Scientific collaboration in indigenous knowledge in context: Insights from publication and co-publication network analysis

Fung, Hon-Ngen; Wong, Chan‐Yuan

doi:10.1016/j.techfore.2017.01.009

Cited by 20 publications

(15 citation statements)

References 33 publications

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“…Social network is a relatively stable system of relations, which is constituted by a group of interrelated and interactional entities Klemmer et al (2002). As a subcategory of social network, the scientific collaboration network is of importance and has been widely discussed in the literature, Fung and Wong (2017) examined publication output and co-publication network structures to investigate the comparative advantage and composition of the research networks in the various economies. As the direct case resulting in coauthored publications, co-authorship has been widely adopted as the best indicator of research collaborations.…”

Section: Collaboration Network Analysismentioning

confidence: 99%

Investigating collaboration in ubiquitous computing research

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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With the rapid development of mobile internet and smart devices, ubiquitous computing is experiencing a huge change. As direct progress reflection of the field, collaboration among scholars has been increasingly important and facilitating a few emerging applications, e.g., collaborator recommendation. In this paper, to further figure out how the contributors collaborate with each other, we explore two representative conferences in ubiquitous computing research field, UbiComp and Pervasive (merged with UbiComp in 2013). Papers collected from the two conferences are data set to examine collaboration from three perspectives: (1) Collaboration overview: we construct collaboration network and conduct analysis from aspects of author, institution, and nation respectively, and shed light on progress of the field. (2) Collaboration Evolution: by segmenting the whole time period of collaboration into two distinct stages, we identify different kinds of collaboration patterns in terms of collaborator and research topic. (3) Collaboration Quality: we propose a new method to quantify the actual quality of each kind of collaboration patterns. Furthermore, we propose some suggestions to development of ubiquitous computing according to our findings.

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Section: Collaboration Network Analysismentioning

confidence: 99%

Investigating collaboration in ubiquitous computing research

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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“…Team collaboration has been discussed in many disciplines, such as ecology [ 7 ] indigenous knowledge [ 8 ] and Zika virus [ 9 ]. Geographic distance is widely considered as one of the major factors affecting team collaboration.…”

Section: Introductionmentioning

confidence: 99%

Understanding Team Collaboration in Artificial Intelligence from the Perspective of Geographic Distance

Tang

Ding

et al. 2021

Diversity, Divergence, Dialogue

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This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance. We obtained 1,584,175 AI related publications during 1950–2019 from the Microsoft Academic Graph. Three latitude-and-longitude-based indicators were employed to quantify the geographic distance of collaborations in AI over time at domestic and international levels. The results show team collaborations in AI has been more popular in the field over time with around 42,000 (38.4%) multiple-affiliation AI publications in 2019. The changes in geographic distances of team collaborations indicate the increase of breadth and density for both domestic and international collaborations in AI over time. In addition, the United States produced the largest number of single-country and internationally collaborated AI publications, and China has played an important role in international collaborations in AI after 2010.

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“…Network visualization methods were used among others to assess the collaboration of scientific institutes in a geographical area [17]; to find potential collaborators or to support interdisciplinary innovation [18,19]. The most commonly used source of collaboration information is the co-authorship of publications.…”

Section: Introductionmentioning

confidence: 99%

A computer framework for analysing networks of locations and topics in a clinical trials database

Tóth¹,

Pollner²,

Palla³

et al. 2019

Clin Med Invest

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In this article, we aim to extract information from a large online clinical research database; demonstrate the pitfalls of data quality; and create a computer framework for the visualization of the results. Data were downloaded from the clinicaltrials.gov website and loaded into a relational database after cleaning. Network analysis methods were applied to find hierarchical relations and inspect temporal connections in the database. A web-based, open source application "h-Vis" (Hierarchical Data Visualization) was developed to visualize the results. Our results highlight some data quality issues and show what steps were necessary for the cleaning and analysis of the data. Despite the lack of uniform data quality, we were able to extract information from the database and present some statistical overview and visualization examples. Institution-level examination was impossible due to a lot of missing or inaccurate data. City-level connections between the research sites were extracted. Using the visualization tool, it is possible to identify cities with experience in certain disease or medical intervention, or to extract collaboration between medical centres. The extracted information can be useful for researchers to see the latest trends in medical research reaching clinical study phase. In addition, they provide a good orientation for the general audience (e.g. decision makers, investors) about the focus of clinical problems investigated in certain countries or cities. Our custom, publicly available framework might enable to perform further analysis on the data.

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Scientific collaboration in indigenous knowledge in context: Insights from publication and co-publication network analysis

Cited by 20 publications

References 33 publications

Investigating collaboration in ubiquitous computing research

Investigating collaboration in ubiquitous computing research

Understanding Team Collaboration in Artificial Intelligence from the Perspective of Geographic Distance

A computer framework for analysing networks of locations and topics in a clinical trials database

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