Bibliometric analysis of CiteSeer data for countries

Global trends and hotspots of phage therapy for bacterial infection: A bibliometric visualized analysis from 2001 to 2021

Background

Antibiotic resistance is one of the main global threats to human health, and just the development of new antimicrobial medications is not enough to solve the crisis. Phage therapy (PT), a safe and effective treatment method, has reignited the interest of researchers due to its efficacy in the clinical treatment of drug-resistant bacterial infections. There is, however, no bibliometric analysis of the overall trends on this topic. Therefore, this study aims to provide an overview of the current state of development and research in this area.

Methods

We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database between 2001 and 2021. We performed bibliometric analysis and visualization using CiteSpace, VOS viewer, and R software. Annual trends of publications, countries/regions distributions, institutions, funding agencies, co-cited journals, author contributions, core journals, references, and keywords were analyzed.

Results

A total of 6,538 papers were enrolled in this study, including 5,364 articles and 1,174 reviews. Publications have increased drastically from 61 in 2001 to 937 in 2021, with 3,659 articles published in the last 5 years. North America, Western Europe, and East Asia were significant contributor regions. The United States, China, and the United Kingdom were the most productive countries. The Polish Academy of Sciences was the most contributive institution. Frontiers in Microbiology and Applied and Environmental Microbiology were the most productive and co-cited journals. A. Gorski and R. Lavigne published most articles in this field, while V. A. Fischetti was the author with the most cited. Regarding keywords, research focuses include phage biology, phage against clinically important pathogens, phage lysis proteins, phage therapy, biofilm-related research, and recent clinical applications.

Conclusion

Phage therapy is a potential strategy for combating antibiotic resistance, and it will provide us with an alternative therapeutic option for bacterial infection. According to global trends, the scientific output of PT in bacterial infections is increasing, with developed countries such as the United States leading the way in this area. Although the safety and efficacy of PT have been proven, more clinical trials on the phages against infectious diseases caused by various pathogens are still needed.

Important citations identification by exploiting generative model into discriminative model

Although the citations between scientific documents are deemed as a vehicle for dissemination, inheritance and development of scientific knowledge, not all citations are well-positioned to be equal. A plethora of taxonomies and machine-learning models have been implemented to tackle the task of citation function and importance classification from qualitative aspect. Inspired by the success of kernel functions from resulting general models to promote the performance of the support vector machine (SVM) model, this work exploits the potential of combining generative and discriminative models for the task of citation importance classification. In more detail, generative features are generated from a topic model, citation influence model (CIM) and then fed to two discriminative traditional machine-learning models, SVM and RF (random forest), and a deep learning model, convolutional neural network (CNN), with other 13 traditional features to identify important citations. The extensive experiments are performed on two data sets with different characteristics. These three models perform better on the data set from one discipline. It is very possible that the patterns for important citations may vary by the fields, which disable machine-learning models to learn effectively the discriminative patterns from publications from multiple domains. The RF classifier outperforms the SVM classifier, which accords with many prior studies. However, the CNN model does not achieve the desired performance due to small-scaled data set. Furthermore, our CIM model–based features improve further the performance for identifying important citations.

Global publication trends and hotspots of molecular biomarkers in DILI from 1991 to 2020: A 30-year bibliometric analysis

Drug-induced liver injury (DILI) is one of the common adverse drug reactions and the leading cause of drug development attritions, black box warnings, and post-marketing withdrawals. Current biomarkers are suboptimal in detecting DILI and predicting its outcome. This study aimed to quantitatively and qualitatively investigate the research trends on DILI biomarkers using bibliometric analysis. All relevant publications were extracted from the Web of Science database. An online analysis platform of literature metrology, bibliographic item co-occurrence matrix builder, and CiteSpace software were used to analyze the publication trends. CitNetExplorer was used to construct direct citation networks and VOSviewer was used to analyze the keywords and research hotspots. We found a total of 485 publications related to DILI biomarkers published from 1991 to 2020. Toxicological Sciences had been the most popular journal in this field over the past 30 years. The USA maintained a top position worldwide and provided a pivotal influence, followed by China. Among all the institutions, the University of Liverpool was regarded as a leader for research collaboration. Moreover, Professors Paul B. Watkins and Tsuyoshi Yokoi made great achievements in topic area. We analyzed the citation networks and keywords, therefore identified five and six research hotspot clusters, respectively. We considered the publication information regarding different countries/regions, organizations, authors, journals, et al. by summarizing the literature on DILI biomarkers over the past 30 years. Notably, the subject of DILI biomarkers is an active area of research. In addition, the investigation and discovery of novel promising biomarkers such as microRNAs, keratin18, and bile acids will be future developing hotspots.

Bibliometric and Visualization Analysis of Human Coronaviruses: Prospects and Implications for COVID-19 Research

Human coronaviruses, which can cause a range of infectious diseases, have been studied for nearly 60 years. The field has gained renewed interest from researchers around the world due to the COVID-19 outbreak in late 2019. Despite a large amount of research, little is known about the knowledge structure and developing trends of this topic. Here, we apply bibliometric analysis along with visualization tools to analyze 15,207 publications related to human coronavirus from the Scopus database, using indicators on publication and citation, journal, country or territory, affiliation and international cooperation, author, and keyword co-occurrence cluster. The results show that research on human coronavirus is dominated by SARS-CoV. Although there have been many publications, only 626 publications (4.1% of total) have more than 100 citations. The top 20 journals with most publications account for 20.6% of total publications and 41% of total citations. In addition to the United States and some European countries, many Asian and African countries are involved in this research, with China holding an important position in this area. Leading researchers from various fields of human coronavirus research are listed to facilitate collaboration and promote effective disease prevention and control. The keywords co-occurrence analysis reveals that the research focus on virology, public health, drugs and other hotspot fields, and uncovers changes in the direction of coronavirus research. The research map on human coronavirus obtained by our analysis are expected to help researchers to efficiently and effectively explore COVID-19.

Bibliometric Analysis of Dendritic Epidermal T Cell (DETC) Research From 1983 to 2019

Dendritic epidermal T cells (DETC) are a group of immune cells expressing canonical γδ TCR in the murine epidermis. Similar to γδ T cells in the human epidermis, DETC serve an important barrier cell in the skin and participate in skin immune surveillance, immune regulation, skin homeostasis, tissue protection, and other activities. Since its discovery in 1983, research on DETC has grown rapidly and unevenly. To evaluate DETC research trends and map the DETC knowledge structure, we have applied bibliometric methods and techniques. A total of 384 DETC-related articles obtained from the Scopus database published between 1983 and 2019 were analyzed using indicators of publication and citation metrics, country and international cooperation, author and co-authorship, and keyword co-occurrence cluster. The present research status, the emerging global trends and the future development direction are also visualized and discussed. In summary, this study provides novel and useful data for the DETC research scientific community, and will help researchers explore DETC more intuitively and effectively.

Modelo de estimación de los indicadores del Academic Ranking of World Universities (Shanghai Ranking)

El interés en los rankings globales de universidades ha crecido significativamente a lo largo de los últimos 10 años. El uso de indicadores simples y sintéticos, la facilidad de interpretación de la información que contienen, el fomento de la competencia entre las universidades, así como la posibilidad de compararlas entre sí, son algunos de los factores que han popularizado su aplicación. Al mismo tiempo, sus críticos han identificado problemas en relación a cuestiones tanto conceptuales como técnicas y metodológicas. Este artículo aborda tres temas que han suscitado un intenso debate metodológico sobre los rankings de universidades: la replicabilidad de los resultados, la relevancia de los indicadores, y la recopilación de datos. También propone una herramienta para estimar las puntuaciones de los dos indicadores de mayor interés para la mayoría de las universidades (Documentos publicados en Nature o Science y publicaciones en WoS). Se informa sobre un método alternativo desarrollado para calcular la puntuación de cualquier universidad en los dos indicadores de ranking más importantes de Shanghái. Una de las principales características del método propuesto es que los input necesarios son fácilmente accesibles para los gestores de política científica, autoridades académicas, estudiantes, y otros grupos de interés, pudiéndose aplicar directamente. Además, con este modelo también se pueden estimar las puntuaciones para las universidades que no figuran entre las primeras 500 en el ranking de Shanghái.

Computer science in Eastern Europe 1989-2014: a bibliometric study

Purpose

– The purpose of this paper is to study the development of research in computer science in 15 Eastern European countries following the breaching of the Berlin Wall in 1989.

Design/methodology/approach

– The authors conducted a bibliometric analysis of 82,121 computer science publications indexed in the Web of Science database and investigated publication, citation, and collaboration patterns of the individual countries.

Findings

– Poland has been the most productive country, followed by Russia, the Czech Republic, Romania, Hungary, and Slovenia. Publication rates have increased substantially over the period, but this has not been accompanied by a corresponding increase in the quality of the publications. Hungary and Slovenia are the most influential countries in terms of citations per paper. Artificial Intelligence is the most frequently occurring computer science subject category, with Interdisciplinary Applications the category with the greatest impact. USA, Germany, UK, France, and Canada are the most frequently collaborating western nations, and papers published in collaboration with US authors accrue the most citations.

Originality/value

– This is the first ever bibliometric study of the whole post-communist Eastern European computer science research as indexed in the Web of Science.

The development of computer science research in the People’s Republic of China 2000–2009: a bibliometric study

This paper reports a bibliometric study of the development of computer science research in the People’s Republic of China in the 21st century, using data from the Web of Science, Journal Citation Reports and CORE databases. Focusing on the areas of data mining, operating systems and web design, it is shown that whilst the productivity of Chinese research has risen dramatically over the period under review, its impact is still low when compared with established scientific nations such as the USA, the UK and Japan. The publication and citation data for China are compared with corresponding data for the other three BRIC nations (Brazil, Russian and India). It is shown that China dominates the BRIC nations in terms of both publications and citations, but that Indian publications often have a greater individual impact.