
41 41
42 42 Ailamaki, A., Ioannidis, Y.E., & Livny, M. (1998). Scientific workflow management by database management. In: Proceedings of the Tenth International Conference on Scientific and Statistical Database Management, Capri, Italy, July 1-3, 1998. Retrieved from [[http:~~/~~/www.cs.cmu.edu/~~~~natassa/aapubs/conference/scientific-workflow-management.pdf>>url:http://www.cs.cmu.edu/~~natassa/aapubs/conference/scientific-workflow-management.pdf||rel="__blank" style="font-size: 14px;"]]
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