• 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.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• International Journal of Knowledge Content Development & Technology
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• v.12 no.4
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• pp.67-94
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• 2022

The paper aims to present a comparative analysis of scholarly research output in the fields of Library and Information Science (LIS) in India and South Korea. The Web of Science database was used to retrieve the bibliographic data of the Indian and South Korean LIS published documents during 2001-2020 and the indicators were included in the analysis: research productivity, publication-quality, most prolific authors, institutions and journals, "Annual Growth Rate (AGR)", "Compound Annual Growth Rate (CAGR)", "Relative Growth Rate (RGR)", and "Doubling Time (DT)". All types of documents such as articles, conference papers, book reviews, corrections, editorial materials, so on were included in the study. MS Excel, VOS viewer, and bibliometrix (R-tool) software were used for tabulation and mapping. The results show that South Korea placed the top in the overall output of LIS research publications during the last two decades. The Indian LIS research output, Annual Growth Rate (AGR), and Compound Annual Growth Rate (CAGR) were good compared to South Korean LIS publications. In addition, the South Korean LIS researchers' output has increased rapidly in terms of publications, citations, average citations. Gangan Prathap (India), Seyoung Lee, and Heejin Lee (SK) are the most prolific authors; Indian Institute Technology, Delhi and Yonsei University, Seoul are the most prolific institutions; and the Scientometrics journal was the most preferred journal by the Indian and South Korean LIS researchers during the study period. The results of this study are useful to administrators, policymakers, and academics. In addition, the scope of this study might include looking at research published by LIS scholars in India and South Korea, as well as examining all types of academic publications.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.53-63
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• 2023

The utilization of department information in bibliometric analysis using scientific and technological literature is highly advantageous. In this paper, the department information dataset was built through the screening, data refinement, and classification processing of authors' department type belonging to university institutions appearing in academic journals in the field of science and technology published in Korea, and the automatic classification model based on deep learning was developed using the department information dataset as learning data and verification data. In addition, we analyzed the co-authorship structure and network in the field of computer science using the department information dataset and affiliation information of authors from domestic academic journals. The research resulted in a 98.6% accuracy rate for the automatic classification model using Korean department information. Moreover, the co-authorship patterns of Korean researchers in the computer science and engineering field, along with the characteristics and centralities of the co-author network based on institution type, region, institution, and department type, were identified in detail and visually presented on a map.

• Korean Journal of Radiology
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• v.23 no.8
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• pp.835-845
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• 2022

Objective: To analyze the characteristics and trends of scientific publications on thyroid ultrasound (US) from 2001 to 2020, specifically examining the differences among disciplines. Materials and Methods: The MEDLINE database was searched for scientific articles on thyroid US published between 2001 and 2020 using the PubMed online service. The evaluated parameters included year of publication, type of document, topic, funding, first author's specialty, journal name, subject category, impact factor, and quartile ranking of the publishing journal, country, and language. Relationships between the first author's specialty (radiology, internal medicine, surgery, otorhinolaryngology, and miscellaneous) and other parameters were analyzed. Results: A total of 2917 thyroid US publications were published between 2001 and 2020, which followed an exponential growth pattern, with an annual growth rate of 11.6%. Radiology produced the most publications (n = 1290, 44.2%), followed by internal medicine (n = 716, 24.5%), surgery (n = 409, 14.0%), and otorhinolaryngology (n = 171, 5.9%). Otorhinolaryngology and internal medicine published significantly more case reports than radiology (p < 0.001, each). Radiology published a significantly higher proportion of publications on imaging diagnosis (p < 0.001 for all) and a significantly lower proportion of publications on biopsy (p < 0.001 for all) than the other disciplines. Publications produced by radiology authors were less frequently published in Q1 journals than those from other disciplines (p < 0.005 for internal medicine and miscellaneous disciplines and < 0.01 for surgery and otorhinolaryngology). China contributed the greatest number of publications (n = 622, 21.3%), followed by South Korea (n = 478, 16.4%) and the United States (n = 468, 16.0%). Conclusion: Radiology produced the most publications for thyroid US than any other discipline. Radiology authors published more notably on imaging diagnosis compared to other topics and in journals with lower impact factors compared to authors in other disciplines.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.145-158
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• 2023

Constructed wetlands (CWs) are effective technologies for urban wastewater management, utilizing natural physico-chemical and biological processes to remove pollutants. This study employed a bibliometric analysis approach to investigate the progress and future research trends in the field of CWs. A comprehensive review of 100 most-recently published and open-access articles was performed to analyze the performance of CWs in treating wastewater. Spain, China, Italy, and the United States were among the most productive countries in terms of the number of published papers. The most frequently used keywords in publications include water quality (n=19), phytoremediation (n=13), stormwater (n=11), and phosphorus (n=11), suggesting that the efficiency of CWs in improving water quality and removal of nutrients were widely investigated. Among the different types of CWs reviewed, hybrid CWs exhibited the highest removal efficiencies for BOD (88.67%) and TSS (95.67%), whereas VSSF, and HSSF systems also showed high TSS removal efficiencies (83.25%, and 78.83% respectively). VSSF wetland displayed the highest COD removal efficiency (71.82%). Generally, physical processes (e.g., sedimentation, filtration, adsorption) and biological mechanisms (i.e., biodegradation) contributed to the high removal efficiency of TSS, BOD, and COD in CW systems. The hybrid CW system demonstrated highest TN removal efficiency (60.78%) by integrating multiple treatment processes, including aerobic and anaerobic conditions, various vegetation types, and different media configurations, which enhanced microbial activity and allowed for comprehensive nitrogen compound removal. The FWS system showed the highest TP removal efficiency (54.50%) due to combined process of settling sediment-bound phosphorus and plant uptake. Phragmites, Cyperus, Iris, and Typha were commonly used in CWs due to their superior phytoremediation capabilities. The study emphasized the potential of CWs as sustainable alternatives for wastewater management, particularly in urban areas.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.216-227
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• 2020

In science and technology diplomacy, major countries actively utilize their capabilities in science and technology for public diplomacy, especially for promoting diplomatic relations with politically sensitive regions and countries. Recently, with an increase in the influence of science and technology on national development, interest in science and technology diplomacy has increased. So far, science and technology diplomacy has relied on experts to find research topics that are of common interest to both the countries. However, this method has various problems such as the bias arising from the subjective judgment of experts, the attribution of the halo effect to famous researchers, and the use of different criteria for different experts. This paper presents an objective data-based approach to identify and recommend research topics to support science and technology diplomacy without relying on the expert-based approach. The proposed approach is based on big data analysis that uses deep-learning techniques and bibliometric methods. The Scopus database is used to find proper topics for collaborative research between two countries. This approach has been used to support science and technology diplomacy between Korea and Hungary and has raised expectations of policy makers. This paper finally discusses aspects that should be focused on to improve the system in the future.

• Journal of Ginseng Culture
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• v.2
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• pp.1-8
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• 2020

We conducted a comprehensive analysis of research papers on ginseng to provide an overview of global ginseng research. The qualitative and quantitative interpretation was carried out using collected data of Panax species and six other herbal plants from the Web of Science^TM Core Collection. We summarized and classified them by country/territory and institutions based on the corresponding author's institution. The first ginseng paper appeared in 1905 and since then, 8,090 papers have been published until 2019. Among them 7,385 papers were published in recent 24 years from 1996 to 2019. It was 18 papers in 1980, 53 in 1990, 97 in 2000, 369 in 2010, and increased to 678 in 2019. Proportion of ginseng papers in total number of scientific papers were also greatly increased, namely, 0.0008% in 1970, 0.0044% in 1980, 0.101% in 1990, 0.0141% in 2000, and 0.0422% in 2019. 7,099 original research papers including notes and 286 review papers were published during last 24 years. Total 3,286 institutions in 78 countries and 1,274 journals contributed to the publication of ginseng papers. Korea was the leading country in ginseng papers up to 2013, however, China took over the top from 2014. Chinese institutions contributed 40.3% of total papers followed by Korea (34.7%), USA (6.0%), Japan (4.1%), and Canada (2.9%). Ginseng was the most studied medicinal plant during last 24 years followed by tea, garlic, ginkgo, and ginger whose number of papers were 6,499, 3,641, 2,590, and 1,945, respectively.