文章摘要

叶光辉,夏立新.跨地域科研协作模式分析[J].中国图书馆学报,2019,45(3):79~95
跨地域科研协作模式分析
Analysis on Cross Regional Scientific Research Collaboration Model
投稿时间:2018-12-03  修订日期:2018-12-25
DOI:
中文关键词: 科研协作  信息检索  信息搜寻  跨地域  社团分析
英文关键词: Scientific research collaboration  Information search  Information retrieval  Cross regional  Community analysis
基金项目:本文系国家自然科学基金项目“基于标签语义挖掘的城市画像计算与应用模型研究”(编号:71804055)和中央高校基本科研业务费项目“基于社会化标签挖掘的城市画像研究”(编号:CCNU18QN040)研究成果之一
作者单位E-mail
叶光辉 华中师范大学信息管理学院,湖北武汉 430079 3879-4081@163.com,3879-4081@163.com 
夏立新 华中师范大学信息管理学院,湖北武汉 430079  
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中文摘要:
      在系统调研跨地域科研协作现状基础上,本研究提出跨地域科研协作模式分析框架,以信息搜寻与信息检索融合(IS&R)等为测试主题,构建跨地域科研协作网络;计算无向加权科研协作网络节点中心性,发现各主题研究热点国家、城市和机构;模拟有向加权科研协作网络连接强度,描绘科研协作关系中知识流动方向;识别科研协作过程中节点角色,发掘城市科研协作主流模式;通过QAP分析,测度地理距离对节点间科研协作强度的影响,剖析节点科研实力与节点间科研协作强度的相关关系;借助演化分析,厘清科研协作网络发展历程及节点角色迁移情况。结果显示,上述主题在跨地域科研协作过程中既存在共性的节点分布、网络连接和扩展模式,又表现出一定的学科差异。图5。表11。参考文献23。
英文摘要:
Independent research is being promoted to the level of interdisciplinary and cross regional scientific research collaboration, but the research on the latter is far less abundant than the former. Through systematic literature analysis, the current research topics on the status quo and performance evaluation of industry university research cooperation are revealed. The main methods are statistical analysis and network analysis.
The information search and information retrieval (IS&R), health informatics (HI) and user seeking behavior (USB) are used as test subjects, and a total of 3 242 bibliographic records in Web of Science are obtained to construct cross regional scientific research collaboration network. The centrality of node in undirected weighted collaboration network is calculated to discover hotspot countries, cities and universities; simulating the connection strength of directed weighted network, the direction of knowledge flow has been depicted; identifying the role of nodes, the mainstream model of scientific research collaboration at city level can be explored. Through QAP analysis, the influence of geographic distance on the collaboration intensity is analyzed, and the correlation between the node research ability and the collaboration intensity is revealed; With the help of evolutionary analysis, the development history of collaboration network and the migration of node roles are clarified.
The results show that the hotspots of research on three themes are mainly located in North America, Europe and Asia. Although there are scientific research collaborations between different regions, research collaboration with regional cities is still the main mode; Europe and North America occupy a dominant position in the dissemination of knowledge on various topics, play the role of the initiator of knowledge flow and have a strong driving role in the development of scientific research in other regions; the core periphery analysis divides the urban nodes participating in scientific research collaboration into four classes: core dominant node, core participating node, periphery dominant node and periphery participating node; the geographical distance between nodes has weak negative correlation with the intensity of scientific research collaboration between nodes; the more disparate the node scientific research strength, the greater the probability of scientific research collaboration; the connection of weak scientific research strength nodes has no significant impact on the formation of scientific research collaboration network. The connection between different scientific research strength nodes has strong randomness, but the overall trend is that the connection between the strong node and the weak node has more beneficial effect on the formation and expansion of scientific research cooperation network. The dynamic analysis of the research collaboration networks has revealed the distinctive research directions and fronts of three themes.
Although the above mentioned topics have relatively complete academic representations, the judgments on cross regional scientific research collaboration may require more tests. Cross regional research collaboration networks have shown some sparseness, and the application effects of some community discovery algorithm need to be improved. 5 figs. 11 tabs. 23 refs.
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