跨地域科研协作模式分析
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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。
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.
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.
引文
[1] Pennington D D.Cross-disciplinary collaboration and learning[J].Ecology and Society,2008,13(2):8.
[2] Cummings J N,Kiesler S.Collaborative research across disciplinary and organizational boundaries[J].Social Studies of Science,2005,35(5):703-722.
[3] Stvilia B,Hinnant C C,Schindler K,et al.Composition of scientific teams and publication productivity at a national science lab[J].Journal of the American Society for Information Science and Technology,2011,62(2):270-283.
[4] Wuchty S,Jones B F,Uzzi B.The increasing dominance of teams in production of knowledge[J].Science,2007,316(5827):1036-1039.
[5] B?rner K,Dall' Asta L,Ke W,et al.Studying the emerging global brain:Analyzing and visualizing the impact of co-authorship teams [J].Complexity,2005,10(4):57-67.
[6] 李纲,叶光辉.基于模糊匹配的跨学科专家团队发现算法研究[J].情报学报,2014,33(1):68-76.(Li Gang,Ye Guanghui.Research on interdisciplinary team of experts discovery algorithm based on the fuzzy matching[J].Journal of the China Society for Scientific and Technical Information,2014,33(1):68-76.)
[7] 王飞跃.SciTS:21世纪科技合作的灯塔?[EB/OL].[2014-03-20].http://www.qstheory.cn/kj/kj/201104/t20110413_76548.htm.(Wang Feiyue.SciTS:a beacon for 21st century technology cooperation?[EB/OL].[2014-03-20].http://www.qstheory.cn/kj/kj/201104/t20110413_76548.htm.)
[8] Boschma R A.Proximity and innovation:a critical assessment[J].Regional Studies,2005,39(1):61-74.
[9] 刘志迎,单洁含.技术距离、地理距离与大学—企业协同创新效应——基于联合专利数据的研究[J].科学学研究,2013,31(9):1331-1337.(Liu Zhiying,Shan Jiehan.The impact of technological distance and geographical distance on university-industry collaborative innovation:an analysis of joint-patent[J].Studies in Science of Science,2013,31(9):1331-1337.)
[10] Hong W,Su Y S.The effect of institutional proximity in non-local university-industry collaborations:an analysis based on Chinese patent data[J].Research Policy,2013,42(2):454-464.
[11] 陈光华,王烨,杨国梁.地理距离阻碍跨区域产学研合作绩效了吗?[J].科学学研究,2015,33(1):76-82.(Chen Guanghua,Wang Ye,Yang Guoliang.Geographical distance and non—local university—industry collaborations performance [J].Studies in Science of Science,2015,33(1):76-82.)
[12] Petruzzelli A M.The impact of technological relatedness,priorities,and geographical distance on university-industry collaborations:A joint-patent analysis[J].Technovation,2011,31(7):309-319.
[13] 吴素春,聂鸣.以创新型城市为主导的区域科研合作网络研究——以湖北省论文合著数据为例[J].情报杂志,2013(8):125-131.(Wu Suchun,Nie Ming.Regional science collaboration network dominated by innovative city:a research based on the data of co-authored papers from Hubei province [J].Journal of Intelligence,2013(8):125-131.)
[14] 李纲,魏旻予,毛进,等.基于合著网络的城市间科研合作特征研究——以计算机领域核心期刊为例[J].情报学报,2014(10):1067-1076.(Li Gang,Wei Minyu,Mao Jin,et al.A study of inter city scientific collaborations based on co-author network:a case study of computer science core journals [J].Journal of the China Society for Scientific and Technical Information,2014(10):1067-1076.)
[15] 刘凤朝,姜滨滨.中国区域科研合作网络结构对绩效作用效果分析——以燃料电池领域为例[J].科学学与科学技术管理,2012,33(1):109-115.(Liu Fengchao,Jiang Binbin.Analysis of the effect of network structure on innovation performance:based on China's regional S&T collaboration network in fuel cell[J].Science of Science and Management of S.& T.,2012,33(1):109-115.)
[16] 张玉涛,李雷明子,王继民,等.数据挖掘领域的科研合作网络分析[J].图书情报工作,2012,56(6):117-122.(Zhang Yutao,Li Leimingzi,Wang Jimin,et al.Research cooperation networks in data mining[J].Library and Information Service,2012,56(6):117-122.)
[17] 司尚奇,冯锋.我国跨区域技术转移联盟研究——基于38个城市合作网络分析[J].科学学研究,2010,28(8):1165-1170.(Si Shangqi,Feng Feng.Research on China's inter-regional technology transfer alliance-based on cooperative network of 38 cities [J].Studies in Science of Science,2010,28(8):1165-1170.)
[18] 刘芳芳,冯锋.产学研跨区域合作现状及特征研究——基于社会网络视角[J].科学学与科学技术管理,2015,36(8):83-92.(Liu Fangfang,Feng Feng.The research on industry-university-research cross-regional cooperation characteristics and current situation:based on social network[J].Science of Science and Management of S.& T.,2015,36(8):83-92.)
[19] 漆永乐.组织、空间情境与科研合作网络关系研究[D].杭州:浙江理工大学,2015.(Qi Yongle.Relationship between organizational,spatial context and scientific cooperation network [D].Hangzhou:Zhejiang Sci-Tech University,2015.)
[20] 夏丽娟,谢富纪,付丙海.邻近性视角下的跨区域产学协同创新网络及影响因素分析[J].管理学报,2017(12):1795-1803.(Xia Lijuan,Xie Fuji,Fu Binghai.The network evolution of China cross region industry-university collaborative innovation and the analysis of influencing factors:based on the perspective of proximity[J].Chinese Journal of Management,2017(12):1795-1803.)
[21] 李健.我国协作研发网络的演化过程与结构特征研究——基于R&D国际化视角[J].科学学与科学技术管理,2013(9):27-36.(Li Jian.A study on the evolution process and structure characteristics of collaboration R&D network in China in the view of R&D internationalization[J].Science of Science and Management of S.& T.,2013(9):27-36.)
[22] 朱桂龙,程强.我国产学研成果转化政策主体合作网络演化研究[J].科学学与科学技术管理,2014(7):40-48.(Zhu Guilong,Cheng Qiang.The evolution research of the main body cooperation network under the achievements transformation policy of U-I collaboration in China[J].Science of Science and Management of S.& T.,2014(7):40-48.)
[23] 刘军.QAP:测量“关系”之间关系的一种方法[J].社会,2007(4):164-174,209.(Liu Jun.QAP:a unique method of measuring relationships in relational data [J].Society,2007(4):164-174,209.)叶光辉华中师范大学信息管理学院副教授。湖北武汉 430079。夏立新华中师范大学信息管理学院教授,博士生导师。湖北武汉 430079。
(1)http://www.vivoweb.org
(2)http://www.iplantcollaborative.org
(3)http://www.iiia.csic.es/ca/project/liquidpub
[2] Cummings J N,Kiesler S.Collaborative research across disciplinary and organizational boundaries[J].Social Studies of Science,2005,35(5):703-722.
[3] Stvilia B,Hinnant C C,Schindler K,et al.Composition of scientific teams and publication productivity at a national science lab[J].Journal of the American Society for Information Science and Technology,2011,62(2):270-283.
[4] Wuchty S,Jones B F,Uzzi B.The increasing dominance of teams in production of knowledge[J].Science,2007,316(5827):1036-1039.
[5] B?rner K,Dall' Asta L,Ke W,et al.Studying the emerging global brain:Analyzing and visualizing the impact of co-authorship teams [J].Complexity,2005,10(4):57-67.
[6] 李纲,叶光辉.基于模糊匹配的跨学科专家团队发现算法研究[J].情报学报,2014,33(1):68-76.(Li Gang,Ye Guanghui.Research on interdisciplinary team of experts discovery algorithm based on the fuzzy matching[J].Journal of the China Society for Scientific and Technical Information,2014,33(1):68-76.)
[7] 王飞跃.SciTS:21世纪科技合作的灯塔?[EB/OL].[2014-03-20].http://www.qstheory.cn/kj/kj/201104/t20110413_76548.htm.(Wang Feiyue.SciTS:a beacon for 21st century technology cooperation?[EB/OL].[2014-03-20].http://www.qstheory.cn/kj/kj/201104/t20110413_76548.htm.)
[8] Boschma R A.Proximity and innovation:a critical assessment[J].Regional Studies,2005,39(1):61-74.
[9] 刘志迎,单洁含.技术距离、地理距离与大学—企业协同创新效应——基于联合专利数据的研究[J].科学学研究,2013,31(9):1331-1337.(Liu Zhiying,Shan Jiehan.The impact of technological distance and geographical distance on university-industry collaborative innovation:an analysis of joint-patent[J].Studies in Science of Science,2013,31(9):1331-1337.)
[10] Hong W,Su Y S.The effect of institutional proximity in non-local university-industry collaborations:an analysis based on Chinese patent data[J].Research Policy,2013,42(2):454-464.
[11] 陈光华,王烨,杨国梁.地理距离阻碍跨区域产学研合作绩效了吗?[J].科学学研究,2015,33(1):76-82.(Chen Guanghua,Wang Ye,Yang Guoliang.Geographical distance and non—local university—industry collaborations performance [J].Studies in Science of Science,2015,33(1):76-82.)
[12] Petruzzelli A M.The impact of technological relatedness,priorities,and geographical distance on university-industry collaborations:A joint-patent analysis[J].Technovation,2011,31(7):309-319.
[13] 吴素春,聂鸣.以创新型城市为主导的区域科研合作网络研究——以湖北省论文合著数据为例[J].情报杂志,2013(8):125-131.(Wu Suchun,Nie Ming.Regional science collaboration network dominated by innovative city:a research based on the data of co-authored papers from Hubei province [J].Journal of Intelligence,2013(8):125-131.)
[14] 李纲,魏旻予,毛进,等.基于合著网络的城市间科研合作特征研究——以计算机领域核心期刊为例[J].情报学报,2014(10):1067-1076.(Li Gang,Wei Minyu,Mao Jin,et al.A study of inter city scientific collaborations based on co-author network:a case study of computer science core journals [J].Journal of the China Society for Scientific and Technical Information,2014(10):1067-1076.)
[15] 刘凤朝,姜滨滨.中国区域科研合作网络结构对绩效作用效果分析——以燃料电池领域为例[J].科学学与科学技术管理,2012,33(1):109-115.(Liu Fengchao,Jiang Binbin.Analysis of the effect of network structure on innovation performance:based on China's regional S&T collaboration network in fuel cell[J].Science of Science and Management of S.& T.,2012,33(1):109-115.)
[16] 张玉涛,李雷明子,王继民,等.数据挖掘领域的科研合作网络分析[J].图书情报工作,2012,56(6):117-122.(Zhang Yutao,Li Leimingzi,Wang Jimin,et al.Research cooperation networks in data mining[J].Library and Information Service,2012,56(6):117-122.)
[17] 司尚奇,冯锋.我国跨区域技术转移联盟研究——基于38个城市合作网络分析[J].科学学研究,2010,28(8):1165-1170.(Si Shangqi,Feng Feng.Research on China's inter-regional technology transfer alliance-based on cooperative network of 38 cities [J].Studies in Science of Science,2010,28(8):1165-1170.)
[18] 刘芳芳,冯锋.产学研跨区域合作现状及特征研究——基于社会网络视角[J].科学学与科学技术管理,2015,36(8):83-92.(Liu Fangfang,Feng Feng.The research on industry-university-research cross-regional cooperation characteristics and current situation:based on social network[J].Science of Science and Management of S.& T.,2015,36(8):83-92.)
[19] 漆永乐.组织、空间情境与科研合作网络关系研究[D].杭州:浙江理工大学,2015.(Qi Yongle.Relationship between organizational,spatial context and scientific cooperation network [D].Hangzhou:Zhejiang Sci-Tech University,2015.)
[20] 夏丽娟,谢富纪,付丙海.邻近性视角下的跨区域产学协同创新网络及影响因素分析[J].管理学报,2017(12):1795-1803.(Xia Lijuan,Xie Fuji,Fu Binghai.The network evolution of China cross region industry-university collaborative innovation and the analysis of influencing factors:based on the perspective of proximity[J].Chinese Journal of Management,2017(12):1795-1803.)
[21] 李健.我国协作研发网络的演化过程与结构特征研究——基于R&D国际化视角[J].科学学与科学技术管理,2013(9):27-36.(Li Jian.A study on the evolution process and structure characteristics of collaboration R&D network in China in the view of R&D internationalization[J].Science of Science and Management of S.& T.,2013(9):27-36.)
[22] 朱桂龙,程强.我国产学研成果转化政策主体合作网络演化研究[J].科学学与科学技术管理,2014(7):40-48.(Zhu Guilong,Cheng Qiang.The evolution research of the main body cooperation network under the achievements transformation policy of U-I collaboration in China[J].Science of Science and Management of S.& T.,2014(7):40-48.)
[23] 刘军.QAP:测量“关系”之间关系的一种方法[J].社会,2007(4):164-174,209.(Liu Jun.QAP:a unique method of measuring relationships in relational data [J].Society,2007(4):164-174,209.)叶光辉华中师范大学信息管理学院副教授。湖北武汉 430079。夏立新华中师范大学信息管理学院教授,博士生导师。湖北武汉 430079。
(1)http://www.vivoweb.org
(2)http://www.iplantcollaborative.org
(3)http://www.iiia.csic.es/ca/project/liquidpub