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ABSTRACT / Science and innovation are increasingly conducted in large and open communities of contributors who collaborate and compete to solve complex problems in ways that traditional organizations are ill suited to manage. While governments, universities and firms have broadly recognized the potential of these communities, we are still lacking in situ quantitative insights on micro-level collaborative processes underlying performance. Here, I will present a large-scale, fine-grain, multi-level, multi-year testbed for studying how team processes underlie team performance and improvement in an open science and engineering interdisciplinary collaborative ecosystem. Using organizational and performance data from 2,168 teams participating in the international Genetically Engineered Machine (iGEM) synthetic biology competition over ten years, we find remarkable institutional stability across time, shared organizational patterns across teams and features associated with team performance and improvement over time. We uncover two distinct regimes of team performance, where small teams rely on team size and external collaborations, while large teams rely on internal coordination and task allocation. Finally, we find that the key difference between teams that overcome early setbacks and those that succumb to them is the extent to which they integrate the core of the broader team collaboration network. This testbed opens new venues for designing data-informed strategies for building open science and innovation ecosystems, and I will present our current efforts in that direction through the lens of the OpenCovid19 community of 1,000+ volunteers on the Just One Giant Lab open science platform.
BIO / Marc Santolini is a long-term research fellow at CRI research (INSERM, Universite de Paris) and a visiting researcher at the Barabasi Lab (Network Science Institute at Northeastern University, Boston). He is also the co-founder of Just One Giant Lab, a nonprofit initiative aimed at developing decentralized open science challenges using smart digital tools. Trained as a theoretical physicist at ENS Paris and Princeton University, he developed a strong interest in the universal organisational properties observed in real-world networks in various domains. This led him to work as a postdoc in network science at the Barabasi Lab in Northeastern University and Harvard Medical School. He now leads the Interaction Data Lab at the Center for Research and Interdisciplinarity in Paris to unravel how communities innovate, learn and solve complex problems. The generated insights are used to develop digital tools fostering collective intelligence for social impact, and in 2018 he received the Sage Bionetworks “Young Investigators Award” for his work on “Algorithms and the role of the individual”.