Co-authorship Network Research Articles

Financial Development and Climate Change: A Detailed Bibliometric Investigation

This paper presents a detailed bibliometric analysis of the interaction between financial development and climate change, with the main aim of elucidating the current state of research in this area, identifying existing gaps, and guiding future researchers interested in this rapidly expanding field. The study used VOSviewer software version 1.6.18 to analyze the bibliometric data, facilitating the mapping of co-author networks, institutional collaborations, and the identification of main research directions. Through this tool, 730 papers from the Web of Science database covering the period 2010–2024 were extracted and analyzed. The study highlights the authors and institutions that have made significant contributions to the investigation of the relationship between financial development and climate change. The identification of key contributors and international collaborative networks provides a solid foundation for future research and policy initiatives. In addition, the study identifies the most prolific journals and assesses the quality and impact of the research. This allows for a deeper understanding of current research directions and potential future developments. The study not only clarifies the current state of research but also opens up new opportunities for investigating innovative and sustainable solutions aimed at improving the quality of life and protecting the environment.

A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis

Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL.

Bibliometric Insights into the "Kathmandu School of Law Review"

This bibliometric study analyzes the academic reach, thematic focus, and collaboration trends of the Kathmandu School of Law Review (KSLR). Through keyword analysis, the research highlights recurring themes such as human rights, governance, policy-making, and development, reflecting a multidisciplinary approach to legal scholarship. Visualizations of co-authorship networks reveal limited collaboration among scholars, suggesting opportunities for the journal to foster joint publications and interdisciplinary partnerships. Citation network analysis indicates a niche area of research with a few central authors like Puja Mitra and Yubaraj Sangroula, while the low overall citation count points to the need for broader academic engagement and visibility. Bibliographic coupling maps show emerging research clusters, indicating the potential for KSLR to build authority in key areas such as South Asian legal systems and international human rights. The study concludes with recommendations for KSLR to increase global outreach, promote interdisciplinary studies, and enhance academic collaboration to strengthen its impact within and beyond the region.

Co-authorship Networks in Left-Behind Children Research in China: A Social Network Analysis

Co-authorship Networks in Left-Behind Children Research in China: A Social Network Analysis

Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis.

Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including 'human' and 'dental caries,' indicated research hotspots. We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Insights and networks: methodological assessment and scientometric analysis of economic evaluations in dentistry

IntroductionAssessing the methodological quality of economic evaluations (EEs) is crucial for evidence-based decision-making. The study aimed to evaluate EEs in restorative dentistry and endodontics, while also analyzing the scientific landscape of researchers and publications through co-authorship and citation network analysis providing an insight into the distribution of scientific expertise.MethodologyA systematic search for relevant articles from 2012 to 2022 was conducted using PubMed, Scopus, and EBSCO. The ten-point Drummond checklist was used to appraise the methodological quality of included studies. Bibliometric data for network analysis were extracted from the Dimensions database and visualized using VOSviewer software.ResultsOf the 37 articles, 81.08% scored good, 16.21% average, and 2.7% poor on the methodological rating scale. Most of the included studies were in Q1 journals, with limited representation in Q2 and Q3 journals. Compliance was highest in Q2 journals (95%), followed by Q1 (88.36%), while it dropped to 40% for Q3 journals. Co-authorship analysis revealed a dense network of researchers, with Prof. Falk Schwendicke V. having a significant influence. Moreover, the Journal of Dentistry had the highest impact, followed by Journal of Endodontics and BMC Oral Health.ConclusionsDespite a diverse scientific landscape, participation from developing countries was limited emphasizing the need for inclusivity and diversity in the scientific network. While the quantity of good-quality studies was encouraging, the overall quality of evidence remains paramount for decision-making in healthcare policy and practice. Therefore, continuous efforts to improve methodological rigor and reporting practices are essential to contribute robust evidence.

Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis

Background Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. Methods A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. Results The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including ‘human’ and ‘dental caries,’ indicated research hotspots. Conclusion We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.

Kidney medicine meets computer vision: a bibliometric analysis.

Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

A BIBLIOMETRIC ANALYSIS OF RESEARCH ON DROPOUT IN OPEN AND DISTANCE LEARNING

The purpose of this study is to examine research on school dropout in open and distance education in the Web of Science (WoS) database using bibliometric analysis and to reveal trends in this area. In line with this goal, a total of 1,615 studies published between 1980 and 2022 were identified in the Web of Science (WoS) indexes. Descriptive and evaluative bibliometric methods were employed in the analysis of these publications, and the results were visualized using VOSviewer software. According to the research findings, studies on school dropout in open and distance education intensified in 2019. The analysis revealed that E.T. Pascarella is the most cited author, and F.D. Pereira has the highest co-authorship network. An examination based on institutions showed that the Universitat Autònoma de Barcelona has the highest number of publications, while the University of Michigan ranks first in terms of citation numbers. Furthermore, in terms of publication productivity and citation numbers, the United States is ranked first, followed by Spain. Looking at the keywords used in articles on the topic, early studies emphasized concepts such as “distance education”, “student retention”, “attrition”, “student success”, “social integration”, “academic integration”, “survival analysis”, “education policy”, “graduation”, and “financial aids”. In subsequent years, these were replaced by terms such as “learning analytics”, “educational data mining”, “systematic review”, “engagement”, “drop prediction”, “decision tree”, “student performance”, “gamification”, “massive open online course- MOOC”, and “artificial intelligence”. In this context, discussions were conducted within the framework of the literature, and various recommendations were provided based on the obtained findings.

Analysis of Effects on Scientific Impact Indicators Based on Coevolution of Coauthorship and Citation Networks

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations.

Bibliometric Analysis of the Development of Entrepreneurial Ecosystem Research in Global Publications

This study conducts a comprehensive bibliometric analysis of the field of entrepreneurial ecosystems, mapping the evolving landscape of research from 1995 to 2024. Utilizing VOSviewer for visual network analysis, the research identifies central themes, examines co-authorship and institutional collaboration networks, and highlights the temporal progression of key topics. The findings reveal "entrepreneurial ecosystem" as a nexus connecting diverse research themes such as innovation, economic growth, and sustainability. Significant emphasis is placed on the integration of digital technologies and sustainability within entrepreneurial practices, reflecting shifting academic and practical priorities. The analysis of co-authorship networks shows robust collaboration among leading scholars and institutions, predominantly from Western regions, with increasing contributions from a global cohort. The study underscores the importance of incorporating diverse perspectives to fully understand the multifaceted nature of entrepreneurial ecosystems and suggests future research directions focusing on digital transformation and sustainability in entrepreneurship.

Global Trends in Employee Wellbeing Research: A Bibliometric Study

This bibliometric study examines global trends in employee wellbeing research from 1990 to 2024, focusing on key themes such as burnout, work engagement, job satisfaction, leadership, and workplace environment. Using co-authorship networks, keyword co-occurrence analysis, and citation metrics, the study identifies influential authors, emerging trends, and underexplored areas in the field. Results show a significant increase in publications since 2017, with a growing focus on the psychological and emotional dimensions of wellbeing. Key findings highlight the positive impact of transformational leadership and supportive work environments, as well as the detrimental effects of toxic behaviors like abusive supervision and workplace bullying. The study also identifies gaps in the literature, particularly regarding the intersection of employee wellbeing and diversity, equity, and inclusion (DEI). This analysis provides a foundation for future research and emphasizes the critical role of employee wellbeing in fostering organizational success and sustainability.

The Role of Innovation Management in Creative Economy-Based Product Development in West Java Province

This study conducts a comprehensive bibliometric analysis of the field of entrepreneurial ecosystems, mapping the evolving landscape of research from 1995 to 2024. Utilizing VOSviewer for visual network analysis, the research identifies central themes, examines co-authorship and institutional collaboration networks, and highlights the temporal progression of key topics. The findings reveal "entrepreneurial ecosystem" as a nexus connecting diverse research themes such as innovation, economic growth, and sustainability. Significant emphasis is placed on the integration of digital technologies and sustainability within entrepreneurial practices, reflecting shifting academic and practical priorities. The analysis of co-authorship networks shows robust collaboration among leading scholars and institutions, predominantly from Western regions, with increasing contributions from a global cohort. The study underscores the importance of incorporating diverse perspectives to fully understand the multifaceted nature of entrepreneurial ecosystems and suggests future research directions focusing on digital transformation and sustainability in entrepreneurship.

Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades

This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.

Exploring Diabetes Research Trends in India: A Comprehensive Bibliometric Analysis Using VOSviewer

Diabetes has become a pressing public health issue in India, prompting the need for a comprehensive understanding of research trends to effectively guide interventions. This study conducted a bibliometric analysis (BA) of diabetes research publications in India spanning from 2014 to 2023 to elucidate the research landscape. Through a systematic literature search using databases, a vast corpus of 498,138 publications was identified, predominantly comprising research and review articles. Temporal analysis of the data revealed a consistent publication output over the years, with a notable increase in recent years, indicating a growing research interest and focus on diabetes-related topics. Keyword analysis shed light on prevalent themes within the literature, such as experimental models and clinical aspects, showcasing the diversity of research focuses within the field. Top-cited articles predominantly addressed clinical studies, suggesting an emphasis on evidence-based research to inform clinical practice and policymaking. Co-authorship networks highlighted collaborative efforts among researchers, with the All India Institute of Medical Sciences emerging as a prominent contributor, reflecting the importance of collaborative research initiatives in tackling complex health challenges. Further analysis through bibliographic coupling and co-citation analysis revealed thematic clusters within the literature, providing insights into specific research areas such as complications and case reviews. These thematic concentrations offer valuable directions for future research and intervention strategies.

Knowledge Mapping and Global Trends in the Field of the Objective Structured Clinical Examination: Bibliometric and Visual Analysis (2004-2023).

The Objective Structured Clinical Examination (OSCE) is a pivotal tool for assessing health care professionals and plays an integral role in medical education. This study aims to map the bibliometric landscape of OSCE research, highlighting trends and key influencers. A comprehensive literature search was conducted for materials related to OSCE from January 2004 to December 2023, using the Web of Science Core Collection database. Bibliometric analysis and visualization were performed with VOSviewer and CiteSpace software tools. Our analysis indicates a consistent increase in OSCE-related publications over the study period, with a notable surge after 2019, culminating in a peak of activity in 2021. The United States emerged as a significant contributor, responsible for 30.86% (1626/5268) of total publications and amassing 44,051 citations. Coauthorship network analysis highlighted robust collaborations, particularly between the United States and the United Kingdom. Leading journals in this domain-BMC Medical Education, Medical Education, Academic Medicine, and Medical Teacher-featured the highest volume of papers, while The Lancet garnered substantial citations, reflecting its high impact factor (to be verified for accuracy). Prominent authors in the field include Sondra Zabar, Debra Pugh, Timothy J Wood, and Susan Humphrey-Murto, with Ronaldo M Harden, Brian D Hodges, and George E Miller being the most cited. The analysis of key research terms revealed a focus on "education," "performance," "competence," and "skills," indicating these are central themes in OSCE research. The study underscores a dynamic expansion in OSCE research and international collaboration, spotlighting influential countries, institutions, authors, and journals. These elements are instrumental in steering the evolution of medical education assessment practices and suggest a trajectory for future research endeavors. Future work should consider the implications of these findings for medical education and the potential areas for further investigation, particularly in underrepresented regions or emerging competencies in health care training.

The relationship between coauthorship and the research impact of medical doctoral students: A social capital perspective

Research impact is an important manifestation of research competence and the focus of medical education. This study is dedicated to exploring the relationships between coauthorship networks and the research impact of Chinese medical doctoral students from a social capital perspective. A total of 16291 scientific papers from 237 doctoral students and 126 mentors at Chinese universities were selected from databases, and a study dataset including 19 variables was constructed. Nine independent variables were defined and obtained through coauthorship network analysis, and the doctoral students’ research impact, as the only dependent variable, was used to test the hypothesized relationships among the variables. The results show that the betweenness centrality, student-mentor coauthorship count and the partnership ability index significantly affect the h-index. Specifically, the coauthorship unit count plays the most important role in developing centrality, which, in turn, produces a higher h-index. In addition, betweenness centrality, student–mentor coauthorship count and the partnership ability index are good predictors of the likelihood of doctoral students entering the greater research impact group and especially improving their betweenness centrality. Specifically, doctoral students whose partnership ability index is greater than 2, student-mentor coauthorship count is greater than 4, coauthorship unit count is greater than 6, and betweenness centrality is greater than 0.02 are considered to have greater research impact. These findings suggest that the important roles of cooperation in the development of research competence and good mentorship in the acquisition of social capital by doctoral students should be emphasized. Several strategies are advised for harnessing social capital rooted in doctoral students’ coauthorship network for relevant organizations, mentors and doctoral students who want to increase medical doctoral students’ research impact.

THE EFFECT OF DIGITALIZATION ON HUMAN RESOURCE MANAGEMENT PRACTICES: A BIBLIOMETRIC ANALYSIS

This study examines the intersection of digitalization and human resource management (HRM) practices through a bibliometric analysis. Utilizing the VOS Viewer program for quantitative analysis and supplemented by qualitative content reading, the research explores key trends, themes, and authorship patterns within the existing literature. The primary data source is the Web of Science database, yielding 268 relevant studies published between 2005 and 2023. Findings indicate a growing interest in the topic, particularly within the management discipline, and highlight key HRM practices impacted by digitalization, including recruitment, training and development, performance management, and career management. The analysis reveals that digitalization enhances HR efficiency, reduces costs, and supports learning and development, while also presenting challenges such as data security risks, skill gaps, and potential resistance to change. Co-authorship and citation networks identify influential authors and emerging research clusters. The study underscores the interdisciplinary nature of digital HRM research and calls for broader database inclusion to enhance future bibliometric studies.

Scientometric analysis of cover crop management: Trends, networks, and future directions

This research paper presents a comprehensive scientometric analysis of English articles from the Scopus database regarding the topic of cover crop management from 1956 to March 2024. Through the analysis of the annual production trend, total production, a co-occurrence network of keywords, co-authorship networks, and co-citation networks, the data was mapped and visualized using VOSviewer and Bibliometrix software. There was an exponential increase in publications from 1991 onwards. The predominant subject was Agricultural and Biological Sciences and the most relevant journals, authors, and documents were related to this topic. Additionally, the most productive country was the United States, but in terms of article production per surface area, other countries, such as Switzerland and The Netherlands, evidence the great weight of cover crop management in these countries. The identified research topics were related to the application of different crops as cover crops and the effects on different cultivars, also soil quality improvement, and fertilization and nutrient efficiency. An emerging research topic was found to be the usefulness of cover crops as effective tools for climate change adaptation and mitigation strategies in agriculture. Future research in the field of cover crop management could be directed towards climate-adaptive cover crop species and varieties, the use of technological innovations for cover crop monitoring, and management and analysis of the economic and social impacts of cover crop adoption.