  Collaboration plays a key role in knowledge production. Here, we show that
patterns of interaction during conferences can be used to predict who will
subsequently form a new collaboration, even when interaction is prescribed
rather than freely chosen. We introduce a novel longitudinal dataset tracking
patterns of interaction among hundreds of scientists during multi-day
conferences encompassing different scientific fields over the span of 5 years.
We find that participants who formed new collaborations interacted 63% more on
average than those who chose not to form new teams, and that those assigned to
a higher interaction scenario had more than an eightfold increase in their odds
of collaborating. We propose a simple mathematical framework for the process of
team formation that incorporates this observation as well as the effect of
memory beyond interaction time. The model accurately reproduces the
collaborations formed across all conferences and outperforms seven other
candidate models. This work not only suggests that encounters between
individuals at conferences play an important role in shaping the future of
science, but that these encounters can be designed to better catalyze
collaborations.
