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 6.1
edited by Jian Qin
on 2013/09/09 06:37
To version 7.1
edited by Jian Qin
on 2013/09/21 23:55
Change comment: There is no comment for this version

Content changes

... ... @@ -19,40 +19,29 @@
19 19
20 20 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) 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).
21 21
22 -=== 1.2.2 Staff data management activities ===
22 +=== 1.2.2 Staffing for data management ===
23 23
24 +(% 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).
24 24
25 -
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).
27 -
28 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).
29 29
30 30
29 +(% style="font-size: 20px; line-height: 1.2em; color: rgb(72, 92, 90);" %)1.2.3 Develop collaboration and partnership
31 31
32 -Identify staffing needs
33 -Roles and responsibilities should be clearly defined (DataOne)
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). collaboration and partnership are often a process of community building that, if managed properly, can contribute to sustaining a community of RDM practice.
34 34
35 -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
33 +(% 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.
36 36
35 +=== 1.2.4 Train researchers and data management personnel ===
37 37
37 +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:\\
38 38
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/]].
39 +* (% style="font-size: 14px;" %)Providing online guidance and workshops for data management
40 +* (% style="font-size: 14px;" %)Training in data documentation best practices
41 +* (% style="font-size: 14px;" %)Training in the unique tools and methods used in a research field
40 40
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.
41 41
42 -
43 -=== 1.2.3 Develop collaboration and partnership ===
44 -
45 -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).
46 -Collaboration and partnership can be managed by creating agendas and schedules for collaborative activities, documenting issues, and developing recommendations for resolving relevant stakeholder issues.
47 -
48 -=== 1.2.4 Train researchers and data management personnel ===
49 -
50 -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.
51 -Examples of training program:
52 -*Providing online guidance and workshops for data management
53 -*Training in data documentation best practices
54 -*Training in the unique tools and methods used in a research field
55 -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.
56 56 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.
57 57
58 58 === 1.2.5 Develop RDM tools ===
... ... @@ -65,3 +65,13 @@
65 65
66 66 Planning for data management increases project efficiency and optimizes the reliability of the data that are collected by minimizing errors.
67 67 • Careful planning for data management before you begin your research and throughout the data's life cycle is essential (Source #50)
57 +
58 +
59 +References for this section:
60 +
61 +
62 +Identify staffing needs. Roles and responsibilities should be clearly defined (DataOne)
63 +
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]]
65 +
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/]].

XWiki Enterprise 5.1-milestone-1 - Documentation