• Journal of the Korean Society for Library and Information Science
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• v.28
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• pp.287-299
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• 1995

지금까지 국내 연구자들이 SCI 학술지에 발표한 의학논문의 수가 아주 미미하기 때문에 전 세계 학자들이 발표한 SCI 전체논문 중 한국논문이 차지하는 비중은 지극히 낮은 것으로 보인다. 따라서, 본 연구에서는 1980년부터 1990년 사이에 국내에 있는 의학 관련기관의 연구자들이 SCI 학술지에 발표한 논문이 SCI 전체 논문 중 차지하는 비중(Percentage Contribution from Korea to SCI Database)을 47개 의학분야별로 조사하였다. 또한. 본 연구에서는 "국제적으로 영향력 있는 학술연구 활동실적(i.e., Mainstream Productivity)"이 타 분야보다 뛰어난 한국의 의학분야를 밝히기 위하여. SCI 학술지에 발표된 모든 한국 의학논문 중 한 분야의 논문이 자치하는 몫(Percentage Share)을 SCI 전체 논문 중 그 분야의 논문이 차지하는 몫으로 나누어 그 상대적 크기를 분석함으로써, 우리나라 학자들의 Mainstream Productivity Ratio(R)를 각 분야별로 측정하였다. (R=1은 SCI 의학 학술지에 발표된 모든 논문 중에서 어떤한 분야의 논문이 차지하는 비율만큼 한국에서도 그 분야의 논문이 SCI 학술지에 투고되었다는 의미이다.) 1980년에서 1990년 사이에 한국 학자들에 의하여 SCI에 발표된 의학논문이 SCI 전체 의학논문 중에서 차지하는 비율은 $0.051\%$였다. 각 의학 분야별로는, Biotechnology & applied microbiology의 기여도가 $0.8\%$를 차지하여 한국 최고를 기록하였다. 전 세계 문헌의 $1\%$ 이상을 생산한 의학 분야는 없었다. 전 세계 문헌의 $0.1\%$ 이상을 SCI 학술지에 게재한 의학분야는 47개 중 6개인데, 위에 언급한 Biotechnology & applied microbiology를 제외하면 $0.1\%$에서 $0.2\%$ 사이의 기여도를 보였다. 최다 편수의 논문을 낸 Pharmacology 8l Pharmacy$(0.075\%)$나 두 번째로 논문수가 많았던 Biochemisty & molecular biology$(0.054\%)$의 기여도는 $0.1\%$에 미치지 못하였다. 조사대상이 된 47개 의학분야 중 27개 분야에서 예상된 것보다 상대적으로 적은수(R<1)의 한국 논문이 SCI 학술지에 게재되었다. Mainstream Productivity Ratio가 3보다 큰 분야는 4개인데, 그 중 Biotechnology & applied microbiology는 R=15.63으로 아주 월등한 논문 발표실적을 올렸다. Dermatology & venereal diseases(3.02)와 Engineering, biomedical(3.75)도 국제적으로 영향력 있는 학술지에 논문 발표실적이 뛰어난 의학분야이다.

• Journal of Information Science Theory and Practice
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• v.6 no.1
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• pp.31-44
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• 2018

Purpose: This study aims to examine the research output in the field of nursing at the global level during the last two decades, viz., for the period 1996-2015, with special reference to India. Some of the aspects examined include the research growth in nursing literature during the period of study, leading nursing research countries at the regional and global level, and citation analysis. Scope and Methodology: This study is global in nature, but emphasis has also been laid on India's research contribution in nursing at the global level. Aspects like regional contribution to the nursing research output have also been assessed. This study is purely based on secondary data retrieved from SCImago Journal and Country Rankings. The figures in the study are based on one particular database and are not exhaustive; hence they simply reflect a trend in nursing research at the global level. Findings: During the period 1996 through 2015, a total of 550,490 research articles were published across the world by 212 nation states at an average of 2,596 articles from each individual country. On average, during the period of study, the number of nursing research publications grew at the rate of 7.36% each year. North America has emerged as one of the leading nursing research continents of the world by publishing 218,614 research articles, constituting 39.71% of the global nursing research output. The U.S. and U.K. are the world's two leading nursing research countries, which contributed 193,819 and 61,730 research articles respectively, comprising a 35.21% and 11.21% share of global nursing research output. India and China, apart from being the two fastest growing nursing research countries, have the potential to meet the global human resource demand in the field of nursing, given the skilled and trained human resource both these countries possess in nursing. Social Implication: There is always a need to share working knowledge in some professions and nursing is one of them. There cannot be a better medium than linking practice with theory through the research medium. Metric studies in turn help to get a better idea about the amount of work done in any given field at the national and international level, thus identifying the need thereof to improve upon those areas where there is research lag.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.411-438
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• 2024

This study aims to identify and comprehensively view health information-related research trends using a bibliometric analysis. To this end, 1,193 papers from 2002 to 2023 related to "health information" were collected through the Korea Citation Index (KCI) database and analyzed in diverse aspects: research trends by period, academic fields, intellectual structure, and keyword changes. Results indicated that the number of papers related to health information continued to increase and has been decreasing since 2021. The main academic fields of health information-related research included "biomedical engineering," "preventive medicine/occupational environmental medicine," "law," "nursing," "library and information science," and "interdisciplinary research." Moreover, a co-word analysis was performed to understand the intellectual structure of research related to health information. As a result of applying the parallel nearest neighbor clustering (PNNC) algorithm to identify the structure and cluster of the derived network, four clusters and 17 subgroups belonging to them could be identified, centering on two conglomerates: "medical engineering perspective on health information" and "social science perspective on health information." An inflection point analysis was attempted to track the timing of change in the academic field and keywords, and common changes were observed between 2010 and 2011. Finally, a strategy diagram was derived through the average publication year and word frequency, and high-frequency keywords were presented by dividing them into "promising," "growth," and "mature." Unlike previous studies that mainly focused on content analysis, this study is meaningful in that it viewed the research area related to health information from an integrated perspective using various bibliometric methods.