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This exploratory study applies social network analysis techniques to existing, publicly available data to understand collaboration patterns within the co-author network of a federally-funded, interdisciplinary research program. The central questions asked: What underlying social capital structures can be determined about a group of researchers from bibliometric data and other publicly available existing data? What are ways social network tools characterize the interdisciplinarity or cross-disciplinarity of co-author teams? The names of 411 grantees were searched in the Web of Science indexing database; author information from the WoS search results resulted in a 191-member co-author network. Research domains were included as attribute data for the co-author network. UCINet social network analysis software calculated a large 60 node component and two larger components with 12 and 8 nodes respectively, the remainder of the network consisted of smaller 2-5 node components. Within the 191-node co-author network the following analyses were performed to learn more about the structural social capital of this group: Degree and Eigenvector centrality measures, brokerage measures, and constraint measures. Additionally, ten randomly selected dyads and the five 4-node cliques within the 191-node network were examined to find patterns of cross-disciplinary collaboration among researcher and within award teams. Award numbers were added as attribute data to five 4-node cliques and 10 random dyads; these showed instances of collaboration among interdisciplinary award teams. Collaboration patterns across disciplines are discussed. Data from this research could serve as a baseline measure for growth in future analyses of the case studied. This method is recommended as a tool to gain insights to a research community and to track publication collaboration growth over time. This research method shows potential as a way to identify aspects of a research community?s social structural capital, particularly within an interdisciplinary network to highlight where researchers are working well together or to learn where there is little collaboration.