Do research institutes benefit from their network positions in research collaboration networks with industries or/and universities?

Technovation ◽  
2020 ◽  
Vol 94-95 ◽  
pp. 102002 ◽  
Author(s):  
Kaihua Chen ◽  
Yi Zhang ◽  
Guilong Zhu ◽  
Rongping Mu
2014 ◽  
Vol 52 ◽  
pp. 130-140 ◽  
Author(s):  
Jiang Bian ◽  
Mengjun Xie ◽  
Umit Topaloglu ◽  
Teresa Hudson ◽  
Hari Eswaran ◽  
...  

2015 ◽  
Author(s):  
Vincent Schubert R Malbas

Collaboration forms an integral aspect of global research endeavors, where co-authorship derived from bibliographic records provides the building block for mapping research collaboration networks. Bibliometric techniques and social network analysis tools were applied to measure the scope and depth of collaboration in biomedical research in Southeast Asia during the period 2005-2009. In particular, centrality scores and draw network maps were calculated for both country and institutional levels of aggregation. In the field of biomedical research, Thailand and Singapore are the most productive and collaborative countries in Southeast Asia during the period studied. Using network analysis, there was strong correlation of research productivity by a country or institution with the number of collaboration and its group influence, and weak correlation with maximal data flow within the research network. There were specific clusters of connected institutions in subnetworks for neoplasm, diabetes, and tuberculosis research. Given the observed frequency of regional collaboration in Southeast Asia, in comparison to foreign collaboration, it is argued that increasing the number of collaborations within Southeast Asia will help advance the region’s efforts on domestic and regional health issues.


2015 ◽  
Vol 53 (2(106)) ◽  
pp. 7-17
Author(s):  
Mieczysław Muraszkiewicz ◽  
Henryk Rybiński ◽  
Piotr Szczepański

PURPOSE: The purpose of the study is to outline a practical model for discovering research collaboration networks on the basis of data and information stored in scientific digital libraries and repositories. The discovered relationships between researchers, projects, scientific institutions and other scientific entities are used for identifying collaboration networks of researchers and research institutions interested in or working on a given subject. Afterwards, such networks can be subject to various types of network analysis in order to get in-depth knowledge on the networks and their components. APPROACH/METHODS: The method adopted in the study is twofold, that is: (i) it takes into consideration the way of discovering collaboration networks by means of simple tools that have been implemented within the ΩΨR system developed at Warsaw University of Technology; (ii) it develops an outline of a formal model of research collaboration networks that takes into account the specificity of scientific digital libraries and repositories and includes the network analysis techniques for discovering knowledge residing/hidden in the networks. RESULTS AND CONCLUSIONS: The outcome of the research is the outline of a formal model of research collaboration networks that includes: (i) a discovery mechanism for identifying thematically related scientists, projects, research institutions, and other scientific entities; and (ii) a set of network analysis methods for getting in-depth knowledge residing in the networks. The model is implementable and scalable in terms of functionality it offers and the network analysis techniques it includes. The model is founded on a solid ground, which is the ΩΨR system functionality to discover simple collaboration networks, and it is being used for enhancing the ΩΨR system. ORIGINALITY/VALUE: The value of the research is the outline of a general research collaboration networks model that: (i) can help identify, build, and analyse research communities, and thereby increases the scope, value and impact of scientific endeavours on science and society; (ii) is used for enhancing the ΩΨR system.


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