Dhand Amar, Luke Douglas A, Carothers Bobbi J, Evanoff Bradley A
Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America.
George Warren Brown School of Social Work, Center for Public Health Systems Science, Washington University in St. Louis, St. Louis, Missouri, United States of America.
PLoS One. 2016 Jan 13;11(1):e0145916. doi: 10.1371/journal.pone.0145916. eCollection 2016.
Academic collaboration is critical to knowledge production, especially as teams dominate scientific endeavors. Typical predictors of collaboration include individual characteristics such as academic rank or institution, and network characteristics such as a central position in a publication network. The role of disciplinary affiliation in the initiation of an academic collaboration between two investigators deserves more attention. Here, we examine the influence of disciplinary patterns on collaboration formation with control of known predictors using an inferential network model. The study group included all researchers in the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis. Longitudinal data were collected on co-authorships in grants and publications before and after ICTS establishment. Exponential-family random graph models were used to build the network models. The results show that disciplinary affiliation independently predicted collaboration in grant and publication networks, particularly in the later years. Overall collaboration increased in the post-ICTS networks, with cross-discipline ties occurring more often than within-discipline ties in grants, but not publications. This research may inform better evaluation models of university-based collaboration, and offer a roadmap to improve cross-disciplinary collaboration with discipline-informed network interventions.
学术合作对于知识产出至关重要,尤其是在团队主导科学研究活动的情况下。合作的典型预测因素包括个人特征,如学术职称或机构,以及网络特征,如在出版网络中的中心地位。学科归属在两位研究者发起学术合作中所起的作用值得更多关注。在此,我们使用推理网络模型,在控制已知预测因素的情况下,研究学科模式对合作形成的影响。研究组包括圣路易斯华盛顿大学临床与转化科学研究所(ICTS)的所有研究人员。收集了ICTS成立前后资助项目和出版物中的共同作者关系的纵向数据。使用指数族随机图模型构建网络模型。结果表明,学科归属独立预测了资助项目和出版网络中的合作,尤其是在后期。ICTS成立后的网络中总体合作有所增加,在资助项目中跨学科联系比学科内联系更频繁出现,但在出版物中并非如此。这项研究可为基于大学的合作的更好评估模型提供参考,并提供一条通过学科导向的网络干预来改善跨学科合作的路线图。