Citation Data Research Articles

Twenty-five years of Accounting History: A bibliometric analysis

This article reviews the 25 years of Accounting History. Using bibliometric analysis, we provide a summary of bibliometric statistics to present an overview of the peer-reviewed journal's publication landscape. For this reason, we use a sample of 566 documents from the Scopus database between 1996 and 2021. We document publication and citation data indicating an increased rate of productivity and influence. We identify the leading authors, institutions and countries and find that most contributions stem from Australia-based affiliations. We also apply network analysis to reveal the major themes, collaboration patterns and existing relationships among topics. We discover that the journal displays global views that are not limited to the wisdom of one country. We classify dominant topics into 11 clusters and suggest promising avenues for future research.

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

Evolving Computer Science Education Research in Teaching and Learning: A Bibliometric Analysis

This study performed a bibliometric analysis of computer science education (CSE) publications in teaching and learning. The research trend in computer science education was analysed using publications from the Scopus-indexed database between 1970 and 2021. This study aims to summarize this information to assist three groups of stakeholders: educators, researchers and policymakers. Our review goal to document and analyse the trends in CSE research publications for teaching and learning. A total of 800 publications were derived for further assessment based on computer science education in the article title with multiple tools. We used, Microsoft Excel, VOSviewer and Harzing’s Publish or Perish were utilized for frequency analysis data visualization and citation metrics and assessment. The study outcomes were documented with standard bibliometric indicators: publication growth, authorship trends, collaboration, prolific authors, national contribution, most active establishments, top-cited articles, preferred journals, intellectual structure and topical trends. Resultantly, CSE research publications have perpetually grown for the past 51 years since 1970 with the United States highlighting the largest study contributor, followed by Germany and the United Kingdom. The ACM SIGCSE Bulletin published the highest number of CSE research publications. The analysis of the cluster on topical trends indicates that curriculum design and implementation (e.g., assessment, broadening participation), elements of teaching and learning (e.g., constructivism, e-learning, game-based learning) and other areas of interest comprise pedagogical approach (e.g., computational thinking, k-12 education, motivation). Based on the research results, we expand on the findings and propose how authors might enhance the CSE function in curriculum design, implementation and shifts could eventually substitute past study clusters.

Higher-order structure based node importance evaluation in directed networks

Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.

Open Science in Agricultural Economics

Abstract We provide a ‘big picture’ of what Open Science is and what benefits, limitations, and risks it entails for agricultural economists. We show that Open Science comprises various aspects, such as, the accessibility of science, transparency of scientific processes, open and FAIR research data and code, and openness in teaching and education. We identify potential benefits of Open Science for individual researchers and the public, as well as adoption barriers. We highlight that public benefits of a wide-spread uptake of Open Science practices still remain unexplored. We share best practice examples for key aspects of agricultural economic research, i.e., primary data collection and analysis, optimization and simulation models, use of replication packages and an Open Science Community. Assessing the author guidelines of twelve Agricultural Economics journals for their Open Science practices, we find that data citation and transparency are considered important in many journals already, whereas replication, pre-registration or results-blind review are encouraged but rarely enforced. It also becomes evident that the journals differ in terms of how strictly they enforce their open-science guidelines. We close by providing recommendations for researchers, journal editors, policy makers, universities, research institutes and funding agencies to better align public benefits with private incentives.

Addressing the Interdisciplinary Nature of Health Communication Research: A Citation Analysis to Determine Core Journals for Different Library Collections

Health communication is important to individuals, organizations, and society. Using citation data, this study analyzes citations to and within the two health communication journals, Health Communication and Journal of Health Communication from 1997/1999 through 2022 and identifies scholarly journals most relevant for health communication research from within and outside the field. Recognizing the interdisciplinary nature of health communication, this study provides a more inclusive picture of health communication research and its relevance to other subject areas by detailing the most cited and citing journals. By applying Bradford’s Law of Scattering, the author will establish a core list of journals for health communication research. The data will provide evidence-based recommendations for collection management to librarians who manage communication, health, and medical journal collections.

Bibliometric Insights into Anger and Aggression Research

Background: Anger and aggression are central constructs in psychology, sociology, and behavioral sciences, affecting mental health and social interactions. Despite extensive research, there is a need to map the trends and focal points of studies in this area, particularly to understand how themes like mental health disorders, developmental psychology, and social dynamics interconnect with aggression. Objective: This study aims to conduct a bibliometric analysis of global research on anger and aggression, identifying key trends, influential authors, leading research areas, and interdisciplinary approaches. The goal is to provide insights into the trajectory and focus of scholarly work in this field. Methods: Using bibliometric techniques, including co-occurrence mapping and citation analysis, data from relevant research articles were extracted and analyzed through VOSviewer. Key terms, authorship networks, and publication patterns were examined to uncover thematic clusters and research trajectories. Findings: The analysis highlights several prominent themes in the literature on anger and aggression, including their links to psychological disorders (e.g., depression, stress), behavioral development in children and adolescents, and the influence of family and social environments. The co-occurrence analysis also points to growing interest in therapeutic interventions, cognitive and neurological underpinnings of aggression, and biological factors, such as brain injury. Authors such as Richard Tremblay, David Farrington, and Manuel Eisner have emerged as influential figures in this research domain. Conclusion: This bibliometric study reveals that research on anger and aggression is characterized by interdisciplinary approaches, integrating psychology, neuroscience, and social sciences. The focus on mental health and therapeutic interventions highlights the practical applications of this research in clinical settings. The increasing interest in the neurological and biological dimensions of aggression also suggests a broadening of the research agenda. Novelty: This study provides a comprehensive overview of the intellectual landscape of anger and aggression research through a bibliometric lens, offering new insights into the evolving trends, collaboration networks, and thematic directions in the field. It is one of the first studies to integrate a co-occurrence analysis with citation data to map both psychological and biological dimensions of aggression research.

A bibliometric analysis and typology of drug pricing policies across the globe.

Limited research exists on pricing policies from a bibliometric and visualization perspective, and there is a lack of understanding of their typology. This study aims to address these gaps in knowledge and provide a deeper understanding of the research topics and development trends in this field. A bibliometric study was conducted on drug pricing approaches in healthcare literature, published between 2000 and June 2023. The literature was identified through an extensive search of healthcare databases and was then classified based on the year of publication, research topics, corresponding authors, location of corresponding authors, and journal titles. The citation data analysis was conducted using Bibliometrix, which consisted of descriptive, geographical, and time-series analyses and visualization. Between 2000 and June 2023, 173 articles were disseminated across 98 distinct publication sources. During the analysis, we observed a significant and consistent rise in literature reports on drug pricing approaches in healthcare, especially in 2010. The research topics were distributed almost equally, discussing improvement or issues with drug pricing models and addressing drug pricing applications. Our analysis revealed that the top ten corresponding authors were responsible for 19% of the total articles, with those based in the United States being the most productive. Furthermore, the "Health Economics" journal ranked first among the top ten journals. These findings align with the overall publication trends of drug pricing methods reported in other fields. The current study offers a comprehensive overview of drug pricing techniques utilized in medicine through visualization and bibliometric techniques. Analysis of authors, journals, institutions, and countries could serve as a reference for new researchers and guide them differently. Researchers can also consider emerging trends when determining the focus of their studies.

Examining Diversity and Equity in LIS Education: A Syllabus to Reading List Analysis

To determine how effectively the Master of Library and Information Science curriculum at Indiana University - Indianapolis is addressing the new ALA core competency related to social justice, we analyzed all the course syllabi and are now in the process of analyzing all course readings, using various text analysis methods. We found the initial review of syllabi to lack the level of granularity that would have revealed the different kinds of diversity and equity issues in the curriculum. Additionally, courses we know for certain that covered topics related to social justice, the syllabus alone indicated otherwise. For these reasons, we decided to examine the readings that comprised the entire curriculum. With 68 courses and over 3000 readings to date, gathering the data for analysis has taken several months of time. Metadata and PDFs of course readings were stored in Zotero and extracted from there for analysis. Terminology, as identified from the study that examined all ALA-accredited LIS program course descriptions, will be used to frame the textual analysis (Dill, Grote, & Hardin, 2023). We will present the topics and trends identified through bibliometric analysis, topic modeling, and visual analysis of the citation data. This analysis will shed light on areas of strength and for improvement within curriculum with the goal of preparing students to effectively work in socially just library environments.

Navigating Global Research Trends on Seafoods-Related Foodborne Diseases: A Decadal Bibliometric Analysis

Ensuring seafood safety has become a critical area of focus due to its strong association with foodborne diseases. Despite the critical nature of this field, an in-depth understanding of global research trends in this field remains limited. Our study conducted an extensive bibliometric analysis to track the evolution and thematic focus of research on seafood safety from 2013 to 2023. Utilizing Scopus for publication and citation data, along with VOSviewer for visualizing keyword networks, this study highlights key developments and scholarly efforts aimed at mitigating risks associated with seafoods consumption. The analysis pinpoints the United States, China, the United Kingdom, France, South Korea and Malaysia as leading contributors. Their extensive research outputs and collaborations have markedly shaped the field's evolution. The overall positive trajectory in research volume experienced a minor setback in 2022, suggesting a complex and dynamic research landscape. "Food Control" is identified as a principal journal for the dissemination of research works, anchoring a dynamic co-citation network that fosters extensive international dialogue. We categorize seven principal research clusters: epidemiology, molecular detections, microbial resistance, bacteriophage applications, toxin detection methodologies, predictive modelling and biofilm studies. These clusters encompass the scopes and the breadth of current investigations. This decade-long review highlights the necessity for cohesive global research strategies to enhance the knowledge and control of foodborne pathogens in seafoods. This strategy is critical for improving food safety measures and safeguarding public health globally.

A new method of calculating the disruption index based on open citation data

A new method of calculating the disruption index based on open citation data

Research Data Management from Hungarian Perspective

In science and education, research data is a valuable resource that can be utilized and repurposed for future use. Research data management is crucial for successful research, as it can generate reliable and advantageous results and facilitate accurate decision-making. The systematic review study followed the PRISMA Statement standards for reporting. Data regarding research data management from a Hungarian perspective was collected from SCOPUS, Web of Science, and EBSCO databases on January 5th, 2024. Forty-one records were identified from SCOPUS, Web of Science, and EBSCO databases. After screening, the remaining 13 articles were included in this study. The Hungarian authors discussed four distinct research data management activities. The activities included data sharing, data citation, data curation, and data storage. Several additional data-related concepts, such as data governance, data quality, data literacy, and research data service, were also examined in this study.

Thematic Trends in Data Curation Literature

ABSTRACTThe field of data curation is rapidly changing due to new developments in technologies and techniques for conducting data work. As the field of data curation evolves, researchers, practitioners, and educators need to be able to respond to these developments. One way to understand trends in a field is by examining published literature. This study first gathered data curation literature through a modified systematic literature review with the framing question, ‘What competencies, skill sets, and proficiencies are needed to conduct data curation activities?’. These literatures were then analyzed using bibliometric analysis, visual analysis of the citation data, and topic modeling to understand trends in the data curation field.

Dynamics of Corporate Finance Research in Global Scientific Literature

This study explores the dynamics of corporate finance research in global scientific literature from 2000 to 2024 using a bibliometric approach. By analyzing keyword co-occurrence and citation data, the research identifies key themes, emerging trends, and influential contributors in corporate finance. Core topics such as capital structure, cost management, and debt remain central, reflecting traditional corporate finance priorities. However, newer areas, including corporate social responsibility (CSR), corporate cash holdings, and behavioral finance, have gained prominence, signaling a shift towards integrating non-financial factors into corporate strategies. The study also highlights the lasting impact of global financial crises, particularly the 2008 crisis, on shaping research in financial distress, risk management, and institutional roles. These insights provide a comprehensive overview of the evolution of corporate finance research and offer directions for future studies.

The Impact of Social Media on Otolaryngology Literature: Analyzing the Correlation Between Altmetrics and Citation Count.

The altmetric attention score (AAS) is an alternative metric that tracks article sharing via online platforms, reflecting an article's online attention trend. The objective of this study was to analyze the impact of social media on Otolaryngology-Head and Neck Surgery (OHNS) literature and analyze the correlation between AAS and citation count. A retrospective review of otolaryngology journal article citation data and Altmetric attention score. The top 10 OHNS journals with highest impact factors were identified using the Journal Citation Reports (JCR). The number of citations in 2018 and 2019 were extracted from JCR and AAS was extracted from the altmetrics website. The primary outcome of this study was to establish whether a correlation between AAS and citation count exists, and whether AAS could serve as a valid alternative metric to assess the quality of individual articles. By analyzing data from 3729 articles, a weak statistically significant positive correlation was identified between AAS and citation count (r = 0.18, P < .001), and between number of citations and Twitter activity (r = 0.18, P < .001). In addition, a statistically significant strong correlation was seen between Twitter activity and AAS (r = 0.79, P < .001). The current results clearly illustrate a weak correlation between AAS and citations and between Twitter activity and citations. Due to various limitations, the use of AAS should be limited to serve as a complementary metric to the current gold standard rather than an alternative metric.

Mapping research data landscape in Africa using the Re3data and data citation Index

Despite the efforts made by most African countries in incubating the practice of research data sharing, little is known on the status of the research data sphere in the African landscape. This study was conducted to establish the status of research data and research data repositories (RDRs) in Africa. Specifically, the study intended to identify the country-wise contributions in terms of research data and research data repositories, trends of publication of research data by African countries, content type and the nature of research data repositories hosted by African countries. The study applied the quantitative research approach. The registry of research data repositories (re3data), which is the prominent worldwide registry for the research data repository, and the data citation index (DCI) hosted by the Web of Science were used in this study. Re3data and DCI index research data repositories and datasets respectively, from almost all the countries around the world. The study identified parameters that were extracted from re3data and DCI using the browse and search facilities. The extracted data were recorded into Google Sheets, refined, and analysed using MS Office Excel. The study found little contribution from the African countries regarding research data and research data repositories to re3data and DCI. The study found further that, despite the presence of a few repositories, most of them had restricted access to external users. This study concludes that African countries are still having a lot to do for the research data practices to be fully incubated. The study suggests that governments and other stakeholders should improve the facilitating conditions, including establishing relevant policies and infrastructures for the practice to be fully embraced.

Bibliometric and Content Analyses of Dyscalculia Research

Dyscalculia is a math learning difficulty that many researchers have studied. A bibliometric and content analysis was conducted using the Web of Science database to provide a comprehensive overview of existing research on dyscalculia. The study aimed to identify key themes, prevailing research trends, potential knowledge gaps, and influential authors and articles in the field. The analysis focused on several research questions related to the distribution of articles and their citation data over the years, the types and numerical distribution of publications on dyscalculia, collaborative networks between authors, institutions, and countries/regions, topical research foci, prominent journals publishing articles on dyscalculia, top authors and countries researching dyscalculia, disciplines comprising the foundation of dyscalculia research, topics covered, the sample studied, and methods preferred in highly cited articles on dyscalculia. The co-occurrence analysis of the keywords revealed that dyscalculia, mathematical difficulties, dyslexia, and developmental dyscalculia were the most frequently cited terms. The top ten most cited articles were subjected to content analysis, revealing the types of research and methodologies. This study paves the way for future research and emphasizes the importance of interdisciplinary collaboration in understanding the nature and underlying causes of mathematics learning disabilities.

Analisis Bibliometrik Untuk Mengkaji Penerapan Digital Marketing Pada UMKM

This paper aims to provide an overview and existing knowledge of digital marketing, which is applied to Small and Medium Micro Enterprises (MSMEs). The study used bibliometric analysis methods. Data were obtained through a Google Scholar (GS) database search with the word Digital Marketing at MSMEs as Article Title, Abstract, Keywords. About 200 articles were obtained in preliminary results with 143102 citations (14310.20 quotes/year). The search results obtained 138 articles, as well as the citation data changed, with 74904 citations and 7490.40 citations/year, during the period 2013-2023. From the results, 138 articles were refined and the citation data was also changed to 74904 citations and 7490.40 citations each year. The results show that the most cited authors are A. D. Teece, Margaret Peteraf, and Sohvi Leih published in journals.sagebup.com b. Zhao Huang and Morad Benyoucef are the most cited authors, Elsevier is the largest publisher crequent words are smes,, medium sized enterprise, marketing, and digital marketing. Bibliometric analysis supports scientific explanations between probability theory and evidence. Research on Digital Marketing in MSMEs can be developed more widely on themes that appear few and have not been included much in research in the last ten years. Therefore, this study is expected to serve as a reference for further research.

Citation of Lithuanian biochemists' works in the world literature

In the article, a short review of Lithuanian authors' research is provided, using the "Science Citation Index." The analysis of citation data is conducted to determine in which countries Lithuanian biochemists' works are disseminated, the main sources in which these works are most frequently cited, the "half-life" of literature in biochemistry, the most cited authors, and their interactions with scientists both domestically and internationally.

Community influence analysis in social networks

Heterogeneous influence detection across network nodes is an important task in network analysis. A community influence model (CIM) is proposed to allow nodes to be classified into different communities (i.e., clusters or groups) such that the nodes within the same community share the common influence parameter. Employing the quasi-maximum likelihood approach, together with the fused lasso-type penalty, both the number of communities and the influence parameters can be estimated without imposing any specific distribution assumption on the error terms. The resulting estimators are shown to enjoy the oracle property; namely, they perform as well as if the true underlying network structure were known in advance. The proposed approach is also applicable for identifying influential nodes in a homogeneous setting. The performance of our method is illustrated via simulation studies and two empirical examples using stock data and coauthor citation data, respectively.