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Presentation . 2012
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Data in Common(s): Collaborative Models for Robust Data Support

Authors: Samantha Guss; Nicole Scholtz; Jennifer Green; Michelle Hudson;

Data in Common(s): Collaborative Models for Robust Data Support

Abstract

This panel session will explore the issues relating to providing data services within a library research commons by examining three new facilities in academic libraries in the United States: The University of Michigan's Stephen S. Clark Library for Maps, Government Information, and Data Services; New York University's Data Service Studio; and Yale University's Center for Science and Social Science Information. Presentations will focus on aspects such as service models, interactions and relationships to other service providers in the commons, facilities, and levels of data support as they relate to the data lifecycle and needs of library patrons including data management, curation, re-use, and education. This comparison will provide insight into successes, challenges, and lessons learned and will be valuable to those planning and working to improve or implement their own research commons with data service components.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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