• Journal of Korean Public Health Nursing
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• v.25 no.2
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• pp.153-173
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• 2011

Purpose: This study was conducted to analyze trends in research papers published in the Journal of Korean Public Health Nursing, and to compare and contrast similarities and differences of papers before and after listing in the Korea Citation Index (KCI) in 2007. Methods: A descriptive study was conducted with 266 published studies during the 10-year period. The criteria for analysis included types of research, characteristics of researchers and participants, designs, data collection methods and study instruments, ethical considerations, data analysis, and keywords. Results: Studies conducted with grants constituted 23.7%, and students (32.3%) and general populations (25.2%) comprised the largest proportion of the study participants. The majority of the papers were quantitative research (93.2%), and self-reported methods (63.1%) were most frequently utilized. Seventy percent of the studies obtained verbal consent from the participants. Among the study instruments, psychosocial indicators (41.1%) were most frequently employed. The trends indicated that grant studies, students and general populations as study participants, and experimental studies increased, and use of written consent increased after KCI registration. Conclusions: The results could be used to understand the context of scientific research and to improve the quality of the research papers published in the Journal of Korean Public Health Nursing.

• Journal of Oriental Neuropsychiatry
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• v.22 no.3
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• pp.101-113
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• 2011

Objectives : Trend of R&D of herbal medicine for dementia treatment was examined based on the quantitative information analysis for establishing the national strategy of research on dementia treatment with oriental medicine. Methods : Definition was made to clarify the technology for development of herbal medicine for dementia treatment. Based on the initial keyword provided by experts in the field, queries were compounded to conduct search in the search engines of WoS and DWPI. The raw data (papers or patents) extracted from the initial search were examined by expert-review before objects of analysis were determined. Then, the accumulated data was analyzed in terms of year, country and organization, which led to examination of the trend of R&D. And the research performance evaluation for dementia treatment technologies was also made in terms of country, organization and researcher based on the forward citation analysis. The international cooperation intensity was examined on the basis of analysis of network by researcher before analysis results were put together to select lead researchers. Results : According to the quantitative information analysis of 1,330 articles that were selected as analysis objects, the number of papers on natural products research for dementia treatment has increased by around 4.6 times in recent five years. This indicates that the intensive studies have been underway recently. It was found to be the US that had the highest level in research filed of herbal medicine for dementia treatment and the highest capacity of international cooperation for that purpose. On the contrary, Korea had the share of papers at 5.1%, the number of countries in cooperation research at 8, and the article quality index at 0.40, showing that the qualitative level was insufficient, compared to the quantitative outcome. In particular, Korea was found to have no intensity of international cooperation among researchers. In case of patent, the results of information analysis of 305 patents selected as analysis objects demonstrated that China had the highest share while Korea had the very low frequency of patent application quantitatively. Conclusions : In this study, the research to develop herbal medicine for dementia treatment has recently drawn much attention that has spread around the globe. Therefore, these results suggest establishing the strategy to develop technology for dementia treatment with oriental medicine in the future based on quantitative information analysis.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.173-192
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• 2021

The purpose of this study is to analyze the previous research on geological fieldwork from 2000 to 2020, examine the tasks that have been focused on, and suggest directions and implications for future geological fieldwork research. The data was conducted for the thesis searched on ScienceON and RISS in relation to geological fieldwork and journals listed in the Korean Citation Index(KCI), and the study title was analyzed using the semantic network analysis. For analysis, the data that had been pre-processed was visualized as a network by semantic network analysis, and frequency and centrality were analyzed. The centrality analysis was based on degree centrality and eigenvector centrality, and all analyzes were performed by dividing the entire study period into four periods: 2000-2005, 2006-2010, 2011-2015, and 2016-2020. As a result, research on geological fieldwork focused more on the development of geological field courses, and in particular, jeju island was actively discussed as a learning site. Also, the study was conducted on students rather than teachers, and among them, high school students showed high frequency and centrality. In addition, it can be seen that studies on the educational effect of geological fieldwork were discussed, either in connection with programs such as STEAM, free-semester program, or indirect geological fieldwork methods such as web, flash panorama, and 3D. This study is meaningful in that it suggests the direction of future research by looking back on the research on geological fieldwork that has been done so far.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.125-139
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• 2021

This study identifies the trend of Korean accounting researches on profit adjustment. We analyzed the abstract of accounting research articles published in Korean Citation Index (KCI) by using text mining technique. Among papers whose themes were profit adjustment, topics were divided into 4 parts: (i) Auditing and audit reports, (ii) corporate taxes and debt ratios, (iii) general management strategy of companies, and (iv) financial statements and accounting principles. Unlike the prediction that financial statements and accounting principles would be the main topic, auditing was analyzed as the most studied area. We analyzed topic trends based on the number of papers by topic, and could figure out the impact of K-IFRS introduction on profit adjustment research. By using Big Data method, this study enabled the division of research themes that have not been available in the past studies. This study enables the policy makers and business managers to learn about additional considerations in addition to accounting principles related to profit adjustment.

• 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.

• Anatomy and Cell Biology
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• v.57 no.3
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• pp.333-345
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• 2024

Stem cells transplantation (SCT) is known as a newfound strategy for multiple sclerosis (MS) treatment. Human umbilical cord mesenchymal stem cells (hUCMSCs) contain various regenerative features. Experimental autoimmune encephalomyelitis (EAE) is a laboratory model of MS. This meta-analysis study was conducted to assess the overall therapeutic effects of hUCMSCs on reduction of clinical score (CS) and restoration of active movement in EAE-induced animals. For comprehensive searching (in various English and Persian databases until May 1, 2024), the main keywords of "Experimental Autoimmune Encephalomyelitis", "Multiple Sclerosis", "Human", "Umbilical Cord", "Mesenchymal", and "Stem Cell" were hired. Collected data were transferred to the citation manager software (EndNote x8) and duplicate papers were merged. Primary and secondary screenings were applied (according to the inclusion and exclusion criteria) and eligible studies were prepared for data collection. CS of two phases of peak and recovery of EAE were extracted as the difference in means and various analyses including heterogeneity, publication bias, funnel plot, and sensitivity index were reported. Meta-analysis was applied by CMA software (v.2), P<0.05 was considered a significant level, and the confidence interval (CI) was determined 95% (95% CI). Six eligible high-quality (approved by ARRIVE checklist) papers were gathered. The difference in means of peak and recovery phases were -0.775 (-1.325 to -0.225; P=0.006; I²=90.417%) and -1.230 (-1.759 to -0.700; P<0.001; I²=93.402%), respectively. The overall therapeutic effects of SCT of hUCMSCs on the EAE cases was -1.011 (95% CI=-1.392 to -0.629; P=0.001). hUCMSCs transplantation through the intravenous route to the animal MS model (EAE) seems a considerably effective procedure for the alleviation of motor defects in both phases of peak and recovery.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.57-77
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• 2014

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.455-460
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• 2020

The governments of Korea, China, and Japan have operated comprehensive research institutes for railway technologies. Korea Railroad Research Institute (KRRI), China Academy of Railway Sciences Corporation Limited (CARS), and Railway Technical Research Institute (RTRI) are representatives of comprehensive railway research institutes in each country. KRRI was found to be the most advanced in the quantitative competitiveness of patents. In terms of qualitative competitiveness, KRRI has strength in civil engineering, whereas RTRI has strength in electricity. KRRI was found to have the greatest efforts in securing competitiveness in overseas property rights. By comparing the publication of papers, CARS published the most papers. On the other hand, from 2015, KRRI showed an upward trend and published the most papers. By examining the impact of the papers by the citation, KRRI was found to have higher competitiveness than the other two institutions. In the future, it will be necessary to perform big data analysis on patents and papers of the three organizations, derive the key research areas and promising technology areas for each institute, and establish a mid-to-long-term development plan for railway technology based on scientific evidence.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.313-325
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• 2020

There is a growing interest and need for the educational use of artificial intelligence as artificial intelligence technologies such as machine learning and deep learning, the core technologies of the intelligent information society, owing to the recent innovative technological advances. Consequently, the Ministry of Education announced the First Information Education Comprehensive Plan for introducing artificial intelligence competence enhancing education into the education field in preparation for the intelligent information society based on artificial intelligence technologies. Therefore, this study collected 416 overseas papers related to the educational use of artificial intelligence from the Web of Science (WoS) in order to explore the potential for using artificial intelligence educationally. This study analyzed the research status and research topic by country, citation counts, network analysis on keywords of the collected data by using the bibliometrix package of R program. Through this, it was possible to identify the research trend on the educational use of artificial intelligence, currently being conducted in foreign countries. It is believed that it will be possible to obtain implications for the topics and directions to be studied in the information education for strengthening artificial intelligence education based on the results of this study.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.3
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• pp.290-307
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• 2017

The purpose of this study is to analyze the research trends related to qualitative research on science education, to provide basic data of qualitative research on science education and to select the direction of follow-up research. The subject of the study is the level of Korean Citation Index (KCI-listed, KCI listing candidates), that can be searched by the key phrase, 'qualitative research', 'science education' in Korean language through the RISS service. In this study, the Descriptive Statistical Analysis Method is utilized to discover the number of research articles, classifying them by year and by journal. Also, the Sementic Network Analysis was conducted to the frequency of key words, Centrality Analysis throughout a variety of research articles using krkwic and Ucinet6.0. The results show that first, 138 research papers were published in 14 journals from 2005 to 2017. Second,, the analysis showed the highest frequency of appearance keyword in each article, 'elementary school teacher', 'gifted student', 'science teacher', 'class' were higher than others. third, according to the results of the whole Network Analysis, 'Analysis', 'elementary school', 'class' were analyzed as a highly influential node. And 'Comparison', 'inquiry', 'recognition', 'gifted students' were not close to the center of network. Fourth, keywords that appear in all sections are analysis, gifted students, and elementary school students, and can be analyzed continuously based on studies, lessons or recognition, and characteristics. Based on the results of this study, we explored the past and present of the study subjects related to the study of science education quality and discussed future direction of study.