Open AccessBreaking Down Silos: Mapping Growth of Cross‐Disciplinary Collaboration in a Translational Science Initiative
Author(s) -
Luke Douglas A.,
Carothers Bobbi J.,
Dhand Amar,
Bell Ryan A.,
MorelandRussell Sarah,
Sarli Cathy C.,
Evanoff Bradley A.
Publication year - 2015
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/cts.12248
Subject(s) - cross disciplinary , translational science , translational research , icts , publication , discipline , social network analysis , bibliometrics , political science , library science , public relations , engineering ethics , data science , medicine , sociology , computer science , engineering , social science , information and communications technology , social media , law , pathology
The importance of transdisciplinary collaboration is growing, though not much is known about how to measure collaboration patterns. The purpose of this paper is to present multiple ways of mapping and evaluating the growth of cross‐disciplinary partnerships over time. Social network analysis was used to examine the impact of a Clinical and Translational Science Award (CTSA) on collaboration patterns. Grant submissions from 2007 through 2010 and publications from 2007 through 2011 of Institute of Clinical and Translational Sciences (ICTS) members were examined. A Cohort Model examining the first‐year ICTS members demonstrated an overall increase in collaborations on grants and publications, as well as an increase in cross‐discipline collaboration as compared to within‐discipline. A Growth Model that included additional members over time demonstrated the same pattern for grant submissions, but a decrease in cross‐discipline collaboration as compared to within‐discipline collaboration for publications. ICTS members generally became more cross‐disciplinary in their collaborations during the CTSA. The exception of publications for the Growth Model may be due to the time lag between funding and publication, as well as pressure for younger scientists to publish in their own fields. Network analysis serves as a valuable tool for evaluating changes in scientific collaboration.