A Capability Maturity Model for Research Data Management

Changes for document 3.2 Ability to Perform

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22 22
23 23 == 3.2.2 Select and acquire tools ==
24 24
25 -Tools for producing metadata should be selected and evaluated for feasibility. Metadata standards often come with tools. Some standards have multiple tools. An example of a type of tool are workflow management systems astrophysicists use that automate capture of metadata. Automated tools typically cannot capture all of the necessary metadata. A best practice is to make use of tools currently in use in a research community for generating metadata (Riley, 2014).
25 +Tools for producing metadata should be selected and evaluated for feasibility. Metadata standards often come with tools. Some standards have multiple tools. An example of a type of tool are workflow management systems astrophysicists use that automate capture of metadata. Automated tools typically cannot capture all of the necessary metadata. A best practice is to make use of tools currently in use in a research community for generating metadata ([[Riley, 2014>>||anchor="Riley"]]).
26 26
27 27 == 3.2.3 Develop strategies for generating metadata based on community practices ==
28 28
29 -A best practice for generating metadata is to leverage existing documentation practices within a community of researchers (Riley, 2014).
29 +A best practice for generating metadata is to leverage existing documentation practices within a community of researchers ([[Riley, 2014>>||anchor="Riley"]]).
30 30
31 -One strategy for generating metadata to facilitate discovery and long-term preservation is to rely on researchers to perform this activity themselves. Thus far this approach has had limited success (Tenopir, 2011), and has inhibited the deposit of data in repositories with useful metadata (Riley, 2014). This is often a default approach for generating metadata due to limited resources.
31 +One strategy for generating metadata to facilitate discovery and long-term preservation is to rely on researchers to perform this activity themselves. Thus far this approach has had limited success ([[Tenopir, 2011>>||anchor="Tenopir"]]), and has inhibited the deposit of data in repositories with useful metadata ([[Riley, 2014>>||anchor="Riley"]]). This is often a default approach for generating metadata due to limited resources.
32 32 \\There are efforts to automate the generation of metadata via software tools, though this capability is not fully realized for most research communities.
33 -\\A best practice in many contexts is to conceptualize metadata creation as a shared responsibility, that is facilitated by librarian support (Riley, 2014). For example, the ICPSR data repository asks researchers to provide descriptive study information, but also devotes significant staff resources to enhancing researcher metadata to make it more fully interoperable with DDI (Data Documentation Initiative) metadata (a social science metadata standard), and transforming data into multiple data formats (for three common statistical software platforms) to make it widely accessible.
33 +\\A best practice in many contexts is to conceptualize metadata creation as a shared responsibility, that is facilitated by librarian support ([[Riley, 2014>>||anchor="Riley"]]). For example, the ICPSR data repository asks researchers to provide descriptive study information, but also devotes significant staff resources to enhancing researcher metadata to make it more fully interoperable with DDI (Data Documentation Initiative) metadata (a social science metadata standard), and transforming data into multiple data formats (for three common statistical software platforms) to make it widely accessible.
34 34
35 35 == 3.2.4 Integrate metadata creation into researcher workflow ==
36 36
37 -Researcher interest in documentation of data is greatest when it assists with everyday project data management (Jahnke & Asher, 2012). A best practice is to integrate metadata creation into researcher workflows during the active phase of research projects, leveraging researcher interest in project data management (Jahnke & Asher, 2012).
37 +Researcher interest in documentation of data is greatest when it assists with everyday project data management ([[Jahnke & Asher, 2012>>||anchor="Jahnke"]]). A best practice is to integrate metadata creation into researcher workflows during the active phase of research projects, leveraging researcher interest in project data management ([[Jahnke & Asher, 2012>>||anchor="Jahnke"]]).
38 38
39 39 == 3.2.5 Arrange staffing for creating metadata ==
40 40
41 -Roles in creating metadata vary with the scale and nature of the research context. Large, heavily funded projects often have internal infrastructure with dedicated data management personnel; smaller projects are more likely to benefit from support from data supports services offered by an academic library (Ray, 2014).
41 +Roles in creating metadata vary with the scale and nature of the research context. Large, heavily funded projects often have internal infrastructure with dedicated data management personnel; smaller projects are more likely to benefit from support from data supports services offered by an academic library ([[Ray, 2014>>||anchor="Ray"]]).
42 42 \\Often there are two levels of metadata that are of concern for research data: annotation on the spot that researchers do in the context of everyday data management, and high-level bibliographic metadata afforded by librarian expertise. When metadata is conceptualized as a shared responsibility project researchers themselves might produce on the spot metadata, and need training in best practices; a librarian might then later produce bibliographic metadata to facilitate discovery.
43 -\\To support documentation of everyday data management it can be helpful for researchers to commit to putting aside time at the end of each work session, and at project milestones, to document project activities (Long, 2009).
43 +\\To support documentation of everyday data management it can be helpful for researchers to commit to putting aside time at the end of each work session, and at project milestones, to document project activities ([[Long, 2009>>||anchor="Long"]]).
44 44
45 45 == 3.2.6 Provide training for researchers and librarians ==
46 46
47 -When metadata creation is conceptualized as a shared responsibility, training can be helpful for both researchers and librarians (Riley, 2014). (% style="font-size: 14.44444465637207px;" %)Training for researchers can be in the form of general information appropriate for a broad range of researchers delivered at key points in the research life cycle (DMPTool offers guidelines for generating metadata as part of data management planning: ), or discipline specific training on data management practices (Colorado Clinical and Translational Sciences Institute (CCTSI) offers education in data management best practices for translational biomedical research via a website with videos: [[http:~~/~~/cctsi.ucdenver.edu/RIIC/Pages/DataManagement.aspx>>url:http://cctsi.ucdenver.edu/RIIC/Pages/DataManagement.aspx||rel="__blank"]]).
47 +When metadata creation is conceptualized as a shared responsibility, training can be helpful for both researchers and librarians ([[Riley, 2014>>||anchor="Riley"]]). Training for researchers can be in the form of general information appropriate for a broad range of researchers delivered at key points in the research life cycle (DMPTool offers guidelines for generating metadata as part of data management planning: ), or discipline specific training on data management practices (Colorado Clinical and Translational Sciences Institute (CCTSI) offers education in data management best practices for translational biomedical research via a website with videos: [[http:~~/~~/cctsi.ucdenver.edu/RIIC/Pages/DataManagement.aspx>>url:http://cctsi.ucdenver.edu/RIIC/Pages/DataManagement.aspx||rel="__blank"]]).
48 48
49 -(% style="font-size: 14.44444465637207px;" %)A promising approach to researcher data management education is the [[TIER protocol>>url:http://www.haverford.edu/TIER/||rel="__blank" style="font-size: 14.44444465637207px;"]] developed by Ball and Medeiros at Haverford College. This approach to researcher education is to experientially teach data management practices that produce replicable analysis through the structure of deliverables required for student research projects. The rationale is that if budding researchers learn data management when they learn research methods, sound documentation practices are not perceived as a hardship.(%%)
50 -\\Training for librarians. When metadata support is offered as a service delivered by subject liaison librarians, training for librarians can come via online resources (e.g. the Digital Curation Centre's curation resources [[http:~~/~~/www.dcc.ac.uk/resources>>url:http://www.dcc.ac.uk/resources]] and training materials [[http:~~/~~/www.dcc.ac.uk/training>>url:http://www.dcc.ac.uk/training]], and Purdue University's Data Profile Toolkit [[http:~~/~~/datacurationprofiles.org/)>>url:http://datacurationprofiles.org/)]], or more in-depth professional development (e.g. ICPSR has a one-week summer training program [[https:~~/~~/www.icpsr.umich.edu/icpsrweb/sumprog/courses/0149>>url:https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0149||rel="__blank"]]), or formal education (five library schools in the United States offer data curation programs (Riley, 2014)).
49 +A promising approach to researcher data management education is the [[TIER protocol>>url:http://www.haverford.edu/TIER/||rel="__blank"]] developed by Ball and Medeiros at Haverford College. This approach to researcher education is to experientially teach data management practices that produce replicable analysis through the structure of deliverables required for student research projects. The rationale is that if budding researchers learn data management when they learn research methods, sound documentation practices are not perceived as a hardship.
50 +\\Training for librarians. When metadata support is offered as a service delivered by subject liaison librarians, training for librarians can come via online resources (e.g. the Digital Curation Centre's curation resources [[http:~~/~~/www.dcc.ac.uk/resources>>url:http://www.dcc.ac.uk/resources]] and training materials [[http:~~/~~/www.dcc.ac.uk/training>>url:http://www.dcc.ac.uk/training]], and Purdue University's Data Profile Toolkit [[http:~~/~~/datacurationprofiles.org/)>>url:http://datacurationprofiles.org/)]], or more in-depth professional development (e.g. ICPSR has a one-week summer training program [[https:~~/~~/www.icpsr.umich.edu/icpsrweb/sumprog/courses/0149>>url:https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0149||rel="__blank"]]), or formal education (five library schools in the United States offer data curation programs ([[Riley, 2014>>||anchor="Riley"]]).
51 51
52 52 == 3.2.7 Assess community data and metadata practices ==
53 53
54 -The provision of metadata services requires understanding of existing research community metadata practices, in addition to metadata structures associated with libraries (Ray, 2014). Purdue university’s data curation profiles which are generated via interviews are one such approach for librarians to increase their knowledge of existing practices. Another approach is to use small pilot studies early on in development of data curation services (Westra, 2014).
54 +The provision of metadata services requires understanding of existing research community metadata practices, in addition to metadata structures associated with libraries ([[Ray, 2014>>||anchor="Ray"]]). Purdue university’s data curation profiles which are generated via interviews are one such approach for librarians to increase their knowledge of existing practices. Another approach is to use small pilot studies early on in development of data curation services ([[Westra, 2014>>||anchor="Westra"]]).
55 55
56 56 == References ==
57 57
58 -
59 -
60 -Hale (2003).
61 -
62 -
63 -Jahnke, L., Asher, A., & Keralis, S. D. (2012). The problem of data. Council on Library and Information Resources (CLIR) Report, pub. #154. ISBN 978-1-932326-42-0 Retrieved from http:~/~/digitalcommons.bucknell.edu/fac_pubs/52/
58 +{{id name="Jahnke"/}}
59 +Jahnke, L., Asher, A., & Keralis, S. D. (2012). The problem of data. Council on Library and Information Resources (CLIR) Report, pub. #154. ISBN 978-1-932326-42-0 Retrieved from [[http:~~/~~/digitalcommons.bucknell.edu/fac_pubs/52/>>url:http://digitalcommons.bucknell.edu/fac_pubs/52/||rel="__blank"]]
64 64
61 +{{id name="Long"/}}
65 65 Long, J Scott. (2009). //The workflow of data analysis using Stata//. College Station, Texas: Stata Press Books.
66 66
64 +{{id name="Ray"/}}
67 67 Ray, J. M. (2014). Introduction to research data management. In J. Ray (Ed.), //Research Data Management: Practical Strategies for Information Professionals (Charleston Insights in Library, Information, and Archival Sciences)//. Purdue University Press.
68 68
67 +{{id name="Riley"/}}
69 69 Riley, Jenn. (2014). Metadata services. In J. Ray (Ed.), //Research Data Management: Practical Strategies for Information Professionals (Charleston Insights in Library, Information, and Archival Sciences).// Purdue University Press.\\
70 70
70 +{{id name="Tenopir"/}}
71 71 Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., Manoff, M., Frame, M. (2011). Data Sharing by Scientists: Practices and Perceptions. //PLoS ONE//, //6//(6), e21101. doi:10.1371/journal.pone.0021101. Retrieved from [[http:~~/~~/www.plosone.org/article/info:doi/10.1371/journal.pone.0021101>>url:http://www.plosone.org/article/info:doi/10.1371/journal.pone.0021101]]
72 72
73 -
73 +{{id name="Westra"/}}
74 74 Westra, Brian (2014). In J. Ray (Ed.), //Research Data Management: Practical Strategies for Information Professionals (Charleston Insights in Library, Information, and Archival Sciences).// Purdue University Press.
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