Co-leading Teams Drive Scientific Novelty in Large-scale Research Infrastructures
TL;DR
It is found that the ideal collaboration architecture evolves with user experience: while newcomers can obtain a large novelty premium from mere staff participation, experienced users only benefit from staff co-leading teams, which suggests a knowledge saturation effect for which a deeper intellectual partnership is needed to sustain novelty.
Abstract
Large-scale research infrastructures (LSRIs) have become the engine of modern scientific discovery. While these big machines predominantly operate under a user-oriented model where external teams conduct research with support from in-house researchers, the structural integration of staff scientists into user teams and its association with scientific novelty remains unclear. By leveraging a dataset of 273,109 publications across 76 global LSRIs and applying a hybrid machine-learning framework to classify papers into three collaboration patterns: external user only, staff participating, and staff co-leading, we find a distinct novelty premium for external teams that formally integrate staff as co-authors, especially when staff scientists play co-leading rather than participating roles. Further, the premium peaks at a relatively balanced user-staff team composition, potentially due to an"epistemic lock-in"by either party. Crucially, we find that the ideal collaboration architecture evolves with user experience: while newcomers can obtain a large novelty premium from mere staff participation, experienced users only benefit from staff co-leading teams. This result suggests a"knowledge saturation effect"for which a deeper intellectual partnership is needed to sustain novelty. By revealing how user-staff collaboration structure drives scientific creativity, our study offers practical policy implications for the strategic management and intervention of LSRIs in the era of human-machine collaboration.