Huffman, Demie, R.Bruns, Catherine, J.Neff, Peter, D.Roop, Heidi, A.2025-02-212025-02-212025-02-21https://hdl.handle.net/11299/270020Data used to conduct this study include a spreadsheet detailing social network survey results from 67 participants, totaling 2,997 identified connections. The spreadsheet also contains the analyzed data for each of 10 quantitative thresholds and typology distributions. Data are stored in a single .csv/Excel file. All identifying information such as names, project groups, and institutional affiliations have been anonymized by random IDs.Funding agencies like the U.S. National Science Foundation (NSF) increasingly fund transdisciplinary research collaboratives to tackle complex societal problems and accelerate innovation. Initiatives such as the NSF Science and Technology Centers (STCs) convene researchers from diverse disciplines to collaborate to address scientific challenges at the nexus of science and technology innovation. The longitudinal evolution of a Center’s social network offers a valuable evaluative tool for understanding how different Center activities and participant identities foster/inhibit an environment conducive to transdisciplinary collaboration and innovation. Given that STC members participate in Center activities with different degrees of involvement, understanding the varying relationships and levels of engagement exhibited within a Center can help to evaluate the effectiveness of team science collaborations in realizing their goals and objectives in real-time. A driving question is whether the whole of an interdisciplinary team is greater than the sum of its parts. In this article, a Science of Team Science mixed-methods social network analysis (SNA) approach is used to evaluate participation and provide data-driven evidence into how relational connections facilitate or hinder pathways for knowledge exchange in an STC called the Center for Oldest Ice Exploration. Using SNA, we establish a set of baseline “participation typologies” with which to measure the evolution of connectivity across the lifetime of the Center. These typologies indicate that pathways to engagement and collaboration are enabled through one’s connection or exposure to different research teams across the Center, as well as through the quality of connection reported between Center participants. Insights from early career researcher participation show how early investment in such activities can strengthen a participant’s connection quality and expose different disciplines to alternative approaches. This methodology can be applied to other large transdisciplinary endeavors to provide real-time evaluation and inform interventions to improve cross-team connections and collaboration.en-USAntarcticaclimatesurvey datatypology analysissocial network analysisDataset