Publication Citation Counts Research Articles

Machine learning-based myocardial infarction bibliometric analysis

PurposeThis study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.MethodA total of 1,036 publications were collected from the Web of Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.ResultsSince the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the United States). Institutional collaborations have formed, notably between Harvard Medical School in the United States and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, Daniel S. in the United States have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in “Circulation” and “Computers in Biology and Medicine” from the United States hold prominent positions in this study.ConclusionThis paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.

Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications.

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of ‘X-ray’ scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted ‘idea tree’ that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.

Predicting the research performance of early career scientists

This paper examines how early career-related factors can predict the future research performance of computer and information scientists. Although a few bibliometric studies have previously investigated multiple factors relating to early career scientists that significantly predict their future research performance, there have been limited studies on early career-related factors affecting scientists in the fields of information science and computer science. This study analyzes 4102 scientists whose publishing careers started in the same year. The criteria used to quantify future research performance of the target scientists included the number of publications and citation counts of publications in a 4-year citation window to indicate future research productivity and research impact, respectively. These criteria were regressed on 13 early career-related factors. The results showed that these factors accounted for about 27% and 23% of the future productivity of the target scientists in terms of journal articles and conference papers, respectively; these 13 factors were also responsible for 19% of the future impact of target scientists’ journal articles and 19% of the future impact of their conference papers. The factor that most contributed to explaining the future research performance (i.e. publication numbers) and future research impact (i.e. citation counts of publications) was the number of publications (both journal articles and conference papers) produced by the target scientists in their early career years.

An examination of the effect of open versus paywalled access publication on the disseminative impact and citation count of publications in intensive care medicine and anesthesia

PurposeWe aimed to assess the impact of open access (OA) versus paywalled access (PA) publication on Altmetric Attention Scores (AAS) and whether AAS correlates with future citation count access in the context of intensive care medicine (ICM) and anesthesia. Methods1854 and 2935 publications, in the year 2015, were identified in ICM and anesthesia respectively, using a Pubmed search. The mean AAS was measured for each article. ResultsMore ICM articles were OA, compared to of anesthesia articles (38.9% v 35.0% p = 0.02). The mean AAS for OA ICM publications was significantly higher than that of PA ICM publications (17.34 vs 8.45, p < 0.01), however, this was not observed when examined in a fixed follow up time frame. AAS appear to correlate with future citation counts. ConclusionsICM publications that are available as OA in the medium term result in higher AAS when compared to PA publications, this phenomenon was not observed in anesthesia. AAS correlate with future citation counts, however, a larger study is required to confirm this.

Year based EM-index: a new approach to evaluate the scientific impact of scholars

In the research community, generally the assessment of scholarly impact of an individual is based on citation counts of publications. Instead of this a different approach, year based h-indices have been developed based on different parameters such as total number of publications in year, total citation count earned by all the publications published in a particular year and the total number of citations earned in a particular year by all the publications which are published in any year. This index used the concept of original h-index to determine the index value. But the h-index suffers with big hit problems and the year based indices do not account the h-index big hit problems. To overcome this limitation of year based indices, we have extend the year based h-index with the EM-index and have defined a new approach named year based EM-index with three variants, which are year based EM-index by publications, publication year citations count and citation year citations. This year based EM-index considers only the h-core articles for scientific evaluation, but all those year based indices do not consider all items which have been occurring at least one time. To overcome this limitation of year based EM-index, we have extended the year based EM-index with $$\hbox {EM}^{'}$$ -index and have defined year based $$\hbox {EM}^{'}$$ -index, that consider all the items which occur at least one time. To legitimate the proposed method, an experimental study has been made on 89 scientists publication data, who are working in scientiometrics and bibliometrics fields. The experimental results show the capabilities of the year based EM-index and $$\hbox {EM}^{'}$$ -index over the year based h-indices.

EM-index: a new measure to evaluate the scientific impact of scientists

During the last decade, several scientometrics as well as bibliometrics indices were proposed to quantify the scientific impact of individual. The h-index gives a breakthrough in scientific evaluation, but this index suffers with big hit problem, i.e., once a paper is selected in h-core publication, further citation of h-core articles is not considered in scientific evaluation. To overcome this limitation of h-index, the e-index was proposed, but it does not consider the core citation count. It considers only the excess citation count. To overcome this limitation, the EM-index is proposed in this article. The EM-index is the extension of h-index and e-index, which uses the concept of multidimensional h-index. The EM-index uses all citation counts of h-core articles at multi-level to quantify the scientific impact of the individual. But this index does not consider all publication citations. To overcome this limitation of EM-index a multidimensional extension of the EM-index is also proposed called EMź-index. To validate the proposed indicators, an experimental analysis has been done on 82 scientist's publication citation count, who are working in scientiometrics field. In such a way, we found a more balanced and fine-grained approach to evaluate the scientific impact of individual as well as to compare the scientific impact of two different researchers/scientists.

Web of Science, Scopus, and Google Scholar citation rates: a case study of medical physics and biomedical engineering: what gets cited and what doesn't?

There are often differences in a publication's citation count, depending on the database accessed. Here, aspects of citation counts for medical physics and biomedical engineering papers are studied using papers published in the journal Australasian physical and engineering sciences in medicine. Comparison is made between the Web of Science, Scopus, and Google Scholar. Papers are categorised into subject matter, and citation trends are examined. It is shown that review papers as a group tend to receive more citations on average; however the highest cited individual papers are more likely to be research papers.

MapReduce: Review and open challenges

The continuous increase in computational capacity over the past years has produced an overwhelming flow of data or big data, which exceeds the capabilities of conventional processing tools. Big data signify a new era in data exploration and utilization. The MapReduce computational paradigm is a major enabler for underlying numerous big data platforms. MapReduce is a popular tool for the distributed and scalable processing of big data. It is increasingly being used in different applications primarily because of its important features, including scalability, fault tolerance, ease of programming, and flexibility. Thus, bibliometric analysis and review was conducted to evaluate the trend of MapReduce research assessment publications indexed in Scopus from 2006 to 2015. This trend includes the use of the MapReduce framework for big data processing and its development. The study analyzed the distribution of published articles, countries, authors, keywords, and authorship pattern. For data visualization, VOSviewer program was used to produce distance- and graph-based maps. The top 10 most cited articles were also identified based on the citation count of publications. The study utilized productivity measures, domain visualization techniques and co-word to explore papers related to MapReduce in the field of big data. Moreover, the study discussed the most influential articles contributed to the improvements in MapReduce and reviewed the corresponding solutions. Finally, it presented several open challenges on big data processing with MapReduce as future research directions.

A comprehensive meta-analysis of the predictive validity of the graduate record examinations: implications for graduate student selection and performance.

This meta-analysis examined the validity of the Graduate Record Examinations (GRE) and undergraduate grade point average (UGPA) as predictors of graduate school performance. The study included samples from multiple disciplines, considered different criterion measures, and corrected for statistical artifacts. Data from 1,753 independent samples were included in the meta-analysis, yielding 6,589 correlations for 8 different criteria and 82,659 graduate students. The results indicated that the GRE and UGPA are generalizably valid predictors of graduate grade point average, 1st-year graduate grade point average, comprehensive examination scores, publication citation counts, and faculty ratings. GRE correlations with degree attainment and research productivity were consistently positive; however, some lower 90% credibility intervals included 0. Subject Tests tended to be better predictors than the Verbal, Quantitative, and Analytical tests.