Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective

Chen, Hongshu; Song, Xinna; Jin, Qianqian; Wang, Ximeng

doi:10.1007/s11192-022-04330-9

Cited by 16 publications

(4 citation statements)

References 71 publications

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“…Co-authorship network analysis provides insights into research collaboration relationships in the field [16]. We identified multiple communities within the network, reflecting collaborative dynamics.…”

Section: Collaborative Network Analysismentioning

confidence: 99%

A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

Zuo,

Huang,

2024

Advances in Transdisciplinary Engineering

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This study aims to gain a deeper understanding of the research dynamics and trends in industry-education integration through a quantitative analysis of the literature in this field. Eight hundred ninety-five relevant articles were retrieved from the Web of Science Core Collection database, and the research focused on the annual publication trends, prominent authors, institutions and countries, highly cited articles, keywords, and collaborative networks. The study found that the field of industry-education integration has undergone three stages. Secondly, researchers from China and the United States ranked first and second in the number of publications in this field. Thirdly, highly cited articles have a particular influence on industry-education integration, but the overall citation count is relatively limited. Fourthly, keywords such as “cooperative education,” “students,” and “performance” are hot topics in this field. Finally, the analysis of collaborative networks revealed the existence of multiple communities, but the overall scale of collaboration is relatively small, and the influence is limited. This study provides profound insights into the academic research in industry-education integration. It helps scholars, policymakers, and practitioners better understand this field’s development trends.

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Section: Collaborative Network Analysismentioning

confidence: 99%

A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

Zuo,

Huang,

2024

Advances in Transdisciplinary Engineering

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“…Some studies rely on indicators such as bibliometric analyses related to scientific publications (i.e., scientometrics) (Zhang et al, 2020b,a;Mayr et al, 2014;Dotsika and Watkins, 2017;Jaewoo and Woonsun, 2014;Rezaeian et al, 2017), patents (Hajikhani and Suominen, 2022;Choi et al, 2022;Chen et al, 2017;Golembiewski et al, 2015;Noh et al, 2016;An et al, 2018;Lee and Lee, 2019;Song et al, 2017), industrymarket indicators (job openings, trade registers, networks of authors and citations), or a combination of these (An et al, 2022;Xi et al, 2022;Ali et al, 2022;Daim et al, 2012;Lee et al, 2010). To analyze this data, text-mining methods (Chen et al, 2017;Choi and Hwang, 2014;Hao et al, 2014;Antons et al, 2020) and network analysis (Chen et al, 2022;Zhang et al, 2022;Li et al, 2019b;Mikheev, 2020;Feng et al, 2022;Hong et al, 2021) are particularly prolific. For instance, Guo, Wang, Tian, and Xian (2019) analyze 1666 scientific publications to detect trends in network technologies and information systems (Guo et al, 2019).…”

Section: Technological Change and Security Development Processesmentioning

confidence: 99%

Measuring security development in information technologies: A scientometric framework using arXiv e-prints

David

Maréchal

Lacube³

et al. 2023

Technological Forecasting and Social Change

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“…Analysis of the acknowledged individuals provides insight into informal scientific collaboration (Rose & Georg, 2021;Kusumegi & Sano, 2022). Acknowledged universities and corporations reveal interactions and knowledge exchange between industry and universities (Chen et al, 2022). Entities from the miscellaneous category include other information like project names, which could uncover international scientific collaborations.…”

Section: Introductionmentioning

confidence: 99%

Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

Smirnova,

Mayr

2023

Scientometrics

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Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgment text in scientific papers. We trained and implemented a named entity recognition (NER) task using the flair NLP framework. The training was conducted using three default Flair NER models with four differently-sized corpora and different versions of the flair NLP framework. The Flair Embeddings model trained on the medium corpus with the latest FLAIR version showed the best accuracy of 0.79. Expanding the size of a training corpus from very small to medium size massively increased the accuracy of all training algorithms, but further expansion of the training corpus did not bring further improvement. Moreover, the performance of the model slightly deteriorated. Our model is able to recognize six entity types: funding agency, grant number, individuals, university, corporation, and miscellaneous. The model works more precisely for some entity types than for others; thus, individuals and grant numbers showed a very good F1-Score over 0.9. Most of the previous works on acknowledgment analysis were limited by the manual evaluation of data and therefore by the amount of processed data. This model can be applied for the comprehensive analysis of acknowledgment texts and may potentially make a great contribution to the field of automated acknowledgment analysis.

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Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective

Cited by 16 publications

References 71 publications

A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

Measuring security development in information technologies: A scientometric framework using arXiv e-prints

Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

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