Does Collocation Inform the Impact of Collaboration?
Name
Lee-2010-Does Collocation Inform the Impact of Collaboration.pdf
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3.68 MB
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Author(s)
Kohane, Isaac
Lee, Kyungjoon
Brownstein, John S.
Mills, Richard G.
Date Issued
December 2010
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Lee, Kyungjoon, John S. Brownstein, Richard G. Mills and Isaac S. Kohane. "Does Collocation Inform the Impact of Collaboration?" (2010) PLoS ONE 5(12): e14279.
Version
Final published version
Abstract
Background
It has been shown that large interdisciplinary teams working across geography are more likely to be impactful. We asked whether the physical proximity of collaborators remained a strong predictor of the scientific impact of their research as measured by citations of the resulting publications.
Methodology/Principal Findings
Articles published by Harvard investigators from 1993 to 2003 with at least two authors were identified in the domain of biomedical science. Each collaboration was geocoded to the precise three-dimensional location of its authors. Physical distances between any two coauthors were calculated and associated with corresponding citations. Relationship between distance of coauthors and citations for four author relationships (first-last, first-middle, last-middle, and middle-middle) were investigated at different spatial scales. At all sizes of collaborations (from two authors to dozens of authors), geographical proximity between first and last author is highly informative of impact at the microscale (i.e. within building) and beyond. The mean citation for first-last author relationship decreased as the distance between them increased in less than one km range as well as in the three categorized ranges (in the same building, same city, or different city). Such a trend was not seen in other three author relationships.
Conclusions/Significance
Despite the positive impact of emerging communication technologies on scientific research, our results provide striking evidence for the role of physical proximity as a predictor of the impact of collaborations.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0014279
