Yu, H.H. (2017), "The role of academic libraries in research data service (RDS) provision: Opportunities and challenges", The Electronic Library, Vol. 35 No. 4, pp. 783-797. https://doi.org/10.1108/EL-10-2016-0233

Definition: Research Data Service

“The two types of RDS provision defined in the literature are informational RDS and technical RDS. The former covers a wide range of services, such as consulting on data management plans (DMP) and reference support. The latter entails providing repository access and discovery systems, preparing data or datasets to be deposited into a repository and creating or transforming metadata.”

Includes literature review - references are a good resource

Soehner, C., Steeves, C. and Ward, J. (2010), E-science and Data Support Services: A Study of ARL Member Institutions, Association of Research Libraries, Washington, DC. https://www.arl.org/resources/e-science-and-data-support-services/

Method tip: needs assessment

More than a third of the respondents (16 of 42) reported conducting needs assessments relating to data services and more were planning assessment activities. These often proved to be sources of substantial information on variations in needs as well as gaps in existing e-science support services.

Examples of surveys:

http://digital.library.wisc.edu/1793/34859

https://research-it.ucsd.edu/otherresources/UCSD-Blueprint-for-the-Digital-University.pdf (p. 74-91 Questionnaire + results)

Erway, Ricky, Laurence Horton, Amy Nurnberger, Reid Otsuji, and Amy Rushing. 2015. Building Blocks:
Laying the Foundation for a Research Data Management Program. Dublin, Ohio: OCLC Research.
http://www.oclc.org/content/dam/research/publications/2016/oclcresearch-data-management-building-blocks-2016.pdf

Method tips: needs assessment, raising awareness

A practical guide for institutions (academic libraries) only starting to provide RDS, but includes useful points even for established data services. Examples of needs assessment and tips on "awareness, promotion and outreach" (p. 10 & 20)

Method tips: peer-review of training materials, collaborative material development using GitHub

Project outcome: Data Management Skillbuilding Hub https://dataoneorg.github.io/Education

"In order to ensure that educational resources designed to teach skills and best practices for scientific research data sharing and management are useful, the maintainers of those materials need to periodically evaluate and update them to ensure their accuracy, currency, and overall quality. This paper has described the process of integrating outside peer review of community resources as part of a comprehensive evaluation for addressing these concerns. It further outlines the motivations, concerns, and actions of moving the updated educational materials to an online community platform (GitHub) in order to build upon mechanisms for open science, ongoing iteration, participation, attribution, and transparent community engagement."

Older (2011), main focus on the UK

Method tips: institutional development of RDM services including raising awareness

Focus on building RDM services in academic institutions. Although published in 2011, many points are still useful, see, e.g. Table 2: A suggested timeline for institutional development.

"The first priority for researchers is typically more clarity on roles and
responsibilities and the support available."

Fearon, David Jr., Betsy Gunia, Sherry Lake, Barbara E. Pralle, and Andrew L. Sallans. Research Data Management Services. SPEC Kit 334. Washington, DC: Association of Research Libraries, July 2013. https://doi.org/10.29242/spec.334

https://publications.arl.org/Research-Data-Management-Services-SPEC-Kit-334/1

Definition: Research Data Management Service

"Research Data Management Services support the management and curation of research data throughout its life cycle. RDM includes services such as: data management plan consulting, data documentation/metadata, data organization, data security and backup, data citation, funder requirements, ethical and legal issues, preserving digital data, sharing data and archiving data."

Survey results: best practices in marketing

"Finally, the third biggest challenge reported is faculty (non)engagement due to a lack of awareness of services that the library provides, low perceived value of services, and resistance to data sharing. Respondents stated that the most effective marketing techniques were through workshops and presentation to researchers, referrals from research project (grants) administration, and direct emails to researchers (Q50)." See more in survey results on p. 78, more marketing tips on pp. 86-87.

Impact assessment tips p. 79-80

Includes practical examples of data management service websites, DMP tools, data retention policies, data needs assessment instruments, and outreach materials from the participating institutions. Unfortunately materials from 2013 can be rather outdated, but can still provide some tips. 

Preprint - not peer reviewed!!

Focus on Australia, UK, USA

Tool tips: using DMPs and DMP templates in a way that would actually benefit the researcher

"We acknowledge, from our own experience, that DMP completion can be a catalyst for conversations with institutional staff who can provide help or point researchers to relevant support service providers. (...) These are services the researcher might not have otherwise known existed had they not completed a DMP. Connecting researchers with these services can enable researchers to gain access to tools and expertise to better manage their research outputs, request storage allocation, discover and deploy collaborative tools, request high performance computing, and access training (...) A well-designed DMP template can also provide general guidance to researchers on how to avoid downstream issues such as those related to IP, ethics and data publication. (...)

Despite the above benefits of integrating education and referral services into DMPs, it is our contention that mandatory and poorly thought through DMP templates may be driving researchers to be minimally engaged with the process, applying minimal effort and producing low-quality or insincere DMPs. Given the significant contemporary focus on DMP completion as a means to achieve good data management practices, we are also concerned that the act of completing a DMP, no matter the quality or thought put into the exercise, leads researchers to think that their research data is well managed. Mere DMP completion does not necessitate, predict, or imply good data management practices."

See especially p.22-24 "Use by institutions to change researcher behaviour", "Use as an institutional business intelligence and systems integration tool" & "Use by researchers for project management"

Parr C., McCarthy S. (2019), "Building Capacity for Data Science with Help from our Friends", Research Library Issues no 298, 28 - 40. https://doi.org/10.29242/rli.298.4

Method tips: Engaging master´s students (data science) and postdocs in building and testing services.

"... the answer to “why explore data science?” is that institutional experience with core data-science activities will inform the larger set of data and data management services the library performs." (p. 28)