Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis

Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors' IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis

BACKGROUND

There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics.

METHODS

The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R.

RESULTS

The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US.

CONCLUSION

The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

Analyzing author collaborations by developing a follower-leader clustering algorithm and identifying top co-authoring countries: Cluster analysis

BACKGROUND

This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022.

METHODS

This study extracted article metadata from the Web of Science and used the statistical software R to implement FLCA, enabling efficient and reproducible analysis of ACs and cowords in bibliometrics. To determine the countries that easily coauthored with other countries, the study observed the top 20 countries each year and visualized the results using network charts, heatmaps with dendrograms, and Venn diagrams. The study also used chord diagrams to demonstrate the use of FLCA on ACs and cowords in Medicine (Baltimore).

RESULTS

The study observed 12,793 articles, including 5081, 4418, and 3294 in 2020, 2021, and 2022, respectively. The results showed that the FLCA algorithm can accurately identify clusters in bibliometrics, and the USA, China, South Korea, Japan, and Spain were the top 5 countries that commonly coauthored with others during 2020 and 2022. Furthermore, the study identified China, Sichuan University, and diagnosis as the leading entities in countries, institutes, and keywords based on ACs and cowords, respectively. The study highlights the advantages of using cluster analysis and visual displays to analyze ACs in Medicine (Baltimore) and their potential application to coword analysis.

CONCLUSION

The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.

A leading bibliometric author does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis

BACKGROUND

A total of 22,367 bibliometric articles have been indexed by Web of Science (WoS). The most significant contribution to the field has not yet been identified through bibliometric analysis. A comparison of individual research achievements (IRAs) and trend analysis of article citations are required after extracting bibliometric articles. The study aimed to confirm whether the leading author has a dominant RA and which articles are worth reading for readers using trend analysis.

METHODS

We identified authors with at least 100 articles related to bibliometrics in the WoS core collection. A total of 399 articles were collected to cluster author collaborations. Co-word analysis and chord diagrams were used to match chief authors in clusters with Keywords Plus in WoS core collection. The category, journal impact factor, authorship, and L-index (CJAL) score and the absolute advantage coefficient (AAC) were used to compare IRAs and identify the leading author who dominated the field significantly beyond the next 2 authors. In addition to network charts and chord diagrams, 4 visualizations were used to report study results, including a Sankey diagram, a dot plot, a temporal trend graph, and a radar plot. The temporal bubble graph was used to select articles that deserve to be read.

RESULTS

The top 3 authors were Lutz Bornmann, Yuh-Shan Ho, and Giovanni Abramo, with CJAL scores of 176.22, 176.02, and 112.06, respectively, from Germany, Italy, and Taiwan. Based on the weak dominance coefficient (AAC = 0.20 < 0.70), it is evident that the leading bibliometric author has no such significant power beyond the next 2 leading authors in IRAs. A trend analysis of the last 4 years was used to illustrate the 2 articles that deserve to be read.

CONCLUSION

Three leading authors were identified through a co-word analysis of bibliometrics. There was no evidence of an author who possessed a dominant position due to a lower AAC on the leading author. The CJAL score and the AAC can be applied to many bibliographical studies in the future rather than being limited to bibliometric studies that evaluate the leading authors in a field, as we did in this study.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis

BACKGROUND

A consensus exists that the first author and corresponding author make the most contribution to the publication of an article. The Y-index has been proposed to assess the scientific achievements of authors, institutions, and countries/regions (AIC/R for short) based on the number of first-author publications (FPs) and corresponding-author publications (RPs). Nonetheless, the Y-index is defined in terms of count and radian (represented by j and h) instead of using the relative radius and angle degree to simplify understanding. In the literature, a method for drawing radar diagrams online with the Y-index is also lacking. This study was conducted to enhance the Y-index with an additional relative radius denoted by k and the angle degree represented by h* (named Yk-index), include easy-to-use features (e.g., copying and pasting) for the delivery of the online Radar-Yk, and identify which one of AIC/R contributed the most to a scientific journal.

METHODS

From the Web of Science (WoS) database, we downloaded 9498 abstracts of articles published in the journal of Medicine (Baltimore) in 2020 and 2021. Three visual representations were used, including a Sankey diagram, a choropleth map, and a radar diagram, to identify the characteristics of contributions by AIC/R to Medicine (Baltimore) using the Yk-index (j, k, h*). A demonstration of Rada-Yk with easy-to-use features was given using the copy-and-paste technique.

RESULTS

We found that Qiu Chen (China), Sichuan University (China), China, and South Korea (based on regions, e.g., provinces/metropolitan areas in China) were the most productive AIC/R, with their Yk equal to 27,715, 12415.1, and 2045, respectively; a total of 85.6% of the published articles in Medicine (Baltimore) came from the 3 countries (China, South Korea, and Japan); and this method of drawing the Radar-Yk online was provided and successfully demonstrated.

CONCLUSION

A breakthrough was achieved by developing the online Radar-Yk to show the most contributions to Medicine (Baltimore). Visualization of Radar-Yk could be replicated for future academic research and applications on other topics in future bibliographical studies.

A comparison of 3 productive authors' research domains based on sources from articles, cited references and citing articles using social network analysis

BACKGROUND

An individual's research domain (RD) can be determined from objective publication data (e.g., medical subject headings and Medical Subject Headings (MeSH) terms) by performing social network analysis. Bibliographic coupling (such as cocitation) is a similarity metric that relies on citation analysis to determine the similarity in RD between 2 articles. This study compared RD consistency between articles as well as their cited references and citing articles (ARCs).

METHODS

A total of 1388 abstracts were downloaded from PubMed and authored by 3 productive authors. Based on the top 3 clusters in social network analysis, similarity in RD was observed by comparing their consistency using the major MeSH terms in author articles, cited references and citing articles (ARC). Impact beam plots with La indices were drawn and compared for each of the 3 authors.

RESULTS

Sung-Ho Jang (South Korea), Chia-Hung Kao (Taiwan), and Chin-Hsiao Tseng (Taiwan) published 445, 780, and 163 articles, respectively. Dr Jang's RD is physiology, and Dr Kao and Dr Tseng's RDs are epidemiology. We confirmed the consistency of the RD terms by comparing the major MeSH terms in the ARC. Their La indexes were 5, 5, and 6, where a higher value indicates more extraordinary research achievement.

CONCLUSION

RD consistency was confirmed by comparing the main MeSH terms in ARC. The 3 approaches of RD determination (based on author articles, the La index, and the impact beam plots) were recommended for bibliographical studies in the future.

Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis

BACKGROUND

The h-index does not take into account the full citation list of a researcher to evaluate individual research achievements (IRAs). As a generalization of the h-index, the hT-index takes all citations into account to evaluate IRAs. Compared to other bibliometric indices, it is unclear whether the hT-index is more closely associated with the h-index. We utilized articles published on hemodialysis and peritoneal dialysis (HD/PD) to validate the hT-index as a measure of the most significant contributions to HD/PD.

METHODS

Using keywords involving HD/PD in titles, subject areas, and abstracts since 2011, we obtained 7702 abstracts and their associated metadata (e.g., citations, authors, research institutes, countries of origin). In total, 4752 first or corresponding authors with hT-indices >0 were evaluated. To present the author's IRA, the following 4 visualizations were used: radar, Sankey, impact beam plot, and choropleth map to investigate whether the hT-index was more closely associated with the h-index than other indices (e.g., g-/x-indices and author impact factors), whether the United States still dominates the majority of publications concerning PD/HD, and whether there was any difference in research features between 2 prolific authors.

RESULTS

In HD/PD articles, we observed that (a) the hT-index was closer to and associated with the h-index; (b1) the United States (37.15), China (34.63), and Japan (28.09) had the highest hT-index; (b2) Sun Yat Sen University (Chian) earned the highest hT-index (=20.02) among research institutes; (c1) the authors with the highest hT-indices (=15.64 and 14.39, respectively) were David W Johnson (Australia) and Andrew Davenport (UK); and (c2) their research focuses on PD and HD, respectively.

CONCLUSION

The hT-index was demonstrated to be appropriate for assessing IRAs along with visualizations. The hT-index is recommended in future bibliometric analyses of IRAs as a complement to the h-index.

Using the Sankey diagram to visualize article features on the topics of whole-exome sequencing (WES) and whole-genome sequencing (WGS) since 2012: Bibliometric analysis

BACKGROUND

Sequencing technologies, such as whole-exome sequencing (WES) and whole-genome sequencing (WGS), have been increasingly applied to medical research in recent years. Which countries, journals, and institutes (called entities) contributed most to the fields (WES/WGS) remains unknown. Temporal bar graphs (TBGs) are frequently used in trend analysis of publications. However, how to draw the TBG on the Sankey diagram is not well understood in bibliometrics. We thus aimed to investigate the evolution of article entities in the WES/WGS fields using publication-based TBGs and compare the individual research achievements (IRAs) among entities.

METHODS

A total of 3599 abstracts downloaded from icite analysis were matched to entities, including article identity numbers, citations, publication years, journals, affiliated countries/regions of origin, and medical subject headings (MeSH terms) in PubMed on March 12, 2022. The relative citation ratio (RCR) was extracted from icite analysis to compute the hT index (denoting the IRA, taking both publications and citations into account) for each entity in the years between 2012 and 2021. Three types of visualizations were applied to display the trends of publications (e.g., choropleth maps and the enhanced TBGs) and IRAs (e.g., the flowchart on the Sankey diagram) for article entities in WES/WGS.

RESULTS

We observed that the 3 countries (the US, China, and the UK) occupied most articles in the WES/WGS fields since 2012, the 3 entities (i.e., top 5 journals, research institutes, and MeSH terms) were demonstrated on the enhanced TBGs, the top 2 MeSH terms were genetics and methods in WES and WGS, and the IRAs of 6 article entities with their hT-indices were succinctly and simultaneously displayed on a single Sankey diagram that was never launched in bibliographical studies.

CONCLUSION

The number of WES/WGS-related articles has dramatically increased since 2017. TBGs, particularly with hTs on the Sankey, are recommended for research on a topic (or in a discipline) to compare trends of publications and IRAs for entities in future bibliographical studies.

Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study

BACKGROUND

An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains uncertain whether the incident rate and CFR can be compared to identify countries affected by COVID-19 that are under (or out of) control. This study aims to develop a diagram for dispersing TCC and CFR on a population of 100,000 (namely, TCC100 and CFR100) using the Kano model, to examine selected countries/regions that have successfully implemented preventative measures to keep COVID-19 under control, and to design an app displaying TCC100 and CFR100 for all infected countries/regions.

METHODS

Data regarding confirmed cases and deaths of COVID-19 in countries/regions were downloaded daily from the GitHub website. For each country/region, 3 values (TCC100, CFR100, and CFR) were calculated and displayed on the Kano diagram. The lower TCC100 and CFR values indicated that the COVID-19 situation was more under control. The app was developed to display both CFR100/CFR against TCC100 on Google Maps.

RESULTS

Based on 286 countries/regions, the correlation coefficient (CC) between TCC100 and CFR100 was 0.51 (t = 9.76) in comparison to TCC100 and CFR with CC = 0.02 (t = 0.3). As a result of the traditional scatter plot using CFR and TCC100, Andorra was found to have the highest CFR100 (=6.62%), TCC100 (=935.74), and CFR (=5.1%), but lower CFR than New York (CFR = 7.4%) and the UK (CFR = 13.5%). There were 3 representative countries/regions that were compared: Taiwan [TCC100 (=1.65), CFR100 (=2.17), CFR (=1%)], South Korea [TCC100 (=20.34), CFR100 (=39.8), CFR (=2%), and Vietnam [TCC100 (=0.26), CFR100 (=0), CFR (=0%)].

CONCLUSION

A Kano diagram was drawn to compare TCC100 against CFT (or CFR100) to gain a better understanding of COVID-19. There is a strong association between a higher TCC100 value and a higher CFR100 value. A dashboard was developed to display both CFR100/CFR against TCC100 for countries/regions.

Citation analysis of the 100 top-cited articles on discectomy via endoscopy since 2011 using alluvial diagrams: bibliometric analysis

Background

Percutaneous endoscopic lumbar discectomy (PELD) is synonymous with percutaneous endoscopic transforaminal discectomy (PETD) and percutaneous endoscopic interlaminar discectomy (PEID). PEID has gained increasing recognition for its small incision, quick recovery, short hospital stay, and equivalent clinical outcome to open surgery. Numerous articles related to PEID have been published in the literature. However, which countries, journals, subject categories, and articles have ultimate influence remains unknown. The study aimed to (1) display influential entities in 100 top-cited PEID-related articles (T100PEID) on the alluvial diagram and (2) investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.

Methods

T100PEID data can be found since 2011 in the PubMed and Web of Science (WOS) databases. Using alluvial diagrams, citation analysis was conducted to compare the dominant entities. We used social network analysis (SNA) to classify MeSH terms and research areas extracted from PubMed and WOS. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100 PEID were examined using one-way analysis of variance (ANOVA) and regression analysis.

Results

A total of 81% of T100PEID is occupied by the top three countries (the US, China, and South Korea). There was an overall T100PEID impact factor of 41.3 (IF = citations/100). Articles were published in Spine (Phila Pa 1976) (23%; IF = 41.3). Six subject categories were classified using the SNA. The most cited article authored by D Scott Kreiner from Ahwatukee Sports and Spine in the US state of Phoenix had 123 citations in PubMed. The network characteristics of T100PEID are displayed on the alluvial diagram. No difference was found in article citations among subject categories (F = 0.813, p = 0.543). The most frequently occurring MeSH term was surgery. MeSH terms were evident in the prediction power of the number of article citations (F = 15.21; p < 0 .001).

Conclusion

We achieved a breakthrough by displaying the T100PEID network characteristics on the alluvial plateau. The MeSH terms can be used to classify article subject categories and predict T100PEID citations. The alluvial diagram can be applied to bibliometrics on 100 top-cited articles in future studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-022-00782-0.

An Alluvial diagram was drawn to display the network characteristics of T100PEID, which is novel and modern in the literature.

The method of drawing the Alluvial demonstrated in detail with documents in supplemental digital contents can be applied to make bibliometric studies brief, concise, and powerful.

The impact beam plot (IBP) is an additional visualization introduced in this study. The online IBP was demonstrated and worthy of future similar studies to highlight the most influential articles with a glance at a picture.

Vaccination associated with gross domestic product and fewer deaths in countries and regions: A verification study

Background:

Vaccination can have a substantial impact on mitigating COVID-19 outbreaks. However, the vaccine rollout rates associated with the gross domestic product (GDP) and few deaths are required for verification. Three hypotheses were made:

Methods:

The corresponding CNCCs and deaths were downloaded from the GitHub website. Four variables, including IP days on CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions, were collected. Correlation coefficients (CCs) between variables were computed to verify the association with vaccination rates. Four tasks were achieved:

Results:

We observed that

Conclusion:

Our results indicate that vaccination has a significant effect on mitigating COVID-19 outbreaks, even with limited protection against infection. Continued compliance with nonpharmaceutical interventions is essential to the fight against COVID-19 in the future.

The Use of hx-index to compare research achievements for ophthalmology authors in Mainland China, Hong Kong, and Taiwan since 2010

BACKGROUND:

Ophthalmology authors in mainland China, Hong Kong, or Taiwan were interested in knowing their individual research achievements (IRAs). This study was to evaluate the most cited authors, institutes, and regions in the mainland, Hong Kong, and Taiwan in the field of ophthalmology in the recent 10 years using the hx-index and to display the result with visual representations.

METHODS:

Using the PubMed search engine to download data, we conducted an observational study of citation analyses in affiliated research institutes and regions (provinces/areas) of all ophthalmology authors since 2010. A total of 19,364 published articles from 22,393 authors in the mainland, Hong Kong, and Taiwan were analyzed. The x-index and the Kano model were complemental to the hx-index in identifying IRAs. A pyramid plot was used to illustrate the importance of the author-weighted scheme (AWS) used in evaluating IRAs in academics. The hx-index combining both advantages of the h and x-index was proposed to assess individual IRAs. Furthermore, we drew: 1. a choropleth map on Google Maps to visualize the differences of IRA among regions and 2. a visual display to present individual hx-indexes.

RESULTS:

There is a significant rise over time in the number of publications. The top-ranking regions in hx-index were Shanghai (26.82), Guangdong (25.82), and Beijing (25.81). We demonstrated that Dr Wu from Taiwan published 144 articles in PMC and used the example to explain the importance of AWS when IRAs were assessed.

CONCLUSIONS:

With an overall increase in publications in the field of ophthalmology, contributions assessed by hx-indexes and the AWS should be encouraged and promoted more in the future.