Social Network Analysis to Assess the Impact of the CTSA on Biomedical Research Grant Collaboration

Abstract

Success of the Clinical Translational Science Award (CTSA) program implicitly demands team science efforts and well-orchestrated collaboration across the translational silos (T1-T4). Networks have proven to be useful abstractions of research collaborations. Networks provide novel system-level insights and exhibit marked changes in response to external interventions, making them potential evaluation tools that complement more traditional approaches. The present study is part of our ongoing efforts to assess the impact of the CTSA on Biomedical Research Grant Collaboration (BRGC). Collaborative research grants are a complex undertaking and an outcome of sustained interaction among researchers. In this report, BRGC networks representing collaborations among CTSA-affiliated investigators constructed from grants management system data at the University of Kentucky across a period of six years (2007–2012) corresponding to pre- and post-CTSA are investigated. Overlapping community structure detection algorithms, in conjunction with surrogate testing, revealed the presence of intricate research communities rejecting random graphs as generative mechanisms. The deviation from randomness was especially pronounced post-CTSA, reflecting an increasing trend in collaborations and team-science efforts potentially as a result of CTSA. Inter-community crosstalk was especially pronounced post-CTSA.

Toward a science of translational science

“…Highly efficient networks strike a balance between functional segregation (eg, a unique strength of an individual CTSA institute) and global integration (eg, the communication structures and knowledge transfer among the institutes). Although the CTSA has more recently adopted the approach of network science with new language and hub-and-spoke models, the quantitative methods of network science have been applied rarely and only by individual CTSA institutes [15][16][17][18][19]. The current CTSA focus on developing common metrics should be complemented by consideration of the various properties of the nodes (ie, the individual CTSA institutes) and a quantitative definition of the links between them.…”

Scientific Retreats with ‘Speed Dating’: Networking to Stimulate New Interdisciplinary Translational Research Collaborations and Team Science

“…However, our RNS results are in accordance with previous studies where an increasing trend in collaborations and team science efforts was found among the CTSA-affiliated investigators compared to pre- and post-CTSA funding. [17, 18] The RNS tools such as Harvard Profiles have been developed as valuable tools for evaluating changes in scientific collaborations over time. We have adopted Harvard Profiles open source software and used bibliometric data from it for the RNS analysis.…”

Bibliometrics approach to evaluating the research impact of CTSAs: A pilot study

“…Emerging as a strong interest among CTSAs, bibliometrics also prompted CTSA hubs to collaborate with librarians at their institutional health sciences or medical libraries for tracking publications, identifying bibliometric tools and resources, and investigating methods to use publication data for evaluating their progress. In addition, to assess the scope and impact of cross-disciplinary research collaboration, researchers have applied the theories and models of social network analysis (SNA) to analyze research grant data or grant and publication data together [18][19][20][21][22][23]. The adopted methods in previous studies focused on measuring the centrality of individual researcher or group in the cross-disciplinary research collaborations, the density of a research network, the strength of the collaborative relationships, the potential to bridge basic science researchers with clinical investigators, the detection of research communities, and the change of collaboration patterns.…”