量化临床-学术合作的影响

中文摘要 AI 辅助

学术合作的价值不言而喻，但需要定量表示才能由政策最优地引导。目前尚无既定的方法论方法来进行这种表示。在此，我们引入一个通用的框架，用于对开放数据进行图形和文献计量分析，以量化学术网络的影响，并以英格兰的NIHR生物医学研究中心（BRC）临床-学术伙伴关系作为原型。我们基于每个BRC合作机构的作者联合，为20个英格兰BRC定义了出版物级别的身份。利用文献计量和行政记录，我们刻画了每个网络的图形属性，估计了大学对医院论文的贡献、伙伴关系对基础设施相对较弱的合作机构论文的贡献，以及这种增益如何取决于现有基础设施。我们将我们的框架应用于NIHR UCLH/UCL BRC作为范例。UCLH/UCL自2007年4月起共撰写了20,985篇网络论文，在整个记录中与9,868个不同的外部合作伙伴合作，形成了英格兰网络图中的最核心节点。大学合著论文的领域加权引用影响（FWCI）是仅医院论文的1.6倍，且被专利引用的可能性是后者的2.1倍。在与范例合作最多的60个英国医疗保健组织中，收益从当地NIHR基础设施活动最密集处的1.8倍上升到最稀疏处的3.4倍，而没有范例时的影响变化很小。学术网络可以从开放数据中稳健地识别，从而实现合作影响的比较分析。应用于NIHR BRCs时，该方法能够量化跨网络的影响，并揭示出收益在基础设施最不发达的地方最为显著。

英文摘要

Academic collaboration is of self-evident value but requires a quantitative representation to be optimally guided by policy. No established methodological approach to such representation exists. Here we introduce a general framework of graphical and bibliometric analysis of open data for the task of quantifying the impact of academic networks, with NIHR Biomedical Research Centre (BRC) clinical-academic partnerships in England as the prototype. We define publication-level identities for the 20 English BRCs based on the conjunction of authors from each BRC's partner institutions. Drawing on bibliometric and administrative records, we characterise the graphical properties of each network, estimate what the university adds to the hospital's papers, what the partnership adds to the papers of relatively infrastructure-poor collaborating institutions, and how that gain depends on existing infrastructure. We apply our framework to the NIHR UCLH/UCL BRC as an exemplar. UCLH/UCL authored 20,985 network papers from April 2007, in collaboration with 9,868 distinct external partners over the whole record, forming the most central node of the graph of networks across England. University co-authored papers exhibited 1.6 times the field-weighted citation impact (FWCI) of hospital-only papers, and were 2.1 times as likely to be cited by a patent. Across 60 of the exemplar's most partnered with UK healthcare organisations, the benefit rose from 1.8 times where local NIHR infrastructure activity was densest to 3.4 times where it was sparsest, while impact without the exemplar varied little. Academic networks can be robustly identified from open data, enabling comparative analysis of collaborative impact. Applied to NIHR BRCs, the approach enables quantification of the impact across networks and reveals that benefit is most pronounced where infrastructure is least developed.

发表机构

• UCL Queen Square Institute of Neurology, University College London（伦敦大学学院）
• NIHR University College London Hospitals Biomedical Research Centre（英国国家卫生与临床优化研究所伦敦大学学院医院生物医学研究中心）
• UCL Cancer Institute（伦敦大学学院癌症研究所）

机构由 AI 辅助整理，请以论文原文为准。