How researchers collaborate across disciplines? Patterns of interdisciplinary collaboration based on a dual-perspective framework

Zhe Cao, Lin Zhang, Ying Huang
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Abstract

With a combination of new data sources and mixed methods including bibliometrics, machine learning and network analysis, this study puts forward a new framework of categorizing different interdisciplinary collaboration patterns from a discipline-contribution perspective. Based on 20,542 research articles published on PLoS series of journals in 2018, 14,744 articles with interdisciplinary collaborations (ICAs) are recognized. By establishing six indicators that measure the variety, similarity and balance of authors’ disciplines and their contribution roles, ICAs are divided into four categories after the agglomerative hierarchical clustering. With a fine-grained analysis of the structural and correlation characteristics of authors’ disciplines and contribution in different clusters, four interdisciplinary collaboration patterns of sheep flock, bee colony, intercropping and rainforest are found. Our results may contribute to developing new methodologies and theories of interdisciplinary collaboration, and enrich the understanding of interdisciplinary collaboration as well as relevant policies.
研究人员如何跨学科合作?基于双重视角框架的跨学科合作模式
本研究结合文献计量学、机器学习和网络分析等新数据来源和混合方法,从学科贡献的角度提出了不同跨学科合作模式分类的新框架。2018年,在PLoS系列期刊上发表的20542篇研究论文中,有14744篇被认可为跨学科合作(ICAs)。通过建立衡量作者学科及其贡献角色的多样性、相似性和平衡性的6个指标,经聚类层次聚类后将ica划分为4类。通过细粒度分析作者学科的结构和关联特征以及不同集群的贡献,发现了羊群、蜂群、间作和雨林四种跨学科协作模式。本研究结果将有助于开发新的跨学科合作方法和理论,并丰富对跨学科合作及其相关政策的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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