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3566 Longitudinal analysis of research collaborations and emerging networks

Published online by Cambridge University Press:  26 March 2019

Ann Marie Dozier
Affiliation:
University of Rochester Medical Center
Elizabeth Wayman
Affiliation:
University of Rochester Medical Center
Camille Anne Martina
Affiliation:
University of Rochester Medical Center
Nicole O’Dell
Affiliation:
Yale University
Eric P. Rubinstein
Affiliation:
University of Rochester Medical Center
Thomas T Fogg
Affiliation:
Yale University
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Abstract

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OBJECTIVES/SPECIFIC AIMS: To longitudinally track emerging research collaborations and assess their development and productivity. METHODS/STUDY POPULATION: In four administrations (2011, 2013, 2015, 2017), all full- and part-time University of Rochester Medical Center faculty received an email invitation to complete a research collaborators survey. Respondents indicated whether they were involved in research, and if involved in research, identified collaborators from a drop-down list of investigators in the institution. Space was provided for write-ins. Full- and part-time status, faculty rank, and departmental affiliation was associated with each investigator. Grant data were obtained from a grant management database maintained by the institution’s Office of Research and Project Administration. Grant data included all submissions (funded and not funded), award number, award effective data, award final expiration date, funding amounts, principal investigator and co-investigators. Using Mathematica SNA software, for each year we identified collaborator dyads (including their characteristics such as inter/intradepartmental; investigator characteristics) and networks (e.g. size, density). RESULTS/ANTICIPATED RESULTS: On average, 1800 (range 1730-2034) full- and part-time faculty received email invitations to complete the survey. An average of 403 respondents (range 385-441) completed the survey each administration. While the response rate seems low, the survey was distributed to every faculty member regardless of their primary appointment. Thus it included a large number of individuals whose role is exclusively clinical. Grant data included 4429 awards received between 2011 and 2018, involving 1395 investigators as principal or co-investigators. Survey respondents naming collaborators ranged from 233 to 280 (average 257) with 1594 to 2265 (average 1988) collaborations named each year. Overall density increased from.0204 in 2011 to.0342 in 2017. Density within the group of female investigators increased from.0219 in 2011 to.0412 in 2017. Within the group of male investigators, density increase from.0226 to.0333 in the same time span. Analysis by rank, changes over time and those with grant funding is underway. DISCUSSION/SIGNIFICANCE OF IMPACT: This methodology captured a consistent number of collaborations over an 8 year period. Analyses reveal network growth over time and of increasing heterogeneity (by gender). Analyzing research networks overtime provides an important metric to assess how research networks evolve and devolve and the characteristics of those that grow or stagnate. Further these analyses can demonstrate the impact of support provided to networks or teams by the CTSI, department or other institutional mechanism.

Type
Team Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Association for Clinical and Translational Science 2019