Research Data Management Principles, Practices, and Prospects Page: 25
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Research Data Management in Policy and Practice: The DataRes Project
Further, the existing institutional policies are weak, and compli-
ance tends to be limited because the only way to compel faculty to
adhere to such a policy is to make compliance a mandate for tenure
and promotion, a step no institution is willing to take. Focus group
respondents uniformly reported that researchers tend to be reluc-
tant to share data, considering them either residual products of their
research or something so idiosyncratic, specialized, or proprietary
that they simply prefer not to share the data. Further, data as such
are neither valued nor rewarded as research products for tenure and
promotion, so they will not be a priority for research faculty whose
efforts are focused on publication and the next grant application.
Of those institutions lacking publicly available policies for data
management, it is possible that some have such policies, but that
they are not public-facing. It is also possible that some institutions
are in the process of revising their data management policies or
drafting new policies in response to the demands of NSF and other
funding agencies. However, given the pace of change at most institu-
tions, it may be years before new policies are implemented.
Data Management in Libraries
Unsurprisingly, given the emphasis of federal agencies on the data
management plan itself, many efforts at both the library and insti-
tutional levels have focused on support for researchers writing their
plans rather than on implementing the plans. For example, as of this
writing, more than 100 institutions are registered with the DMPTool,
meaning that they have Shibboleth login access to the tools for local
researchers to develop plans, as opposed to the eight contributing
institutions working on development of the tool (California Digital
Library, 2013a). Although this focus is certainly important and re-
flects the short-term needs of researchers, it does not address what
is necessary to implement a data management plan. Development of
resources for long-term preservation and access to research data has
been uneven and is generally less robust than support services for
plan development.
In the course of our research, we identified 32 universities where
libraries are providing some level of data management plan sup-
port for researchers, but this number is far from comprehensive.
Models of support vary widely, from simple web pages linking back
to the policy and guidance documents of federal funding agencies,
to programs that offer workshops and other practical support for
researchers, to infrastructure projects costing millions of dollars per
year, or a combination of these. At the University of Minnesota, for
example, the libraries provide a range of data management support
functions (University of Minnesota 2011). Library specialists can help
draft data management plans, consult on funding agency require-
ments, confer on subject-specific data repositories, and give access to
on-campus research computing resources. In collaboration with the
university office of research, the library also offers data managementworkshops to graduate students, faculty, and researchers. These
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Asher, Andrew; Deards, Kiyomi; Esteva, Maria; Halbert, Martin; Jahnke, Lori; Jordan, Chris et al. Research Data Management Principles, Practices, and Prospects, book, November 2013; Washington, DC. (https://digital.library.unt.edu/ark:/67531/metadc234929/m1/34/: accessed March 29, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .