Terms Of Citation Impact Research Articles

Gaps between needs of patient and public involvement and interests of researchers on pancreatic cancer

ObjectiveIdentify whether there were gaps between needs of end-users and interests of researchers focusing on pancreatic cancer. MethodsA questionnaire for end-users (patients, close family, others) and researchers was developed to measure value from the perspective of different stakeholder groups. Two separate literature analyses were conducted to assess the prevalence and impact of patient and public involvement (PPI). ResultsSignificant gaps were found between end-users and researchers in valuing basic research (15 vs 25 points, p = 0.005) and treatment (36 vs. 26 points, p = 0.015), but not in early diagnosis, risk factors, or quality of life. PPI was absent from the top 100 cited publications on pancreatic cancer research and was featured in 0.1% of all studies within the field. ConclusionsGaps existed between needs of end-users and interests of researchers on basic research and treatment. PPI constituted an insignificant part of the overall pancreatic cancer research literature and had negligible impact in terms of citations. Practical ImplicationsTo help close the gaps, PPI should be incorporated throughout the research process. The impact of PPI can be enhanced by prestigious journals in consideration of journal policies and encouragements and by dissemination at academic conferences.

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Publication Trends and Collaborative Patterns in Periodontics Research from Saudi Arabia: A Bibliometric Analysis.

To perform a comprehensive bibliometric analysis encompassing all articles published on periodontics from Saudi Arabia. Using the Scopus database on June 8, 2023, the search term "Periodontic*" was entered in the primary search bar to extract all documents published on periodontics. Following, the year filter was applied to include articles published from the earliest available date until the date of data collection, excluding 2023. Next, we used the country/region filter to limit our documents to Saudi Arabia (n = 1929). We used Microsoft Excel (v.16) to examine periodical growth, collaboration patterns, influential institutions, frequently used sources, international research collaboration, and most-cited papers. Saudi Arabia ranked 11th in periodontics research, contributing 3.43% to the global research output, reaching a peak of 7.63% in 2022. Notably, there was significant growth observed during the last 5 years of the study, with an average of 232.8 documents per year. The analysis of citations revealed that the selected documents received an average of 13.39 citations per document. Indigenous literature received less citation on average compared to internationally collaborated documents. King Saud University ranked first among the most productive institutions in Saudi Arabia, accounting 40.74% of the total output. The Journal of Contemporary Dental Practice published the highest number of documents, followed by the Saudi Dental Journal and the Journal of Periodontology. In terms of citation impact, the Journal of Periodontology emerged as the most influential, with an average of 32.83 citations per document. Research collaboration was most prevalent with researchers from the United States (22.03%), although Germany ranked first in terms of citation impact. Saudi Arabia's contribution to periodontics research has shown significant growth in recent years. The collaborative efforts with international researchers, particularly those from the United States, have played a crucial role in fostering advancements within the field.

The application of neuromarketing tools in communication research: A comprehensive review of trends

AbstractThis study provides an overview of the evolution of the body of knowledge, current research streams, potential new domains, and theoretical models of interest for neuromarketing research developed in the field of communication. Neuromarketing is defined here as research applying psychophysiological or neuroscience methods in communication. We particularly implemented a comprehensive analysis of 861 publications using keyword cooccurrence analysis and science mapping tools. The performance analysis shows a strong growth in the number of publications over the last decade, with a slight declining impact in terms of citations after reaching a peak around the turn of the century. Contributions published in journals in business and communication fields (and not psychology or neuroimaging) are those with the largest average citations per year. The evolution diagrams highlight that the use of brain imaging tools to study ad persuasion in virtual environments and social marketing contexts (e.g., health or sustainable communication), as well as the employment of novel metrics (neural synchronization) and deep learning methods to analyze data, constitute fruitful research streams in the application of neuromarketing tools in communication research. These results offer communication scholars an accurate insight on recent scientific research applying neuromarketing that can shape further studies.

Bayesian estimation reveals that reproducible models in Systems Biology get more citations

The Systems Biology community has taken numerous actions to develop data and modeling standards towards FAIR data and model handling. Nevertheless, the debate about incentives and rewards for individual researchers to make their results reproducible is ongoing. Here, we pose the specific question of whether reproducible models have a higher impact in terms of citations. Therefore, we statistically analyze 328 published models recently classified by Tiwari et al. based on their reproducibility. For hypothesis testing, we use a flexible Bayesian approach that provides complete distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e., 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that differences in journal impact factors do not explain this effect and that this effect increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates the long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidence for the increased use of reproducible models in the scientific community.

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

AbstractThe amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.

Adherence of the Spanish National Plan of Science and Innovation to open access mandates

As one of the fundamental pillars of open science (De Filippo & Mañana Rodríguez, 2020; De Filippo, Silva & Borges, 2019), open access (OA) is the central topic of the European Commission’s Open Science Policy Platform (Mendez et al., 2020) and the main goal of the CoalitionS initiative for open publication of publicly funded research. The Spanish Science, Technology, and Innovation Act (BOE, 2011) requires that publicly funded research is published in OA, but with so many exceptions to the rule it makes OA rather a choice than a stipulation. Unfortunately, the draft of the new bill has retained the same formulation.The Spanish National Plan of Science and Innovation (SNP) is Spain’s leading research funding instrument. This paper analyses publications derived from the projects funded by the SNP in the period 2013–2019 and their compliance with the OA recommendations. Projects obtained after 2019 are not included as most of them do not have published results. We investigate the presence of OA by area, how OA influenced the citation impact of the publications, and how did their inclusion in journals by journal quartile. These are the two most common measures of the quality of the article and a requisite for academic advancement in Spain.OA rates noticeably vary between research areas and types of institutions. OA rates highly (with (over 60% of articles) in life sciences, biomedicine, physical sciences, and technology whereas in arts, humanities, and social sciences it is below 50%. As for the type of institution, regional research, government and technology centres and hospitals have over 70% of their academic production published in OA, whereas this decreases to 30% for private foundations. Surprisingly, the university rate is below 60%. Overall, OA has experienced a steady growth over the past years, which is reflected in a 9% increase in the studied period. This is an encouraging result compared to earlier reports (Borrego, 2016). Future research should look more closely into the differences between research areas and institution to inform policies and regulations on open access at universities and research institutions.Our major finding is that the relationship between OA and citation count is positive (Mann-Whitney U test and GLM regression; p<0.01) controlling for research area, number of authors, presence in Q1, international collaboration, reference count, and other article measures such as paper and abstract length. This result is in line with previous reports of citation advantages of OA publications (Bautista et al., 2020; Piwowar et al., 2018). However, being published in a Q1 has a significant negative relationship with OA. Although preliminary, these results show that publishing in OA might be positive for the article impact in terms of citations but punitive in terms of academic advancement if evaluation agencies and public policies have not adapted to the new OS paradigm. Further research might explore the effects on individual authors, who might be forced to publish in high-impact open journals. More importantly, OA publication might be punitive if only a few high-impact OA journals are available.

Detection and Management of COVID-19 by Image Processing: A Scientometric Assessment of Global Publications

Background and Aim: Diagnosis of the Covid-19 disease is important to provide the best care to patients and reduce their burden in the health care system. Of late artificial intelligence and machine learning approaches have been playing an important role in image processing for the diagnosis of Covid-19. The aim of this study is to examine the global literature on “Detection and Management of Covid-19 by Image Processing” during 2020- 21, using quantitative and qualitative methods and select bibliometric indicators. Methods: A systematic search strategy was developed and performed in Scopus database for “Detection and Management of Covid-19 by Image Processing” using keywords related to “Covid-19” and “Imaging” for identification of studies published up to 10 February 2022. Results: In all 2451 global articles were identified on this topic in Scopus database, which consisted of articles (62.55%), conference papers (15.46%) and reviews (8.36%). These together registered 11.77 citations per paper. The 19.95% (489) share of the global output received extra-mural funding support and registered 14.22 citations per paper. Only 2.12% (52) share of the global output are considered high-cited as they have received 100+ citations and registered 294.27 citations per paper. The 2451 global articles originated in 100 countries, with the participation of 1661 organizations and 11755 authors and published in 1088 journals and generated 9233 keyword plus and 3831 author keywords, Although USA (506 papers), China (412 papers) and India (362 papers) were the most productive countries, however, in terms of citation impact (citations per paper and relative citation index) China (27.02 and 2.30), Germany (26.23 and 2.23) and France (23.72 and 2.02) registered the highest citation impact. Although Harvard Medical School, USA (42 papers), Massachusetts General Hospital, USA (36 papers) and Shahid Beheshti University of Medical Sciences, Iran contributed the highest publication productivity; however, Icahn School of Medicine at Mount Sinai, USA (158.44 and 13.46), Sun Yat-Sen University, China (84.0 and 7.14) and Ministry of Education, China (71.78 and 6.10) registered the highest citation impact. Although A. Gholamrenzanezhad (USA) (14 papers), D. Shen (South Korea) (12 papers) and J. Liu (China)(11 papers) contributed the highest productivity, however A. Bernheim (USA) (235.83 and 20.04), N. Sverzellati (Italy)(135.86 and 11.54) and A. Gholamrenzanezhad (USA) (62.07 and 5.27) registered the highest citation impact. Although Computers in Biology and Medicine (38 papers), Academic Radiology (29 papers) and Clinical Imaging (28 papers) published the largest number of papers, however Radiology (153.09), Journal of Infection (105.75) and Radiology Cardiothoracic (64.94) registered the highest citation impact. The top three subjects contributing the largest share of global literature to this field were Medicine (57.36%), Computer Science (35.70%) and Engineering (21.95%). Besides Covid-19, the leading keywords were X-Ray (883), Computer-Assisted Tomography (836), Deep Learning (725), Diagnostic Imaging (692), Convolutional Neural Networks (464), Chest X-ray (398) and Diagnosis (325). Conclusion: Several artificial and machine learning models adopted by researchers, policy-makers and healthcare professionals to classify the images related to the diagnosis and management of COVID-19 were observed, which promised outcomes in terms of accuracy, cost, and detection speed. The analysis provide insights of the field, indicates the research trends, identifies the existing gaps and provides a perspective for future research. The need for active collaboration among countries, organizations and authors to improve research quality and impact was suggested.

Exploring Current Status and Evolutionary Trends on the Paid Use of State-Owned Forest Resources in China: A Bibliometric Perspective

State-owned forest resources occupy an important position in China, and the development of their paid use will help to improve the economic benefits of these resources. For this study, 451 journal documents involving the paid use of state-owned forest resources in the CNKI database of China from 2008 to 2021 were selected as samples. Combining qualitative reviews with quantitative analysis, statistical analysis software was used as an analytical tool. The knowledge maps can be visualized by cluster analysis, multidimensional scaling (MDS), and co-occurrence network analysis. The change laws of this research in the time dimension were obtained using developing trend analysis. The results are as follows: 1. The number of research documents on the paid use of state-owned forest resources is increasing. 2. The core authors account for 29.27%; the research impact is relatively scattered. 3. Research institutions are primarily colleges and universities. 4. The support of provincially funded projects accounts for the highest proportion. 5. There is a relatively stable number of journals in this research field. Forestry Economy, Green Science and Technology and China Forestry Economy are the top three journals in terms of citation impact. 6. The existing research topics mainly focus on the development status of paid use, forest tourism and forest health, and the under-forestry economy (under-forestry planting, breeding, and product processing). 7. The intermediary centralities of state-owned forest farms and under-forestry economy are the highest, followed by forest tourism and forest experience, etc. With time and the promulgation of policies, the research focus in this field has gradually shifted from forest assets and forestry economics to ecotourism and forest health, and research on forest carbon sequestration is a technical branch worthy of attention in the future.

WeChat uptake of chinese scholarly journals: an analysis of CSSCI-indexed journals

The study of how science is discussed and how scholarly actors interact on social media has increasingly become popular in the field of scientometrics in recent years. While most prior studies focused on research outputs discussed on global platforms, such as Twitter or Facebook, the presence of scholarly journals on local platforms was seldom studied, especially in the Chinese social media context. To fill this gap, this study investigates the uptake of WeChat (a Chinese social network app) by the Chinese scholarly journals indexed by the Chinese Social Sciences Citation Index (CSSCI). The results show that 65.3% of CSSCI-indexed journals have created WeChat public accounts and posted over 193 thousand WeChat posts in total. At the journal level, bibliometric indicators (e.g., citations, downloads, and journal impact factors) and WeChat indicators (e.g., clicks, likes, replies, and recommendations) are weakly correlated with each other, reinforcing the idea of fundamentally differentiated dimensions of indicators between bibliometrics and social media metrics. Results also show that journals with WeChat public accounts slightly outperform those without WeChat public accounts in terms of citation impact, suggesting that the WeChat presence of scientific journals is mostly positively associated with their citation impact.

COVID-19 and Substance Use: A Scientometric Assessment of Global Publications During 2020 and 2021

Aim: This study aimed to assess the characteristics and trends of research on substance use and COVID-19. Methods: Keywords related to “Covid-19” and “Substance Use” were used in a search query formulated for the Scopus search engine. The articles published during the years 2020 and 2021, through early November 2021, were considered. Results: A total of 2184 publications were published on this topic, averaging 9.69 citations per paper. About one-seventh (13.96%) share of global publications was supported by extramural funding support. The maximum number of publications emerged from the United States of America (USA) ( n = 831; 38.05%), followed by the United Kingdom (UK) ( n = 212; 9.71%), India ( n = 165; 7.55%), and Canada (155 papers; 7.10%). In terms of citation impact, publications emerging from China (24.42 and 2.52) had the highest citation impact, followed by publications emerging from Australia (18.83 and 1.94), France (16.48 and 1.70), the UK (15.44 and 1.59), Italy (13.36 and 1.38), and Canada (12.73 and 1.31). When the data in terms of specific institutes were evaluated, Harvard Medical School, USA ( n = 52), was ranked first in productivity, followed by the University of Toronto, Canada ( n = 47); the Yale School of Medicine, USA ( n = 35); INSERM, France ( n = −29); and the University of British Columbia, Canada ( n = 2s). The University College London, UK (30.24 and 3.12), ranked first in citation impact, followed by INSERM, France (22.0 and 2.27); the Sapienza University of Rome, Italy (17.4 and 1.8); and the University of Toronto, Canada (13.68 and 1.41). When the journals were evaluated, the International Journal of Environmental Research and Public Health ( n = 83) ranked first in publication productivity, followed by the Journal of Substance Abuse Treatment ( n = 73), Frontiers in Psychology ( n = 39), Drug and Alcohol Dependence ( n = 28), and International Journal of Drug Policy ( n = 26). Conclusion: This bibliometric study suggests that a large amount of literature has accumulated during the COVID-19 pandemic on substance use disorders, both from developed and developing countries.

Covid-19 and Psychology: A Scientometric Assessment of India’s Publications during 2020-21

International Journal of Medicine and Public Health,2022,12,1,1-7.DOI:10.5530/ijmedph.2022.1.1Published:January 2022Type:Review ArticleCovid-19 and Psychology: A Scientometric Assessment of India’s Publications during 2020-21Sandeep Grover, BM Gupta, Madhu Bansal, and K.K. Mueen Ahmed Sandeep Grover1,*, BM Gupta2, Madhu Bansal3, K.K. Mueen Ahmed4 1Post Graduate Institute of Medical Education and Research, Chandigarh, INDIA. 2Formerly with CSIR-NISTADS, New Delhi 110012, INDIA. 3Panjab University, Department of Mathematics, Chandigarh, INDIA. 4Phcog.Net #17, Cox Town, Bengaluru, Karnataka, INDIA. Abstract:Objectives: The present study examines the publication output of authors from India on the topic of “Covid-19 and Psychology” using bibliometric methods. Methods: The publications as listed in Scopus database were identified by using “Covid-19” and its synonyms keywords in “Keyword” and “Title” tags. The results obtained were further restricted to subject “Psychology” under subject tag and country to India. Results: 372 publications emerged in the last 2 years on the topic of “Covid-19 and Psychology”, accounting to 4.63% share of global output. About 9.95% and 25.81% share of Indian publications received external funding support and involved international collaboration, respectively. The author’s from 277 organizations and 416 authors participated unevenly in the research in this area. The highest number of publications emerged from National Institute of Mental Health and Allied Sciences (NIMHANS), Bangaluru, followed by Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, All India Institute of Medical Sciences (AIIMS), New Delhi and All India Institute of Medical Sciences (AIIMS), Bhubaneswar. Authors from Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry had highest impact in terms of citations per paper and relative citation index, followed by authors from King George’s Medical University (KGMU), Lucknow, and Manipal Academy of Higher Education (MAHE), Manipal. The journals that published the highest number of publications were Asian Journal of Psychiatry (158 papers), Indian Journal of Psychological Medicine (44 papers), and Frontier in Psychology (18 papers). The most commonly investigated topics in terms of frequency of appearances of keywords were mental health (93), followed by anxiety (80), mental disease (68), depression (64), mental stress (34), and social isolation. Conclusion: Over the last 2 years a large number of publications have emerged in the area of COVID-19 and psychology from India. Keywords: Covid-19, Psychology, India, Bibliometrics, Scientometrics. Keywords:Bibliometrics, COVID-19, India, Psychology, Scientometrics.View:PDF (579.03 KB)

Covid-19 and Pregnancy: A Scientometric Assessment of Global Publications during 2020-21

<p style="text-align: justify;"><strong>Background:</strong> Covid-19 pandemic had been a disastrous catastrophe on human race affecting different groups of population differently. Pregnancy is a condition which too has been affected by Covid-19. The present study aims to examine the research productivity of Covid-19 and pregnancy. <strong>Methods: </strong>Keywords pertaining to &ldquo;Covid-19&rdquo; and &ldquo;Pregnancy&rdquo; were used in search string in Scopus database for the period 2020-21. Data regarding publications growth, productive countries, authors and institutions, high cited papers and significant keywords was retrieved to analyze results. <strong>Results:</strong> The search retrieved 2945 global publications for the period 2020-21. The USA, UK and China were the most productive countries (with 28.96%, 11.27% and 9.3% global share) on &ldquo;Covid-19 and Pregnancy&rdquo;. The 613 organizations and 953 authors participated in global research on the subject, with top 20 most productive organizations and authors contributing 26.38% and 9.68% global publication share and 53.64% and 38.53% global citation share respectively. Huazhong University of Science and Technology, China, Tongji Medical College, China and Harvard Medical School, USA were the most productive organizations (with 68, 64 and 52 publications) and Huazhong University of Science and Technology, China (45.88 and 2.12), Tongji Medical College, China (44.96 and 2.08) and University of Health Sciences, Turkey (22.38 and 1.03) led the world as most impactful organizations in terms of citation impact and relative citation index. A Khalil (23 papers), C. Gyamfi-Bannerman (19 papers) and D. Goffman and I.C. Poon (17 papers each) were the most productive authors and H. Yang (197.4 and 4.66), l. Feng (105.12 and 2.5) and D.A. Schwartz (74.82 and 1.77) were the most impactful authors. <em>American Journal of Obstetrics and Gynecology</em> (94 papers), <em>International Journal of Obstetrics and Gynecology </em>(85 papers) and <em>European Journal of Obstetrics and Gynecology and Reproductive Biology </em>(66 papers) were the most productive journals in global research on &ldquo;Covid-19 and Pregnancy&rdquo;. <em>JAMA-Journal of the American Medical Association </em>(45.52), <em>Acta Obstetricia et Gynecologica Scandinavica </em>(34.22) and <em>Ultrasound in Obstetrics and Gynecology </em>(25.95) were leading most impactful journals. The 61 (2.07%) out of 2945 global publications on &ldquo;Covid-19 and Pregnancy&rdquo; registered 102 to 1571 citations per paper and they together received 14620 citations averaging 239.67 citations per paper. These 61 publications are assumed as high cited publications and involve 153 authors, 116 organizations and published in 43 journals.

The dissemination of brain imaging guidelines and recommendations.

Many neuroimaging guidelines and recommendations have been published in the literature to guide fellow researchers to conduct and report research findings in a standardized manner. It was largely unknown if they were cited or read by the scientific community. Analyses were conducted to assess their impact in terms of citations, Twitter posts, and Mendeley reads. Web of Science Core Collection database was accessed to identify relevant publications. The number of their Twitter posts and Mendeley reads were recorded from Altmetric and Mendeley databases respectively. Spearman correlation tests were conducted to evaluate if the citation count had a relationship with these metrics. When all 1786 publications were considered, citation count had a strong positive correlation with Mendeley reads (rho = 0.602, p < 0.001), but a weak negative correlation with Twitter posts (rho = −0.085, p < 0.001). When publications in the 2010 s were specifically considered, citation count had an even stronger positive correlation with Mendeley reads (rho = 0.712, P < 0.001), whereas the correlation with Twitter posts became positive but still weak (rho = 0.072, P = 0.012). Temporal profiles of citation and Mendeley counts showed that these guidelines and recommendations had a relatively stable influence in the field for years after being published.

A retrospective overview of Journal of Enterprising Communities: People and Places in the Global Economy from2007 to 2021 using abibliometric analysis

Purpose This paper aims to examine the pattern of publications, using a bibliometric analysis of the Journal of Enterprising Communities: People and Places in the Global Economy (JEC) for the period between 2007 and 2021. Design/methodology/approach The study uses relevant bibliometric metrics and procedures. The analysis covers mainly the number of articles published in JEC, most influential years in terms of the number of publications and citations, top productive countries, most prolific authors, most influential institutions, funding institutions, co-authorship trends, keywords co-occurrence, and vital themes of JEC articles between 2007 and 2021. Findings The journal’s influential impact in terms of citations has increased over time, with 83.62% of the published works receiving at least one citation. Léo-Paul Dana has been recognised as the most prolific author by virtue of his contribution of articles in JEC, and the maximum contribution to JEC comes from the USA, followed by Canada and the UK. University of Canterbury, New Zealand and La Trobe University, Australia were the leading contributing institutions. The study identified “indigenous entrepreneurs”, “gender”, “social entrepreneurship”, “education” and “innovation” as contemporary keywords in the study of enterprising communities. These issues present a clear opportunity for research-related topics for the JEC. Originality/value To the best of the authors’ knowledge, this is the first comprehensive piece in the journal’s history that provides a general overview of the journal's major trends and researchers.

What do we know about business strategy and environmental research? Insights from Business Strategy and the Environment

AbstractBusiness Strategy and the Environment (BSE) is a premier journal dedicated to interdisciplinary research that advances business practice leading to improvements in environmental performance. Using big data analytics, this review examines the intellectual structure and the drivers of research impact of BSE in the scholarly domain. The bibliometric results suggest three major findings. First, the top three countries contributing to BSE are the United Kingdom, the United States, and China. Second, BSE's research manifests through five thematic clusters, namely, business strategy and sustainability; corporate governance and sustainability reporting; green marketing and pro‐environmental behavior; innovation and environmental policy; and environmental management systems. Finally, BSE's research impact in terms of citations is significantly influenced by author affiliation (United States); article age (older), appearance (lead article and special issue), length (longer), and method (mix methods); title length (shorter title); and number of keywords (more keywords) and references (more references). Implications for BSE's readers and future contributors are discussed.

Medial Sigmoid Depression of the Mandibular Ramus as a Lesion-Mimicking Anatomical Variation: A Systematic Review.

(1) Background: Medial sigmoid depression (MSD) of the mandibular ramus is an anatomical variation that resembles non-odontogenic cystic lesion. (2) Aim: The aim of this systematic review was to survey the literature to identify the relevant journal publications, reveal their scientific impact in terms of citations and compare the reported prevalence of MSD. (3) Materials and methods: PubMed, Google Scholar, Scopus and Web of Science were queried to identify relevant publications. The search string was: “medial depression of mandibular ramus” OR “medial depression of the mandibular ramus” OR “medial depression of the mandibular rami” OR “medial depression of mandibular rami” OR “medial sigmoid depression”. (4) Results: Eight studies were identified. Dry mandibles and patient dental panoramic radiographs were evaluated in four and seven of the eight studies, respectively. The prevalence of MSD varied from 20.2% to 82.0%. In male and female patients, the prevalence was 18.3–76.0% and 22.0–64.0%, respectively. MSD tended to occur bilaterally and most prevalent in patients with Angle’s Class II occlusion. The semilunar and triangular shapes were more common than teardrop and circular shapes. The most cited study had 12 citations. (5) Conclusions: MSD was a seldom investigated and cited anatomical variation that was not uncommon. Its recognition should be further promoted.

Exploring the emerging ICT trends in seismic hazard by scientometric analysis during 2010–2019

Earthquake is among the most disastrous natural hazards existing in the global economy. Large-scale earthquakes exhibit threats and challenges to the modern society, causing casualties and deaths, impeding socio-economic activities and causing massive economic losses throughout the world. Thus there is a need for an earthquake early warning, detection, and rescue system which can save lives of human, environment, and economy of the world. Tremendous growth in the use of Information and Communications Technology (ICT) applications has opened up new possibilities in characterizing infrastructure, risk area and disaster zones, planning and implementation of seismic hazard reduction measures etc. Furthermore, ICT provides an enormous opportunity for both disaster management systems and earthquake-related authorities (emergency responders, police, public health and seismology departments) to acquire state-of-the-art assistance and improved insights for precise and appropriate decision making. Consequently, it is now of paramount importance to transform from individual monitoring, prediction and decision-making frameworks to smart earthquake management systems which includes decision makers and earthquake affecting people equally with the assistance of recent technological advancements. Even though a significant number of earthquake or seismic hazard papers are published in international journals, there has been no quantitative assessment of information and communication technologies (ICT) in this literature yet encountered. Hence, this study is performed. This paper presents a bi-dimensional scientometric study of research from the perspective of various domains, research areas of seismic hazard and ICT trends over the last 10 years, as indexed in Scopus. The essence behind this study is to unveil the main influencing aspects that govern the publications and its citation structure using scientometric method. The publication pattern is analyzed first, along with related subject categories and contributing countries. Then, citation structure is analyzed which includes the distribution of citations over the years, citing journals, documents, authors and influential institutions along with their impact in terms of citations per paper. Furthermore, the keyword analysis is visualized using VOSviewer. Timeline review of keywords are exported from VOSviewer to pinpoint the hotspots and research trends. This paper provides in-depth understanding of existing seismic hazard research and indicates the emerging ICT trends in this research domain.

Publish or perish, but do not forget your software artifacts

Open-science initiatives have gained substantial momentum in computer science, and particularly in software-engineering research. A critical aspect of open-science is the public availability of artifacts (e.g., tools), which facilitates the replication, reproduction, extension, and verification of results. While we experienced that many artifacts are not publicly available, we are not aware of empirical evidence supporting this subjective claim. In this article, we report an empirical study on software artifact papers (SAPs) published at the International Conference on Software Engineering (ICSE), in which we investigated whether and how researchers have published their software artifacts, and whether this had scientific impact. Our dataset comprises 789 ICSE research track papers, including 604 SAPs (76.6 %), from the years 2007 to 2017. While showing a positive trend towards artifact availability, our results are still sobering. Even in 2017, only 58.5 % of the papers that stated to have developed a software artifact made that artifact publicly available. As we did find a small, but statistically significant, positive correlation between linking to artifacts in a paper and its scientific impact in terms of citations, we hope to motivate the research community to share more artifacts. With our insights, we aim to support the advancement of open science by discussing our results in the context of existing initiatives and guidelines. In particular, our findings advocate the need for clearly communicating artifacts and the use of non-commercial, persistent archives to provide replication packages.

Noncumulative measures of researcher citation impact

The most commonly used publication metrics for individual researchers are the total number of publications, the total number of citations, and Hirsch’s h-index. Each of these is cumulative, and hence increases throughout a researcher’s career, making it less suitable for evaluation of junior researchers or assessing recent impact. Most other author-level measures in the literature share this cumulative property. By contrast, we aim to study noncumulative measures that answer the question “In terms of citation impact, what have you done lately?” We single out six measures from the rather sparse literature, including Hirsch’s m-index, a time-scaled version of the h-index. We introduce new measures based on the idea of “citation acceleration.” After presenting several axioms for noncumulative measures, we conclude that one of our new measures has much better theoretical justification. We present a small-scale study of its performance on real data and conclude that it shows substantial promise for future use.