Name
Using Social Network Analysis as an Assessment of Team Science Across Two Academic Research Networks
Number
509
Authors

Daniel Knight, University of Colorado Boulder
Kate Doxey, Arizona State University
Sharon Crook, Arizona State University
Kate Cochran, University of Colorado Boulder

Date
Tuesday, July 29, 2025
Time
3:30 PM - 4:30 PM (EDT)
Presentation Category
Team Science in Academia
Description

In 2020, the NSF NeuroNex Program funded four large international research networks with a focus on team science. This investigation looks at two of these networks and evaluates the similarities and differences in team science organization across networks. The Odor2Action Network (O2A) coordinates efforts that study an end-to-end understanding of how brains organize and process information from odors in the environment to guide adaptive behaviors. The Communication, Coordination, and Control in Neuromechanical Systems (C3NS) seeks to address the fundamental question of how biological nervous systems control and execute interactions with the environment. While these groups address different topics, they serve as good comparison networks given their similar funding mechanism, size, organization, aims, and approaches. For this investigation, we conducted Social Network Analysis (SNA) to study team science across each network. We adapted a survey for each network with assessment questions targeting various aspects of research and communication within their groups. We distributed the survey to all network members, and these individuals rated their interactions with each other. We collected baseline data across four years for one network and comparison data across two years for both networks. We analyzed the collected data and created social network graphs for both networks. We calculated metrics to empirically evaluate the structure of each. Results of this work revealed development within the baselines measures for average connectivity and network diameter and similarities across networks on these metrics. Similarities and differences were observed across networks on network graphs. These results highlight the importance of conducting network integration across time.

Abstract Keywords
Social Network Analysis, Academic Research Networks