Publication Patterns Research Articles

Analyzing and Assessing a Library Collection Using Faculty Citations Via OpenAlex and R

Objective – Citation analysis is becoming a popular means of analyzing and assessing library collections due to its relatively unobtrusive nature and the growing accessibility of citation data. The primary goal of this study was to assess whether the library at the University of Scranton is successfully meeting the research needs of faculty based on analysis of faculty publication and citation data from OpenAlex’s application programming interface. Secondarily, this study analyzed faculty publication and citation patterns to help identify opportunities for the library to better support faculty in their research and publishing. Methods – This case study focused on a citation analysis of the University of Scranton’s faculty publications from 2013 to the present. Using OpenAlex and R computing language as non-proprietary sources of data and data analysis, faculty publications and citations were examined and compared to current library holdings. Results – Overall, 16,786 unique citations from 1,045 unique faculty publications were examined and compared to a list of current library holdings. Findings concluded that approximately 65% of citations were available through library holdings. Further analysis of faculty publication practices suggested that there are a growing number of faculty publishing open access which indicates that there may be additional opportunities to support faculty in this area. Conclusion – While this case study represented specific needs and use cases at the University of Scranton, the ultimate importance of this study is the process itself. The use of non-proprietary tools and data sources like OpenAlex and R create exciting new opportunities for others who wish to conduct similar studies at their own institutions without relying on proprietary tools and data sources or resorting to more labor-intensive methods.

Scientific mapping of the use of lecithin in breast cancer therapy

Lecithin, a natural phospholipid, possesses health-beneficial properties, recognized for its potential in breast cancer treatment. In recent years, it has been utilized in systems to enhance chemotherapy effectiveness, increase tumor penetration, and reduce side effects, offering less invasive and economically viable treatments. The aim of this study was to identify publication trends and patterns, leading journals, prominent authors, frequent keywords, and the influence of collaboration among researchers and institutions in scientific production on the topic. Employing a scientometrics approach, the study involved rigorous criteria for material search and selection to ensure data quality. A total of 241 studies were identified, with 81 selected after detailed filtering. Results indicate a rise in publications post-2012, peaking in 2020. 411 authors were identified, with Le, Ngoc Thuy Trang standing out, alongside three high-impact factor journals dominating the field. Strong collaboration among researchers and institutions was observed. Results were interpreted and discussed within the broader context of scientometrics. The study faced limitations, such as exclusive use of Bibliometrix potentially restricting analysis scope, and reliance on different databases yielding incomplete information. This study suggests future research to explore other natural substances with therapeutic potential and develop new methodologies to optimize drug delivery.

The internationalization of Chinese social sciences research: publication, collaboration, and citation patterns in economics, education, and political science

ABSTRACT Despite recent Chinese government policies aiming to balance international and national publishing patterns, the enduring impact on China's global engagements, publications, and collaborations remains uncertain. Analyzing 8,962 publications from Scopus between 2016 and 2020, the paper assesses publication, collaboration, and citation patterns among the top 500 productive China-affiliated scholars within Economics, Education, and Political Science. Results indicate that publications are primarily in Western journals, with Political Science often focusing on China-specific issues, suggesting potential ‘silos’. Co-authorship patterns show Western dominance, with diverse international collaborations in Education and Economics, but more national-focused in Political Science. The paper concludes with discussions on practical implications and study limitations.

A bibliometric analysis of Indonesian stem education research (2019–2023): Trends, contributors, and future directions

Science, technology, engineering, and mathematics (STEM) education is a global priority, but effective implementation faces challenges. This bibliometric study analyzed the results of Indonesian STEM education research to elucidate publication and contributor patterns. The Scopus database was searched for Indonesian STEM education publications from 2019–2023 and produced 52 documents from 23 sources. The analysis found a negative average growth rate of −5.43%, with a peak of 14 releases in 2020, possibly related to the COVID-19 pandemic. Although the output was relatively limited, the diversity of sources suggests wide-ranging interest. The leading authors were identified based on their productivity and impact on citation, with Wahono. emerging as the most influential worldwide. Universitas Pendidikan Indonesia was an institutional leader. The Journal of Physics Conference series dominated the contributions and emphasized the role of conference proceedings. Examination of the citations and text frequencies revealed key themes that include technology, engineering, pedagogy, and skills of the 21st century. Several widely cited works ensured international visibility. In general, this bibliometric analysis quantitatively mapped the landscape of Indonesian STEM education research, finding a decline in performance but a strong foundation of committed institutions and authors. The sustainability of production and impact requires targeted policies based on insight into existing strengths, productive scholars, and influential publications. The results provide an empirical basis for practices and policies for the effective development of STEM education in Indonesian schools.

Correction: A comprehensive scan of psychological disciplines through self-identification on Google Scholar: Relative endorsement, topical coverage, and publication patterns

Correction: A comprehensive scan of psychological disciplines through self-identification on Google Scholar: Relative endorsement, topical coverage, and publication patterns

Identification of latent classes of randomized controlled trials based on integrity and reporting characteristics.

Reliable, well conducted and adequately reported research is essential for decision-making. This study uses an exploratory clustering approach to identify patterns (subgroups) of trials based on research conduct and reporting characteristics to better understand heterogeneity. Describing features of these subgroups may help elucidate mechanisms of poor planning and reporting that can be acted upon by the research community to improve the research practices. Estimating the impact of trials from specific subgroup classes on pooled treatment effects and overall certainty of evidence may inform conduct and interpretation of systematic reviews in the future. Our study used data from 456 randomized controlled trials included in our recent update of a Cochrane review, Exercise treatment for chronic low back pain, to explore groups of trials that have various characteristic patterns of research planning, conduct, reporting and publication. Using latent class analysis, we explored the patterns that exist in 43 characteristics of trial planning, conduct, reporting and publication characteristics. We identified a four-class model as the best fit for the data; classes were labelled based on the patterns of characteristics that emerged: 1. Well Resourced & Thorough, 155 trials (34%), 2. Dated, 92 trials (20%), 3. Fundamental Deficiencies, 102 trials (22%), and 4. Research Waste, 107 trials (24%). The characteristics that best differentiated the classes were: trial registration status, institution/ethics board approval, conflict of interest reported, and reporting of adverse events. There were no significant differences for estimates of treatment effect in the four classes for all comparisons of treatments with pain intensity outcomes; two classes overestimated functional limitations outcomes for exercise compared to no trial treatment and compared to other conservative treatments. Using a single characteristic to define quality or trustworthiness is no longer sufficient; individual 'problematic' characteristics could be found in all four classes. This novel research could be used as a framework for further exploration of research integrity issues in the health sciences, and the development of interventions to improve our evidence-base, taking into consideration the research motivations and means of trialists.

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Post-COVID-19 Publication Opportunities for Medical Academics in Top Indian Medical Institutes: An Investigation into Productivity, New Venues, and Collaborations

ABSTRACT The COVID-19 pandemic has reshaped research dynamics in Indian medical academia, prompting shifts in publication patterns. Using data from 2017 to 2022 from Scopus, encompassing pre- and post-pandemic periods, the study focuses on the top six medical institutes in India. Despite pandemic challenges, using scientometric analysis, this study reveals increased research productivity, albeit with a decline in book-based scholarly communication. Notably, there’s been a significant rise in unique publication venues post-COVID-19, indicating a shift in publishing preferences. Additionally, medical academics have initiated collaborations with new countries, signaling proactive responses to the pandemic. These findings have crucial implications for medical institutes’ libraries. Libraries, as pivotal information hubs, must adapt to support researchers amidst evolving publication trends. Understanding these shifts enables libraries to tailor services, facilitate access to resources, and foster scholarly communication effectively.

Data augmentation based on large language models for radiological report classification

The International Classification of Diseases (ICD) is fundamental in the field of healthcare as it provides a standardized framework for the classification and coding of medical diagnoses and procedures, enabling the understanding of international public health patterns and trends. However, manually classifying medical reports according to this standard is a slow, tedious and error-prone process, which shows the need for automated systems to offload the healthcare professional of this task and to reduce the number of errors. In this paper, we propose an automated classification system based on Natural Language Processing to analyze radiological reports and classify them according to the ICD-10. Since the specialized use of the language of radiological reports and the usual unbalanced distribution of medical report sets, we propose a methodology grounded in leveraging large language models for augmenting the data of unrepresented classes and adapting the classification language models to the specific use of the language of radiological reports. The results show that the proposed methodology enhances the classification performance on the CARES corpus of radiological reports.

The landscape of Industry 4.0 and business model innovation: A scientometric analysis of research trends and emerging patterns

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.

Exploring outsourcing dynamics: a comprehensive bibliometric analysis of emerging markets

PurposeThis research aims to enhance understanding of outsourcing dynamics from emerging markets by employing a bibliometric analysis of relevant scholarly articles. The study explores publication patterns, citation networks, author collaborations and contributions by different countries, offering insights for researchers and practitioners investigating outsourcing in these markets.Design/methodology/approachA comprehensive bibliometric analysis was conducted using 197 articles from the SCOPUS database, spanning January 1999 to June 2023. Established bibliometric techniques were utilized to analyze publication patterns, citation networks, author collaborations and country contributions, identifying key research themes and influential authors and institutions.FindingsThe study identified eight distinct research clusters, highlighting interdisciplinary collaborations, influential authors like Thomas Frisanco and Erdener Kaynak, and significant contributions from the United States and the UK. Notable organizational collaborations include the Hong Kong Polytechnic University and the University of Miami. These findings provide valuable insights into outsourcing practices and research trends in emerging markets.Research limitations/implicationsThe study acknowledges limitations such as sampling bias, language bias and publication bias, which may affect the generalizability of the findings. Future research should strive for more diverse and representative samples, including non-English publications and studies with non-significant results, to provide a more comprehensive understanding of outsourcing dynamics.Practical implicationsThe findings offer practical insights for organizations considering outsourcing from emerging markets, guiding strategic decision-making and policy development. Understanding publication patterns and research themes can help organizations navigate location selection, vendor management, risk mitigation and performance evaluation in these markets.Social implicationsOutsourcing can significantly impact employment, income generation, skill development and overall economic development in emerging markets. Policymakers can use these insights to foster business growth, infrastructure development and investment incentives, contributing to economic growth and employment opportunities.Originality/valueThis study provides a comprehensive bibliometric analysis of outsourcing from emerging markets, filling a gap in the existing literature. The insights gained can guide future research, inform strategic decision-making and enhance the understanding of outsourcing dynamics in these rapidly evolving markets.

Unveiling Trends, Themes and Collaborative Networks of Research on Sustainable Development Goals in India: A Bibliometric Analysis

The goal of global sustainability has acquired substantial attention as a result of the United Nation’s Sustainable Development Goals (SDGs), which serves as a comprehensive framework for addressing major societal and environmental concerns. This study applies bibliometric analysis to assess the landscape of research articles on the SDGs. The study aims to uncover significant trends, thematic areas, influential authors, and collaborative networks within the discipline through a comprehensive examination of an extensive body of scholarly literature. The analysis entailed gathering data from prominent academic database Scopus, spanning a specific time frame. Utilizing bibliometric technique, the study assesses patterns in publication, citation, and collaboration to provide a comprehensive understanding of the research landscape. The analysis dives into the frequency of SDG-related research outputs, identifies theme clusters and goal’s interconnections, and identifies significant works affecting the conversation. The results of this bibliometric investigation reveal evolving trends in SDGs research, identify emergent and established focal points, and shed light on the changing environment of research collaboration. The analysis focused on the progress made in tackling the SDGs across several academic disciplines, while also revealing gaps and potential for future research. The findings of this bibliometric analysis have significance for policymakers, researchers, and practitioners working to advance sustainable development efforts and strengthen the collective knowledge base in support of the SDGs.

Bibliometric perspectives in mapping the progress of river water quality index studies

ABSTRACT This paper employs a bibliometric analysis to guide researchers in forecasting the trajectory of research related to the development of the river water quality index. It scrutinizes issues such as variable selection, weight allocation, and the formulation of the function, which are key concerns in the river WQI development. This study also addresses gaps in the existing literature by examining how information about river water quality indices is disseminated, focusing on aspects such as contributing research and prevalent keywords. While bibliometric analysis has been widely applied in various research domains, its use in developing river water quality indices has been comparatively limited. To address this, the current study conducts a bibliometric analysis to identify trends in index creation and to assess the current state of the literature in the field of water quality indices. This examination includes a comprehensive review of papers published in the Scopus database from 1973 to 2024, relevant to the river WQI. Utilizing tools such as R, VOSviewer, and BiblioMagika, the research uncovers patterns in publication among authors and organizations. A finding is that China and India have emerged as leading contributors to publications in this field, indicating their pivotal role in the advancing river water quality index research.

Bibliometric Review: Current Trends and Future Direction of Public Sector Budgeting

Research aim: This study conducts a bibliometric analysis to delve into the current state and future direction of public sector budgeting globally by scrutinising the overall publishing patterns and knowledge landscape surrounding the evolution of public sector budgeting research. Design/ Methodology/ Approach: A bibliometric analysis technique was employed to study the development of scientific publications about public sector budgeting. The Scopus database was selected as the main database, and all the data were collected as of November 3, 2023. The first stage involved identifying all relevant documents from the Scopus database. Inclusion and exclusion criteria resulted in a total of 1,925 documents within the period between 1913 to November 3, 2023. The bibliometric analysis of public sector budgeting yielded significant insights into publication patterns, influential literature, notable authors, highly referenced works, and prevalent themes within this domain. Research finding: This analysis reveals a consistent growth in published material over time, with the United States emerging as the primary contributor in terms of volume. Distinguished scholars like Hou Yilin and Richard J. Cebula have made substantial impact, garnering noteworthy citations for their contributions. Moreover, the review outlines prominent themes in public sector budgeting, underscoring the multifaceted nature of research within this crucial area. Practitioner/ Policy implication: The study findings offer valuable perspectives for researchers, practitioners, and policymakers, enhancing comprehension of the challenges and prospects in public sector budgeting. Furthermore, longitudinal studies tracking the evolution of themes and trends in public sector budgeting over time could offer valuable insights into the changing landscape of budgetary practices in the public sector. Additionally, comparative studies across different countries and regions could provide a more nuanced understanding of the diverse approaches to public sector budgeting and the factors influencing budgetary practices in various contexts. Research limitation: The analysis was based on a specific set of data from the Scopus database, and therefore, might not capture all publications in the field of public sector budgeting. Additionally, the review focuses on quantitative analysis of publication trends and citations; thus, the qualitative aspects of the included works are not fully explored. Furthermore, the study is limited to the available data and might not encompass the most recent publications in the field.

Mapping the Research Landscape of Ordinary Differential Equations through Bibliometric Analysis

Ordinary Differential Equations (ODEs) are a fundamental field used for mathematical modelling in wide-ranging applications. This research visually represents the research landscape of ODEs by utilizing bibliometric analysis and social network analysis. We analysed 1,849 documents related to topic developments, patterns in publications, and collaborative networks spanning the years 2019 to 2024. Consequently, numerous papers have been published by authors from the United States, China, and Russia, where research activity is significantly focused. The analysis identifies the articles with the highest citation rates, as well as the important authors and universities that are at the centre of publishing on ODEs. Furthermore, we display the collaborative network of the research group and countries using the VOSviewer application. In conclusion, the study highlights the current state and progress of ODE research by identifying recent developments and intriguing topics for future exploration. This paper serves as a valuable reference for researchers seeking to understand the impact and progress of ODE investigations within the broader mathematical domain.

Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis.

As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies. From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others. Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD. https://doi.org/10.23641/asha.27162564.

The influence of student voice on teacher professional learning: A systematic literature review

AbstractStudent voice has the potential to prompt changes in teaching and foster teacher development; however, it is underutilised in teacher professional learning. Research on its impact is emerging, and a systematic review is needed to identify trends and themes. This review investigates current trends and themes in the influence of student voice on teacher learning from 2012 to 2023 within K‐12 education. Analysis of 19 empirical papers revealed irregular patterns of publications, a predominant emphasis on secondary education, and qualitative methodologies. Participatory research designs emerged as practical approaches, engaging teachers in inquiry processes and collaboration. Student feedback led to a transformative process for many teachers, prompting them to reflect and change their practice. Supportive school structures and leadership are emphasised for embedding student feedback in classrooms, engaging teachers and students in collaboration, and overcoming obstacles. This review demonstrated that when student voice is valued and acted upon, it can stimulate teacher development. A proposed model for teacher development incorporating student voice consists of three interconnected levels—school leadership support, collaborative practice and learning outcomes—ultimately contributing to overall school improvement. Recommendations for future research are discussed.

INPUTS AND OUTPUTS OF HIGHER EDUCATION: A READING OF THE ALGERIAN HIGHER EDUCATION SYSTEM IN THE LIGHT OF THE KNOWLEDGE INDEX

Objective: The purpose of this research is to assess and understand the condition and difficulties facing the education system, in Algeria in light of the knowledge economy, with particular focus on the alignment between university outputs and labor market demands. Theoretical Framework: The study is based on the concept of the knowledge based economy model which underscores the pressing requirement for institutions to adjust to progress and changing economic landscapes. It examines the relationship between higher education outcomes and economic development in developing nations. Method: The study employs an analytical approach to evaluate Algeria's higher education system, examining public spending patterns, graduate employment rates, and the alignment between academic programs and market needs. Results and Discussion: The results indicate that with rises, in funding, for public education and enhancements educational inputs, Algeria faces persistent challenges: (1) high unemployment rates among university graduates, (2) misalignment between academic training and industry requirements, and (3) limited focus on entrepreneurship and innovation in university curricula. Research Implications: The study recommends strategic reforms including: (1) reorienting universities toward entrepreneurship education, (2) strengthening research and innovation capabilities, and (3) developing learning environments that foster creativity and practical skills. These changes aim to shift from a purely employment-focused model to one that emphasizes job creation. Originality/Value: In this study a thorough examination of the education system, in Algeria is presented within the framework of the knowledge economy., offering evidence-based recommendations for transforming universities from institutions that prepare job seekers to centers that create job creators, thereby contributing to sustainable economic development.

Collaboration in The Scientific Production of Articles on E-Learning in Spain

Objective: The objective of this study is to determine the rate of collaboration among Spanish researchers in the field of e-learning and to know the type of collaboration that takes place. Theoretical Framework: In this topic we present the background of what e-learning means and indicate previous bibliometric works on this subject in order to have a vision of the international panorama with respect to the publication patterns on this subject. Method: The methodology adopted for this research includes bibliometric techniques focused on content analysis. The information was taken from the SCOPUS database and the variables chosen were the name and number of authors, institutional affiliation and the countries to which they belong. Results and Discussion: The results obtained revealed that there is a pattern of collaboration mainly of a local nature and that international collaboration is beginning to emerge, especially with the USA and the United Kingdom. It was found that the Spanish university with the highest productivity in this field is the Universidad Oberta de Catalunya. Implications of the research: This study has made it possible to identify the most prolific Spanish authors as well as the universities with the highest productivity. It should be noted that there is good collaboration with some Latin American countries. Originality/value: This study contributes to know and highlight the role of Spain in the international dissemination of e-learning research.

Engaging with underserved communities during times of crises: A computational analysis of social media interactions with government information about COVID-19 economic relief programs

Racial and social inequalities in the digital landscape remain pressing concerns in society. Previous research on government efforts to address digital inequality has largely focused on improving access to digital infrastructure and literacy. However, there is a severe shortage of research that examines how underserved communities engage with government information on relief measures and policies during crises. This study fills these gaps by drawing insights from three bodies of literature – digital inequality, the situational theory of problem solving, and fear. We investigate how underserved communities, such as low-income individuals, communities of color, and rural populations, interacted with government information about COVID-19 economic relief programs designed to mitigate the adverse effects of crises, and how fear influenced public engagement patterns. We analyzed Facebook posts (N = 28,887) containing links (URLs) to government websites on COVID-19 economic relief programs from 2020 to 2022. Our findings show that posts shared within underserved communities generated higher levels of engagement (shares, comments) compared to those shared in nonunderserved communities. This indicates that underserved communities showed great interest in and participation with government information about relief programs during the pandemic. Notably, engagement significantly decreased when fear was expressed in discussions, and such a pattern was more pronounced in underserved communities than in nonunderserved ones. These results offer valuable insights into the communication dynamics of underserved communities during crises, contributing to a deeper understanding of digital inequality within the policy context.