Co-authorship Network Research Articles

Assessing business readiness for Industry 4.0: A bibliometric analysis of research trends

This research provides a comprehensive analysis of the state of readiness assessment for enterprises in the context of Industry 4.0. It examines the most influential authors, countries, and journals in this field, identifies key research themes, and explores the evolving landscape of Industry 4.0. By bibliometric analysis, this study uncovers the intricate network of co-authorship and co-citation among researchers, journals, and countries. The analysis underscores the dominance of China in terms of both publication volume and citation impact, driven by its strategic "Made in China 2025" plan. The United States and India also make substantial contributions, reflecting the global nature of Industry 4.0 research. The key themes in this area include digital transformation, IT innovations, production, and supply chain management. This study pinpoints three distinct clusters of journals, demonstrating the multidisciplinary nature of Industry 4.0. In addition, a co-occurrence analysis of keywords highlights the most prevalent themes, including Industry 4.0, frameworks, big data, and performance. This research offers valuable insights into the research landscape, informing scholars and industry stakeholders of key players, trends, and emerging areas in assessing enterprise readiness for Industry 4.0.

A Systematic Literature Review and Bibliometric Analysis of Green Brand Image From 2000 to 2022

The growing scholarly attention on green brand image may be traced to the prevailing trend of organisations and customers increasingly prioritizing environmental sustainability. In order to assist these scholars, our research offers a bibliometric examination of the body of literature pertaining to the green brand image spanning the years 2000 to 2022. The Scopus database screen is used to locate publications categorized by year, papers with the highest number of citations, authors with the highest productivity, countries, and organisations. The programme known as Vos viewer is designed to analyse co-authorship, inter-country co-authorship networks, and the incidence of keywords. The bibliometric study reveals that in 2017, there was a significant increase in the publishing of publications focused on green brand images. The paper titled "The Impact of Marketing on Consumer Purchase Decisions in the Context of Pro-environmental Products" is widely cited. Chen Y-S, a very active researcher from The Chinese University of Hong Kong, is the most prolific author in the field of green brand image. The Journal of Cleaner Production, Environment Development and Sustainability, published by Elsevier, and Sustainability (Switzerland), published by MDPI, have the highest number of papers on green brand image. This analysis provides guidance to researchers entering the field of green brand image by presenting information on renowned journals, authors, organizations, and nations in the field of environmental sustainability, as well as commonly used keywords in studies on green brand images.

Mapping research trends on development of scientific literacy assessment: A bibliometric analysis

This study conducts a bibliometric analysis to map the research trends and intellectual landscape surrounding the development of scientific literacy assessments in educational contexts from 2019 to 2023. Scientific literacy, which encompasses the ability to comprehend, evaluate, and apply scientific knowledge, has emerged as a crucial competency in science education. However, existing assessment tools often prioritize factual recall over deeper conceptual understanding and critical thinking abilities. To address this gap, there is a need for innovative assessment approaches that holistically evaluate students' scientific literacy across multiple dimensions. Through a systematic analysis of 185 relevant publications from the Scopus database, this study investigates the numerical distribution of literature by year, document type, journal, author, institution, and country. Additionally, it explores the prominent themes, evolving topic trends, and emerging clusters within the scientific literacy assessment research domain. Bibliometric techniques, including citation analysis, co-authorship networks, and keyword co-occurrence mapping, are employed to provide a comprehensive perspective. The findings reveal a steady increase in publications focused on scientific literacy assessments, reflecting the growing recognition of this research area. The analysis identifies highly influential journals, articles, authors, and institutions actively shaping the field. Furthermore, it highlights key research themes, such as secondary education, teacher professional development, and information literacy. However, the integration of scientific literacy assessments with artificial intelligence remains an underexplored area, presenting opportunities for future research.

An attribute-based Node2Vec model for dynamic community detection on co-authorship network

An attribute-based Node2Vec model for dynamic community detection on co-authorship network

An Analysis of the Authorship and Co-authorship Networks of the Brazilian Human-Computer Interaction Conference

In Brazil, the Brazilian Symposium on Human Factors in Computing Systems (IHC) gathers the scientific community of researchers interested in the field of Human-Computer Interaction since 1998, being the main Brazilian event in this sub-area of Computing. Over twenty-one editions, the IHC received works from researchers from different regions of the country who, over the years, have been building their own co-authorship relationships with the other authors of the Symposium. In this context, this paper analysed the IHC from the perspective of those who helped to consolidate this important national scientific event, as well as in the expansion of the Human-Computer Interaction area in the Brazilian scenario, that is, its researchers-authors. In total, 1,443 authors were identified and analysed in the study presented in this work, which considered 873 publications of three IHC tracks: Full Papers, Short Papers, and Innovative Ideas and Emerging Results. Issues related to the publications and to the co-authorship relationships of these authors over the years and in the different article tracks of the IHC were considered. In order to describe their research trajectories within the IHC itself, the study presents, in different scales of time, how these authors evolved in relation to their contributions over time. In addition, this paper analyses how the authors contributed with each other and originated the complex collaboration network of the IHC. For this, co-authorship networks and groups of authors who published together were explored, aiming to clarify the collaborations between these authors, as well as how they evolved until the edition of 2022. In this sense, this work seeks, with each research question, to simplify the presentation of results through different visualizations, which were planned and created to describe information that are not clearly evident when observing the IHC publications in a “disconnected” manner. The results of this study are revealed, described and analysed under different perspectives, as well as discussed in details in this paper.

Powder metallurgy processing of high entropy alloys: Bibliometric analysis and systematic review

Abstract Research attention in powder metallurgy (PM) processing of high-entropy alloys (HEAs) is rising. Some reviews have been published but a detailed historical analysis to identify the thematic research areas and prospective future research areas is lacking. Therefore, this study presents a bibliometric literature analysis of PM-processed HEAs by mapping and clustering 700 articles published between 2007 and August 2022 in the Scopus database. The most prolific authors, their collaborators, institutions, and most preferred journals publishing PM-HEA works are identified and mapped. Publication trend shows that significant research attention in the PM processing of HEAs began to gain traction in 2016. The top three journals in this field are Journal of Alloys and Compounds, Materials Science and Engineering A, and Intermetallics. However, co-authorship network analysis does not reveal significant inter-institutional research collaboration indicating that strengthening this area could help to accelerate scientific discovery, enhance technology transfer, and commercialization of HEA products. Based on the co-occurrence frequencies of author keywords, popular research directions are identified, and a systematic review of emerging functional applications is undertaken. This work provides a comprehensive visual reference guide for researchers to deepen their knowledge of this field and delivers insight into prospective future research opportunities to stimulate further ground-breaking works.

In the eye of the promoter? How faculty ratings of attractiveness matter for junior academic careers

We investigate whether junior researchers’ attractiveness matters for obtaining an academic job post-PhD in a specific field within the social sciences, using faculty ratings on photographs along with data on publications, quality of PhD-granting university and employment type. The use of faculty ratings rather than external rating platforms allows for a closer match to a real-life academic setting. We find a significant association between faculty-perceived attractiveness and the probability of junior female researchers being in academic positions. A one standard deviation increase in the faculty attractiveness rating is associated with an increase in female junior researchers’ academic job holding by 25 % of the mean. This association is equally strong for the full sample of females and a seasoned subsample of females, observed at least 5 years post-PhD. We show that faculty ratings are different from general perceptions, the latter being irrelevant for junior researchers’ academic job holding. We also find that research productivity or access to high-quality coauthorship networks cannot explain the association. Collegiality, inferred using facial action coding of the photographs, is associated with the probability of females being in academia, but does not alter the association between faculty perceived attractiveness and females’ chances of an academic job post-PhD.

EVOLVING TRENDS OF TECHNOLOGICAL INNOVATION IN EDUCATIONAL ASSESSMENT: A BIBLIOMETRIC ANALYSIS

This study is a bibliometric analysis investigating the incorporation of technology in educational assessment. This study aims to analyze the influence of these developments on assessment methods in schools, considering the increasing integration of technology due to the availability of digital tools and the emphasis on data-driven, individualized learning. This study examines the current patterns in academic research, identifies important topics, and investigates the impact of technological breakthroughs such as Artificial Intelligence (AI) on teaching methods and educational policies. The objective is to provide guidance for future educational strategies and assessments. We utilize a bibliometric methodology, utilizing the Scopus database to extract pertinent publications. The inclusion criteria pertain to works published from 2014 to 2023, with a specific focus on Technological Innovation in Educational Assessment. Analyzed are key bibliometric indicators, including publishing patterns, prolific authors, and subject distribution. The study utilizes visualization methods to depict co-authorship networks, citation patterns, and theme clusters. Co-authorship networks and citation patterns provide insights into collaborative efforts and the spread of impactful research. This bibliometric analysis sheds light on the changing scholarly landscape of Technological Innovation in Educational Assessment. The detected tendencies enhance our comprehension of the intricate and diverse characteristics of research in this sector. Given the ongoing importance of technology innovation in education, the findings of this study provide a basis for future research, policy development, and educational methods within a country.

Portfolio analysis of global tobacco control research funding at the National Cancer Institute, 2000-2019.

Research in low- and middle-income countries (LMICs), where the majority of global tobacco users reside, is critical to addressing the global tobacco epidemic. This analysis describes the global tobacco control research portfolio funded by the National Cancer Institute from fiscal years 2000 to 2019. We used the National Institutes of Health Query, View, Report database to identify extramural grants relevant to global tobacco control research. Abstracts were analyzed to describe grant characteristics, including topic areas, tobacco products, countries, and regions of focus. Bibliometric and co-authorship network analyses were performed for publications associated with relevant grants. Of the 93 relevant grants with foreign (non-US) involvement, the majority (83.9%) supported research in upper and lower middle-income countries. The majority of grants (86.0%) focused on cigarettes, with a small subset of grants addressing smokeless tobacco, waterpipe use, or other non-cigarette products. Most grants focused on at least one of the six tobacco control policy measures in the World Health Organization MPOWER package; almost half (48.4%) focused on monitoring tobacco use and around one-third (32.3%) focused on offering tobacco cessation treatment, while other MPOWER measures received less attention in the research portfolio. While most of these grants, and the funding initiatives that supported them, emphasized research in low- and middle-income countries (LMICs), only 3 of 93 grants were awarded directly to LMIC-based institutions. There is a critical need for research to develop and test strategies to adapt, implement, and scale up evidence-based interventions across diverse LMIC settings. This study identified gaps in research activity that should be addressed to strengthen global tobacco control research capacity.

A bibliometric analysis of the quantitative schema therapy literature.

The evidence base for schema therapy has evolved significantly since it was first developed by Jeffrey Young in the 1990s. The aim of this bibliometric analysis was to summarize the trends and characteristics of the quantitative literature on schema therapy. PsycINFO, PubMed and CINAHL Complete databases were last searched on 1 June 2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement to identify peer-reviewed journal articles written in English that reported on original quantitative research on schema therapy or schema therapy constructs. NVivo was used to complete a descriptive analysis of the bibliographic, sample and study characteristics, and a coding framework was applied to capture the aspect of the schema therapy model that was the focus of each study, as well as the study context (e.g., the population or outcomes under investigation). SciVal was used to complete citations and authorship analyses. VOSviewer was used to examine co-authorship networks. A total of 704 quantitative studies on schema therapy were published by 483 unique first authors between 1994 and mid-2023. Studies predominantly used correlational designs with small samples (Mdn N = 153) of mostly females aged 18 years or older. The articles tended to focus on early maladaptive schemas, rather than schema domains or schema modes. Schema therapy and its concepts were most frequently studied in the context of depression and personality disorders. SciVal analyses indicated that, on average, articles were cited 27 times, with a Field Weighted Citation Impact of 1.02. Schema therapy research output appears to have slowed in recent years and several critical research gaps were evident. Areas of high priority for future research include schema modes and coping responses, and the use of developmental and longitudinal designs to evaluate several key causal assumptions in the theory underpinning schema therapy.

Research Trends and Co-author Network Analysis of the Journal of the Korean Home Economics Association: Articles Published from 2010 to 2022

The purpose of this study was to analyze the research trends and co-author networks of academic articles published in the Journal of the Korean Home Economics Association from 2010 to 2022. The network analysis was conducted using Excel and NetMiner 4.4, and the results were as follows. First, the number of published articles has been maintained at around 40 per year since 2019. By field, most articles were published in the field of child studies and family studies, followed by consumer studies, home management, clothing studies, home economics education, food and nutrition, and housing. The research methods were primarily quantitative (71.61%). Second, the most common keywords in the titles of the published articles were “influence” and “relationship”, with “influence”, “consumer”, “mediating effect”, “parent”, and “control” identified as influential keywords. Third, the published articles were categorized into nine topics based on subject matter, while the number of topic types varied by year. Fourth, the total number of authors of the 627 articles was 712, with 1.92 authors per article, as well as the number of authors who published two or fewer articles accounted for 85.5% of the total. By institution, Yonsei University had the highest number of authors and the highest number of published articles, while Korea National Open University played a leading role in the network of co-authors by institution. This study is significant in providing basic data for the future development of the Korean Home Economics Association and the field of home economics.

Co-authorship network analysis of North Korean chemistry researchers based on issues of <i>Chemistry and Chemical Engineering</i> published from 2008 to 2022: a bibliometric study

Purpose: This study conducted a comprehensive analysis of North Korean domestic journals, using scientific quantification methodologies to identify prominent researchers and research areas within the field of chemistry.Methods: Data were collected from the journal Chemistry and Chemical Engineering published in North Korea. Through an analysis of co-authorship relations and literature reviews of papers authored by researchers who were highly influential in research networks, core research areas were identified.Results: The researcher with the highest number of publications in the given period was Yong-Chol Lee, with 31 publications, followed closely by Gyun Kim, who also demonstrated significant research activity. When focusing on the last 5 years, Myeong-Cheol Hong emerged as a prominent figure. Yong-Chol Lee has expertise across diverse fields of chemistry, including fine chemicals, biochemistry, and mineral materials. Gyun Kim, in contrast, is recognized for his in-depth knowledge of organics, enzymes, processes, catalysis, fine chemicals, and industrial chemistry. Myung-Cheol Hong’s research primarily centers around organic chemical synthesis within the fine chemical domain. All three researchers are making substantial contributions to the chemical industry.Conclusion: The findings of this study provide valuable insights into research trends in the field of chemistry in North Korea and contribute to a broader understanding of the discipline’s internal knowledge structure within the global academic community. This research is anticipated to be especially useful for scholars who are analyzing bibliographic information pertaining to North Korea.

Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics

PurposeThe study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.Design/methodology/approachBy conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.Findings(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.Originality/valueThis study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

A Bibliometric Analysis of Research on ChatGPT in Education

ChatGPT has become a prominent tool for fostering personalized and interactive learning with the advancements in AI technology. This study analyzes 212 academic research articles indexed in the Scopus database as of July 2023. It maps the trajectory of educational studies on ChatGPT, identifying primary themes, influential authors, and contributing institutions. By employing bibliometric indicators and network analysis, the study explores collaboration patterns, citation trends, and the evolution of research interests. The findings show the exponential growth of interest in leveraging ChatGPT for educational purposes and provide insights into the specific educational domains and contexts that have garnered the most attention. Furthermore, the study reveals the collaborative dynamics and intellectual foundations shaping the field by examining co-authorship and citation networks. This bibliometric analysis contributes to a comprehensive understanding of the current state of ChatGPT research in education, offering researchers and practitioners valuable insights into evolving trends and potential future directions for this innovative aspect of AI and learning.

Wearable biosensors for human health: A bibliometric analysis from 2007 to 2022.

This study aimed to determine the status of scientific production on biosensor usage for human health monitoring. We used bibliometrics based on the data and metadata retrieved from the Web of Science between 2007 and 2022. Articles unrelated to health and medicine were excluded. The databases were processed using the VOSviewer software and auxiliary spreadsheets. Data extraction yielded 275 articles published in 161 journals, mainly concentrated on 13 journals and 881 keywords plus. The keywords plus of high occurrences were estimated at 27, with seven to 30 occurrences. From the 1595 identified authors, 125 were consistently connected in the coauthorship network in the total set and were grouped into nine clusters. Using Lotka's law, we identified 24 prolific authors, and Hirsch index analysis revealed that 45 articles were cited more than 45 times. Crosses were identified between 17 articles in the Hirsch index and 17 prolific authors, highlighting the presence of a large set of prolific authors from various interconnected clusters, a triad, and a solitary prolific author. An exponential trend was observed in biosensor research for health monitoring, identifying areas of innovation, collaboration, and technological challenges that can guide future research on this topic.

Measuring Innovation and Collaboration System using Big Data: A Case Study about Ho Chi Minh City

Abstract Academic, government, and industry researchers have attempted to build some theoretical and empirical ways to understanding how to innovate and interact in data- driven contexts in recent years. However, it does not appear to go above and beyond our expectations in terms of offering fresh views and resources to investigate this trend in terms of measurement. In this brief study, I show how Dimensions.ai, a new academic big data resource, can be used to assess knowledge-based innovation processes in emerging and transitional countries like Vietnam. This article investigates creativity and collaboration in Ho Chi Minh City using a “n-tuple helix” framework based on Loet Leydesdorff’s sociological theory of communication. From a social network standpoint, I argued that a city’s innovation system is inextricably linked to its position in intercity and global co-authorship networks. This shows how the helix model and applications can be utilized to discover a data-driven society.

Telemedicine Research Trends in 2001-2022 and Research Cooperation Between China and Other Countries Before and After the COVID-19 Pandemic: Bibliometric Analysis.

Advancements in technology have overcome geographical barriers, making telemedicine, which offers remote emergency services, healthcare, and medication guidance, increasingly popular. COVID-19 restrictions amplified its global importance by bridging distances. This study aimed to analyze Chinese and global literature data, present new global telemedicine research trends, and clarify the development potential, collaborations, and deficiencies in China's telemedicine research. We conducted bibliometrics and network analyses on relevant documents from the Web of Science database from 2001 to 2022. Data collection was completed on October 30, 2023. Considering COVID-19's impact, 2020 was used as a baseline, dividing the data into 2 periods: 2001-2019 and 2020-2022. The development potential was determined based on publication trends. An international coauthorship network analysis identified collaboration statuses and potential. Co-occurrence analysis was conducted for China and the world. We identified 25,333 telemedicine-related research papers published between 2001 and 2022, with a substantial increase during the COVID-19 period (2020-2022), particularly in China (1.93-fold increase), moving its global publication rank from tenth to sixth. The United States, the United Kingdom, and Australia contributed 62.96% of the literature, far ahead of China's 3.90%. Globally, telemedicine research increased significantly post-2020. Between 2001 and 2019, the United States and Australia were central in coauthor networks; post-2020, the United States remained the largest node. Network hubs included the United States, the United Kingdom, Australia, and Canada. Keyword co-occurrence analysis revealed 5 global clusters from 2001 to 2019 (system technology, health care applications, mobile health, mental health, and electronic health) and 2020 to 2022 (COVID-19, children's mental health, artificial intelligence, digital health, and rehabilitation of middle-aged and older adults). In China, the research trends aligned with global patterns, with rapid growth post-2020. From 2001 to 2019, China cooperated closely with Indonesia, India, Japan, Taiwan, and South Korea. From 2020 to 2022, cooperation expanded to Japan, Singapore, Malaysia, and South Korea, as well as Saudi Arabia, Egypt, South Africa, Ghana, Lebanon, and other African and Middle Eastern countries. Chinese keyword co-occurrence analysis showed focus areas in system technology, health care applications, mobile health, big data analysis, and electronic health (2001-2019) and COVID-19, artificial intelligence, digital health, and mental health (2020-2022). Although psychology research increased, studies on children's mental health and middle-aged and older adults' rehabilitation were limited. We identified the latest trends in telemedicine research, demonstrating its significant potential in China and providing directions for future development and collaborations in telemedicine research.

Infants on the move: bibliometric analyses of observational vs. digital means of screening infant development.

Neurodevelopmental disorders are on the rise, yet their average diagnosis is after 4.5 years old. This delay is partly due to reliance on social-communication criteria, which require longer maturation than scaffolding elements of neuromotor control. Much earlier criteria could include reflexes, monitoring of the quality of spontaneous movements from central pattern generators and maturation of intentional movements and their overall sensation. General Movement Assessment (GMA) studies these features using observational means, but the last two decades have seen a surge in digital tools that enable non-invasive, continuous tracking of infants' spontaneous movements. Despite their importance, these tools are not yet broadly used. In this work, using CiteSpace, VOSViewer and SciMAT software, we investigate the evolution of the literature on GMA and the methods in use today, to estimate how digital techniques are being adopted. To that end, we created maps of key word co-occurrence networks, co-author networks, document co-citation analysis and strategic diagrams of 295 publications based on a search in the Web of Science, Dimensions and SCOPUS databases for: 'general movement assessment' OR 'general movements assessment'. The nodes on the maps were categorized by size, cluster groups and year of publication. We found that the state-of-the-art methodology to diagnose neurodevelopmental disorders still relies heavily on observation. Several groups in classical GMA research have branched out to incorporate new techniques, but few groups have adopted digital means. We report on additional analyses of methods and biosensors usage and propose that combining traditional clinical observation criteria with digital means may allow earlier diagnoses and interventional therapies for infants.

Detecting communities with multiple topics in attributed networks via self-supervised adaptive graph convolutional network

Attributed networks which have topology information and attribute information simultaneously are very common in the real world, e.g., online social networks and co-authorship networks. Community detection in attribute networks is a very valuable research topic. Although there is already a lot of work focused on it, but most of existing approaches still face two key challenges: topology information and attribute information fusion, and multiple topics community detection. To effectively address these challenges, in this paper we devise an approach called SSAGCN, which adopts the self-supervised learning paradigm using the autoencoder architecture. Specifically, SSAGCN comprises three main parts: adaptive graph convolutional network (AGCN) encoder, modularity maximization and dual decoder. AGCN uses two GCNs with shared parameters and the attention mechanism to fuse topology information and attribute information automatically. To drive AGCN encoder to uncover community structure, we select the modularity maximization as the optimization objective. The dual decoder is applied to reconstruct both topology structure and network attributes. By introducing the joint training strategy, SSAGCN is able to discover multiple topics community through the end-to-end manner. Extensive experiments are conducted on nine benchmark attributed networks, and the results illustrate not only the superiority of SSAGCN over state-of-the-art approaches, but also its good ability of community-topic analysis. For the reproducibility, we release the source code at https://github.com/GDM-SCNU/SSAGCN.

Cochrane systematic reviews in dentistry: an Altmetric and network analysis.

Introduction Cochrane systematic reviews (CSRs) play an important role in evidence-based decision-making. Therefore, the present study aimed to determine the social impact of CSRs in dentistry and the inclusivity and diversity of researchers contributing to one of the largest databases in health care research.Methodology The Altmetric and bibliometric data for CSRs in dentistry were obtained through Altmetric Explorer and the Dimensions database and were analysed to determine the trends. Furthermore, the correlation between the number of citations and the Altmetric Attention Score (AAS) was identified using Spearman's correlation co-efficient.Results Mendeley was found to be the most active Altmetric resource, followed by Twitter. The tweets were more popular among the members of the public (65.5%) and had a diverse geographic spread. The co-authorship network analysis revealed an overall dense network of researchers. In the co-citation network analysis, the Journal of Community Dentistry had the greatest influence. Moreover, a weaker correlation was noticed between the citation counts and AAS (rs=0.325; p <0.01).Conclusion CSRs had a modest social impact in terms of AAS; however, the social network of contributing researchers was diverse and the researchers affiliated with the University of Manchester, UK were found to have the strongest link.