• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.559-570
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• 2017

우리나라는 과거부터 현재까지 미래 성장동력 육성을 목표로 정부주도하에 국가 R&D 투자를 점진적으로 늘려왔다. 그 결과, 최근에는 GDP 대비 연구개발비 비중이 세계 최고 수준에 이르렀다. 이렇게 연구개발 예산의 양적인 확대와 함께 연구개발 예산의 효율적 활용은 더욱 중요한 과학기술 분야의 정책적 이슈로 부각되고 있다. 연구개발 예산의 효율적인 집행을 위해서는 R&D 사업의 유사 중복성의 검토가 필수적이지만, 대부분의 유사 중복성 검토는 전문가의 직관적인 판단에 근거하여 이루어져왔다. 하지만, 전문가의 직관에만 의지한 판단은 때로는 불명확하거나 잘못된 결과를 가져올 수도 있다. 따라서, 본 연구에서는 네트워크 분석을 통해 정부 R&D 사업의 유사 중복성을 체계적으로 검토하기 위한 데이터기반의 방법론을 제안하여 전문가의 직관에 의한 유사 중복성 검토를 보완할 수 있는 가능성을 모색하고자 한다. 먼저, 본 연구에서는 정부 R&D사업 유사영역의 전체적인 구조 및 형태와 국가과학기술연구회 소속 25개 정부출연연구기관 R&D사업의 유사영역의 전반적인 형태를 시각화하여 유사영역을 파악하고 직관적인 판단과 선택을 할 수 있는 의사결정 정보를 제공하는데 초점을 두었다. 이를 위해, NTIS의 2015년 데이터를 사용하여 과제 키워드 기반으로 동시단어출현 분석을 수행하였다. 본 분석을 통해 25개 기관의 세부적인 유사연구영역 형태를 제시하였으며, 국내의 과학기술정책적 또는 과학기술학적인 현상들을 시각화하였다. 그 결과, 국내 출연연 R&D사업이 기관별 고유영역이 확고히 보이는 Mode 1적인 형태와 사회경제적인 맥락과 필요 및 유망성을 따르고, 다학제적, 적용중심적이며 과제별로 다양한 과제수행기관들이 과제들을 동시에 수행하는 Mode 2적인 형태가 출연연의 R&D사업 내에 공존하고 있음을 확인하였다.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.63-83
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• 2016

As the usage of information resources produced in various media and forms has been increased, the importance of metadata as a tool of information organization to describe the information resources becomes increasingly crucial. The purposes of this study are to analyze and to demonstrate the intellectual structure in the field of metadata through co-word analysis. The data set was collected from the journals which were registered in the Core collection of Web of Science citation database during the period from January 1, 1998 to July 8, 2016. Among them, the bibliographic data from 727 journals was collected using Topic category search with the query word 'metadata'. From 727 journal articles, 410 journals with author keywords were selected and after data preprocessing, 1,137 author keywords were extracted. Finally, a total of 37 final keywords which had more than 6 frequency were selected for analysis. In order to demonstrate the intellectual structure of metadata field, network analysis was conducted. As a result, 2 domains and 9 clusters were derived, and intellectual relations among keywords from metadata field were visualized, and proposed keywords with high global centrality and local centrality. Six clusters from cluster analysis were shown in the map of multidimensional scaling, and the knowledge structure was proposed based on the correlations among each keywords. The results of this study are expected to help to understand the intellectual structure of metadata field through visualization and to guide directions in new approaches of metadata related studies.

• Korean Journal of Environment and Ecology
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• v.31 no.1
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• pp.30-41
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• 2017

Mammals in Korea have been studied in various fields such as animal science, veterinary medicine, laboratory animal science, ecology, and genetics. As the importance of biodiversity has been emphasized recently, conservation and management of mammals have attracted much public attention. However, in spite of such an increase in scientific research and public interest, it is still difficult to find a report or summary to grasp the trend of mammalian research in Korea. The purpose of this study is to provide the basic data for future plans of the detailed research area and the related policies by grasping the research trends of mammals in Korea. Using text-ming and co-word analysis, we analyzed 392 mammalian research papers published in Korean national journals as of 2015. Our results showed that the number of mammalian research papers published in Korea has gradually increased and that the research target species have also become increasingly diverse. The major research areas identified through text-mining and co-word analysis are (1) evolution/phylogenetics/genetics, (2) environmental science/ecology, (3) embryology/reproductive biology/cell biology, (4) veterinary medicine related to parasites, (5) parasitology related to rodents, (6) bacteriology/virology, (7) anatomy/cell biology/laboratory animal science, (8) veterinary science related to morphology and anatomy, (9) animal science, (10) marine mammalogy, and (11) Chiroptera (bat) research. Environmental science/ecology has been the most active field among the 11 research areas in recent times, and the proportion of research has increased sharply compared to the past. Environmental science/ecology is the core of biodiversity conservation, and as the importance of biodiversity has been emphasized in recent years, researchers' interest in mammal ecology appears to have increased. We expect that the results of this study will be useful for future research plan and related policies on mammals in Korea.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.333-356
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• 2019

Open access has emerged as an alternative to overcome the crisis brought by scholarly communication on commercial publishers. The purpose of this study is to suggest the intellectual structure that reflects the newest research trend in the field of open access, to identify how the subject area is structured by using co-word analysis, and compare and analyze with the existing study. In order to do this, the total number of dataset was 761 papers collected from Web of Science during the period from January 2012 to November 2018 using information science and 2,321 keywords as a noun phase are extracted from titles and abstracts. To analyze the intellectual structure of open access, 13 topic clusters are extracted by network analysis and the keywords with higher centrallity are drawn by visualizing the intellectual relationship. In addition, after clustering analysis, the relationship was analyzed by plotting the result on the multidimensional scaling map. As a result, it is expected that our research helps the research direction of open access for the future.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.65-86
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• 2021

In the foreign language education, learners are an important part of education, however in the Korean language education, the study of learners was insufficient compared to the contents of education, teaching methods and textbooks. Therefore, it is meaningful to analyze how learner research, especially learning strategy research, has been conducted and derive areas that need research for better education. In this study, co-word analysis was conducted on the titles of academic journals and dissertations in order to analyze the learning strategy research in Korean language education. I found it is about "reading" that the most studies related to Korean language learners' learning strategies were conducted and those studies' subjects mostly were 'Chinese international students' and 'marriage-immigrants'. In addition, the results of the subgroup analysis on the research topic show four major subgroups: a group related to 'reading for academic purposes', a group related to 'request, rejection, conversation, etc.', a group related to 'writing', and a group related to 'vocabulary, listening'. This shows that the researchers' major interests in studying Korean learner's strategies are "reading" and "speaking" and their studies have been concentrated in the specific areas. Therefore, it is necessary for researchers to study various functions and subjects in Korean language learner's learning strategies.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.153-171
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• 2017

Many readers tend to read books of a specific author and to expand their reading areas according to the author. This study chose Edgar Allan Poe and analyzed the image of the author using co-recommended authors and books by other readers. The frequencies of co-occurred authors and books were investigated and the relations of authors and books were analyzed with network analysis methods. As a result, genre images of Poe, related authors, and related books are discovered. This study also suggested the methods to identify the image of a author, related author groups, and related books for libraries' reading programs and book curation.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.263-287
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• 2021

Researches on inequality in Korean society has been sporadically conducted in various areas. In this study, research trend related to inequality was analyzed through basic statistical analysis, co-occurrence analysis, and main path analysis using articles related to inequality from Korea citation index. In basic statistical analysis, key authors, journals, and articles are identified. In co-occurrence analysis, income inequality, educational inequality, welfare inequality, and policy on inequality were identified as main topics. Main path analysis showed two research trends after 2004. One was research trend on economic inequality, and the other was on health inequality and social structural inequality.

• Journal of the Korean Society for information Management
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• v.36 no.1
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• pp.117-135
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• 2019

Data journals and data papers have grown and considered an important scholarly practice in the paradigm of open science in the context of data sharing and data reuse. This study investigates a total of 713 data papers published in Scientific Data in terms of author, citation, and subject areas. The findings of the study show that the subject areas of core authors are found as the areas of Biotechnology and Physics. An average number of co-authors is 12 and the patterns of co-authorship are recognized as several closed sub-networks. In terms of citation status, the subject areas of cited publications are highly similar to the areas of data paper authors. However, the citation analysis indicates that there are considerable citations on the journals specialized on methodology. The network with authors' keywords identifies more detailed areas such as marine ecology, cancer, genome, database, and temperature. This result indicates that biology oriented-subjects are primary areas in the journal although Scientific Data is categorized in multidisciplinary science in Web of Science database.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.351-379
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• 2023

The goal of this study is to investigate the domestic and international research trends on metaverse related researches. To achieve this goal, a set of 913 journal articles were collected from KCI (Korea Citation Index), 232 articles from WoS (Web of Science), and 277 articles from WoS-CPCI (Conference Proceeding Citation Index). A descriptive analysis shows the number of researches has been increased radically, and the mostly researched subject areas are interdisciplinary, computer science, and education in KCI, business and economics in WoS, and computer science in WoS-CPCI. The co-occurrence network analysis using author keywords revealed that technology related terms such as virtual reality and augmented reality showed high centrality measures in all of the databases, and the cluster analysis resulted in education and metaverse platform related keywords cluster from KCI, bibliometric analysis related keywords cluster from WoS, and all the metaverse technology related keywords cluster from WoS-CPCI.