• Title/Summary/Keyword: Author co-citation

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Characteristics of Korean Researchers through Bibliometric Analysis of Papers Published in International LIS Journals (문헌정보학 분야 국제 학술지 논문 계량분석을 통한 국내 연구자 특성 연구)

  • Lee, Jong-Wook;Bak, Hye-Rin
    • Journal of Korean Library and Information Science Society
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    • v.47 no.1
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    • pp.217-242
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    • 2016
  • To investigate the characteristics of Korean researchers who have published journal articles in the international Library and Information Science (LIS) journals, we analyzed the types of institutions of the authors and their disciplines by publication year and journal type. Specifically, we analyzed 819 pieces of author information from 384 articles published between 2005 and 2014 in 31 journals using the existing categories, allowing comparisons of research trends between past and current and between Korean and international researchers. In addition, we used a co-authorship credit allocation method to calculate the credits of individual authors in an accurate manner, and found that 342.6 of 384 papers were contributed by solely Korean researchers. We demonstrated that the internationality and quality of Korean LIS research has increased during this time. In particular, authors who were affiliated with universities took the lead in publishing papers, and we presented that the nature of research might be related to the type of author affiliation. Based on the disciplinary backgrounds of the researchers, we also suggest that LIS research is associated with such disciplines, computer science, management, and communications.

Using Text Mining for the Analysis of Research Trends Related to Laws Under the Ministry of Oceans and Fisheries (텍스트 마이닝을 활용한 해양수산부 법률 관련 연구동향 분석연구)

  • Hwang, Kyu Won;Lee, Moon Suk;Yun, So Ra
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.28 no.4
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    • pp.549-566
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    • 2022
  • Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

A Study on the Altmetrics of the Papers of Library and Information Science Researchers Published in International Journals (국제 학술지에 발표된 문헌정보학 연구자 논문의 알트메트릭스에 관한 연구)

  • Jane Cho
    • Journal of Korean Library and Information Science Society
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    • v.53 no.4
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    • pp.143-162
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    • 2022
  • Altmetrics is an alternative impact evaluation index that evaluates the social interest in the research performance of individuals or institutions in universities, research institutions, and research fund support institutions. This study empirically analyzed what kind of attention a papers of domestic library and information science researchers published in an international academic journal was receiving in the international community using Altmetric explorer. As a result of the analysis, 230 papers were tracked. The average Altmetric Attention Score (AAS) was 6.63, but there were 2 papers that received overwhelming attention (over 170 points) as they were mentioned in news report and Twitter. Second, there was a tendency for high AAS to appear in cases where a domestic researcher participated as a co-author and the main author belonged to an overseas institution, and in the case where the research funds were supported by foreign government agencies. In addition to the field of the library information science or information system, the papers classified as the field of public health service and education showed high AAS, and it was confirmed that these papers were published in the journals of various fields such as life science. Finally, it was confirmed that there was a weak correlation of r =0.25 between the AAS and the number of citations of the analyzed paper, but a strong correlation of r =0.68 between the number of Mendeley readers and the number of citations.

Analyzing Research Trends of Domestic Artificial Intelligence Research Using Network Analysis and Dynamic Topic Modelling (네트워크 분석과 동적 토픽모델링을 활용한 국내 인공지능 분야 연구동향 분석)

  • Jung, Woojin;Oh, Chanhee;Zhu, Yongjun
    • Journal of the Korean Society for Library and Information Science
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    • v.55 no.4
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    • pp.141-157
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    • 2021
  • In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.

An Overview of Research Trends in 'Aesthetic Science-Education': Focused on Bibliographic Analysis Using Bibliometrix Package in the R Program (미적 과학교육 연구 동향 분석 -R 프로그램의 Bibliometrix 패키지를 활용한 상세 서지분석을 중심으로-)

  • Kyungsuk, Bae;Jun-Young, Oh;Jaehyeok, Choi;Yejin, Moon;Yeon-A, Son
    • Journal of The Korean Association For Science Education
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    • v.42 no.5
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    • pp.543-555
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    • 2022
  • This study aims to identify the trends in 'Aesthetic Science-Education' research through bibliographic analysis and provide future implications for research in this field. To this research, 100 studies were extracted using the search function of the Web of Science provided by Clarivate Analytics. Detailed bibliometrics was analyzed using the Bibliometrix package of the R program. As a result of the analysis, the average number of papers increased from 1993 to 2022, and academic journals in which related papers were published were evenly distributed locally. As a result of keyword analysis, papers with top citations, author collaboration networks, and literature co-citation networks, Aesthetic Science-Education could be classified as inducing aesthetic experience by integrating art in science education, and categories using 'formalist aesthetic' and 'emotional response'. The implications derived from this study are as follows: first, the aesthetic aspects of science should be actively studied to emphasize 'Agency' and 'Active Learning' in future science education. Second, it is necessary to develop a new approach to science education by further utilizing the 'formalist aesthetic' of science in science education. Third, the aesthetic aspect of science can change the perception of the Nature of Science of teachers, pre-service science teachers, and students. Fourth, it is necessary to discover implications for utilizing aesthetic aspects in science education through extensive research on the aesthetic aspects of science for teachers, students, and pre-service teachers. This study is meaningful because it provides an overview of the 'Aesthetic Science-Education' research to realize the above implications.