Tracking the dynamics of co-word networks for emerging topic identification

Huang, Lu; Chen, Xiang; Ni, Xingxing; Liu, Jiarun; Cao, Xiaoli; Wang, Changtian

doi:10.1016/j.techfore.2021.120944

Cited by 25 publications

(2 citation statements)

References 56 publications

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“…We previously demonstrated that the retrospective study with co-word analysis for vast datasets was still effective to comprehend the mechanisms that generate emerging topics [ 4 , 50 , 51 ]. Our in-house scientometrical method for quantifying past and current ETs in life science and medicine via PubMed, currently the main repository for these types of articles [ 5 ], was listed as a representative method for co-word analysis by Rotolo et al [ 2 ].…”

Section: Conceptual Framework; Emerging Abstract As Elements Of Emerg...mentioning

confidence: 99%

The effectiveness of Japanese public funding to generate emerging topics in life science and medicine

Ohniwa,

Takeyasu,

Hibino

2023

PLoS ONE

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Understanding the effectiveness of public funds to generate emerging topics will assist policy makers in promoting innovation. In the present study, we aim to clarify the effectiveness of grants to generate emerging topics in life sciences and medicine since 1991 with regard to Japanese researcher productivity and grants from the Japan Society for the Promotion of Science. To clarify how large grant amounts and which categories are more effective in generating emerging topics from both the PI and investment perspectives, we analyzed awarded PI publications containing emerging keywords (EKs; the elements of emerging topics) before and after funding. Our results demonstrated that, in terms of grant amounts, while PIs tended to generate more EKs with larger grants, the most effective investment from the perspective of investor side was found in the smallest amount range for each PI (less than 5 million JPY /year). Second, in terms of grant categories, we found that grant categories providing smaller amounts for diverse researchers without excellent past performance records were more effective from the investment perspective to generate EK. Our results suggest that offering smaller, widely dispersed grants rather than large, concentrated grants is more effective in promoting the generation of emerging topics in life science and medicine.

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Section: Conceptual Framework; Emerging Abstract As Elements Of Emerg...mentioning

confidence: 99%

The effectiveness of Japanese public funding to generate emerging topics in life science and medicine

Ohniwa,

Takeyasu,

Hibino

2023

PLoS ONE

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“…In the late 1990s, with the improvement of computer technology, AI technology also achieved rapid development. The potential benefit of AI is the automation of cognitive tasks, including classification and perception, and its application has penetrated many fields, such as mathematics, physics, medicine, and financial trade [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Dong

Meng

2022

Sustainability

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Artificial intelligence (AI), as a rapidly developing interdisciplinary field, is a key driver of future economic development. The Yangtze River Delta (YRD) is one of the most significant economic regions of China, which also has a leading role in the AI industry. In this study, based on the patent cooperation data of YRD in the past decade, we focus on studying the collaborative innovation relationship in the AI field of the YRD from the perspective of complex networks. In order to investigate the interprovincial, intra-city and inter-city collaborative innovation relationships, we construct the Yangtze River Delta AI collaborative innovation (YRD-AICI) network. Subsequently, to analyze the development status and collaborative innovation relationship of innovation bodies in the AI field of YRD, we construct the Yangtze River Delta AI patent cooperation (YRD-AIPC) network. Next, the basic characteristics and spatio-temporal evolution of these two networks are explored, and the research results are presented that: (1) Shanghai, Jiangsu Province, and Zhejiang Province have obvious leading advantages in the AI field of the YRD, and the development gap between cities is significant; (2) the pioneering innovation bodies in the AI industry of the YRD are identified using centrality measures, and their cooperative innovation relationship is revealed; (3) based on link prediction methods, future partnerships between cities and innovation bodies are predicted to provide the future development trend of the YRD. The results provide theoretical support for exploring the cooperation mechanism of collaborative innovation in the AI field of YRD and inspire future development planning.

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