Author ORCID Identifier

Jill Cofield: https://orcid.org/0000-0003-3028-3833

Carol Anne Germain: https://orcid.org/0000-0002-8675-3032

Lauren Puzier: https://orcid.org/0000-0002-4019-9312

Emily Kilcer: https://orcid.org/0000-0002-4141-5646

Document Type

Presentation

Publication Date

10-2021

Abstract

Academic libraries at Association of Research Libraries (ARL) & Carnegie R1 universities in the U.S. and Canada provide leadership to deliver comprehensive integrated Web-based data management services for faculty, graduate students, and researchers. Data management makes data more findable, usable, and reproducible; supports an ethical, responsible research environment; and meets funder and journal data-sharing requirements. Since the White House Office of Science and Technology Policy’s 2013 memorandum requiring federal agencies to increase public access to the results of federally funded research, many funders and journals have mandated data planning and sharing. Developing high quality data management plans take time and require training on essential elements and accepted practices. As a result, data management services are in demand by faculty, graduate students, and researchers. To that end, academic libraries have been developing a rich array of data management services that includes support for drafting and reviewing data management plans; sharing best practices related to data sharing, storage, and security; recommending data curation strategies; and more. This poster discusses findings from a survey of 145 ARL and Carnegie R1 library websites in the United States and Canada related to the work libraries are leading to provide user-centered, web-based data management services. We share key data points; identify trends in the development of library-based data management services; and note recommendations for libraries to prepare for future growth in data management services as technology and research continues to evolve and expand.

CLRC2021DataServicesGoodieBag.pdf (226 kB)
CLRC2021 Virtual Poster: Additional Data Services Findings

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