This new preprint is the result of a collaboration initiated during my postdoctoral stay at the Barabasi lab in Boston, which I continued at the LPI as an affiliated professor. In this project, we introduce the synthetic biology competition iGEM as a model system for the Science of Science and Innovation, enabling large-scale “laboratory ethnography.” We present the collection and analysis of laboratory notebooks data from 3,000 teams, which we deposited on the open archive Zenodo. We highlight the organizational characteristics (intra- and inter-team collaboration networks) of teams related to learning and success in the competition. In particular, we emphasize how teams overcome coordination costs as they grow in size, as well as the crystallization of the inter-team collaboration network over time, limiting access to relational capital for peripheral teams. This work is currently funded by an ANR JCJC grant to collect field data and build network models of collaborations and performance.

Understanding how teams function has become a central concern in both the science of science and innovation studies. While it is well-established that teams now dominate knowledge production, the organizational processes underpinning team performance and learning remain difficult to observe systematically. In this work, we present the iGEM synthetic biology competition as a platform for large-scale, longitudinal analysis of team science. By analyzing open digital lab notebooks from over 3,000 teams, we gain access to the micro-dynamics of collaboration, including division of labor, intra- and inter-team networks, and long-term learning trajectories.

Our findings indicate that team organization follows consistent structural patterns: most contributions are concentrated within a small active core, and collaboration within sub-teams tends to plateau around eight members, possibly reflecting coordination thresholds. As teams grow, success appears to depend less on size per se and more on how effectively members synchronize and integrate their efforts—a dynamic captured through a new metric of core task overlap.

The inter-team collaboration network also reveals important dynamics. While the overall density of connections increases, we observe growing disparities in access to collaboration. Peripheral teams tend to face structural barriers in acquiring relational capital, reflecting broader patterns of inequality in scientific ecosystems. Notably, our longitudinal analysis identifies a lock-in effect: teams that succeed early are more likely to sustain high performance, whereas those that encounter early setbacks often struggle to recover—unless they adopt more distributed and collaborative practices. Teams that improve over time tend to make these shifts before any measurable success is visible. This suggests that early organizational interventions may have long-term benefits.

Taken together, these results offer new insight into the organizational foundations of team learning and collective intelligence. The iGEM competition—through its standardized, open, and multi-year structure—offers a valuable setting for investigating these dynamics. The findings have direct implications for research policy, education, and the design of interdisciplinary innovation ecosystems.