The increasing complexity of modern scientific challenges has led to extensive collaboration within Big Science. This study introduces co-utilization – a novel type of collaboration in which researchers leverage multiple global big science facilities (BSFs) to science advance. By analyzing 245,984 publications from 40 BSFs, we identify 23,046 publications that utilized multiple facilities, revealing an uneven global distribution influenced by user demand for diverse technologies and geographical constraints. Our findings indicate that co-utilization is associated with lower disruptive potential but enhances scientific impact. Through a comprehensive analysis of five key factors, we find that diversity in knowledge contributes, while technological diversity might undermine the disruption. Specifically, inter-community collaboration, international co-utilization, and low knowledge similarity positively influence disruption, while cross-energy co-utilization has a slightly negative effect. Additionally, we uncover that past co-utilization times negatively while time span of co-utilizations positively associated with disruption, suggesting that both the establishment of new networks and the maintenance of long-term collaborations are crucial. The U-shaped impacts of knowledge similarity on disruption are also discussed and the low- or high-level similarity might describe different mechanisms. These findings provide valuable insights for facility managers, policymakers, and funding agencies, emphasizing the need to support strategic co-utilization networks and promote knowledge-diverse collaboration. By shedding light on this emerging collaboration model, our study contributes to the ongoing exploration of scientific disruption and Big Science governance.
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Mingze Zhang
Lili Wang
Lingling Zhang
Humanities and Social Sciences Communications
Maastricht University
University of Chinese Academy of Sciences
United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology
Building similarity graph...
Analyzing shared references across papers
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Zhang et al. (Fri,) studied this question.
synapsesocial.com/papers/69bf86ecf665edcd009e9126 — DOI: https://doi.org/10.1057/s41599-026-06992-9