联合领导团队推动大型研究基础设施中的科学创新
Co-leading Teams Drive Scientific Novelty in Large-scale Research Infrastructures
AI总结：
本研究以76个LSRIs的27万余篇出版物为基础，采用混合机器学习框架，发现内部人员联合领导的团队能为外部用户带来显著科学创新溢价，且该架构随用户经验动态演变，为LSRIs管理提供政策启示。
AI中文摘要：
大型研究基础设施（LSRIs）已成为现代科学发现的引擎。这些大型装置主要采用用户导向模式，外部团队在内部研究人员支持下开展研究，但将内部科研人员整合进用户团队的结构及其与科学创新的关联仍不明确。本研究利用全球76个大型研究基础设施产出的273109篇出版物数据集，采用混合机器学习框架将论文分为三种合作模式：仅外部用户、内部人员参与、内部人员联合领导，发现外部团队将内部人员作为共同作者正式整合时，会获得显著的创新溢价，尤其是内部科研人员担任联合领导而非参与角色时。此外，溢价在用户-内部人员团队构成相对平衡时达到峰值，这可能是由于任何一方的“认知锁定”。关键的是，理想的合作架构会随用户经验演变：新用户仅通过内部人员参与就能获得大量创新溢价，而经验丰富的用户仅从内部人员联合领导的团队中获益。这一结果表明存在“知识饱和效应”，即需要更深层次的智力伙伴关系来维持创新。本研究揭示了用户-内部人员合作结构如何驱动科学创造力，为人机协作时代大型研究基础设施的战略管理和干预提供了实用政策启示。
英文摘要：
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.
