Cross-campus collaboration: A scientometric and network case study of publication activity across two campuses of a single institution.
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| Title: | Cross-campus collaboration: A scientometric and network case study of publication activity across two campuses of a single institution. |
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| Authors: | Birnholtz, Jeremy1 jpb277@cornell.edu, Guha, Shion2 sg648@cornell.edu, Yuan, Y. Connie3 yy239@cornell.edu, Gay, Geri3 gkg1@cornell.edu, Heller, Caren4 cah2021@med.cornell.edu |
| Source: | Journal of the American Society for Information Science & Technology. Jan2013, Vol. 64 Issue 1, p162-172. 11p. 5 Charts, 1 Graph. |
| Subjects: | Universities & colleges, Authors, Databases, Interprofessional relations, Regression analysis, Research funding, Science, T-test (Statistics), Descriptive statistics |
| Geographic Terms: | New York (State) |
| Abstract: | Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross-campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that cross-campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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