Co-leading Teams Drive Scientific Novelty in Large-scale Research Infrastructures. Zhang, M., Li, Y., Peng, H., & Li, Z. August, 2026. arXiv:2608.13195 [cs.DL]![Co-leading Teams Drive Scientific Novelty in Large-scale Research Infrastructures [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@misc{zhang_co-leading_2026,
title = {Co-leading {Teams} {Drive} {Scientific} {Novelty} in {Large}-scale {Research} {Infrastructures}},
url = {http://arxiv.org/abs/2608.13195},
doi = {10.48550/arXiv.2608.13195},
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.},
urldate = {2026-08-18},
publisher = {arXiv},
author = {Zhang, Mingze and Li, Yizhan and Peng, Hao and Li, Zexia},
month = aug,
year = {2026},
note = {arXiv:2608.13195 [cs.DL]},
keywords = {Computer Science - Digital Libraries},
}
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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. 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