Co-leading Teams Drive Scientific Novelty in Large-scale Research Infrastructures

2026-08-13

AI Summary

This study investigates how collaboration between external users and in-house researchers in large-scale scientific facilities influences scientific novelty—a relationship that remains poorly understood. Leveraging a dataset of 270,000 publications, the authors develop a hybrid machine learning framework to identify three distinct collaboration patterns and quantify their impact on novelty. They find, for the first time, that scientific novelty peaks when in-house researchers participate as co-leads and the ratio of users to staff is balanced. Notably, experienced users benefit significantly only under this specific configuration, providing empirical support for the “knowledge saturation effect.” These findings uncover a dynamic optimization mechanism underlying collaborative structures and offer evidence-based guidance for the design of scientific organizations.

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.

Problem

• large-scale research infrastructures
• scientific novelty
• user-staff collaboration
• co-leading teams
• epistemic lock-in

Innovation

• scientific novelty
• large-scale research infrastructures
• co-leading collaboration
• knowledge saturation effect
• epistemic lock-in