Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names: A bibliometric study

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.

Predicting article citations using data from 100 top-cited publications in the field of Psoriasis Vulgaris and biological agents (PVBA) since 1991: A bibliometric analysis

BACKGROUND

Psoriasis Vulgaris is a chronic inflammatory disease characterized by keratinocyte hyperproliferation. Bibliometric analysis helps determine the most influential article on the topic of "Psoriasis Vulgaris and biological agents (PVBAs)", and what factors affect article citation remain unclear. This study aims (1) to identify the top 100 most cited articles in PVBA (PVBA100 for short) from 1991 to 2020, (2) to visualize dominant entities on one diagram using data in PVBA100, and (3) to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.

METHODS

The top 100 most cited articles relevant to PVBA (1991-2020) were downloaded by searching the PubMed database. Citation analysis was applied to compare the dominant roles in article types and topic categories using pyramid plots. Social network analysis (SNA) and Sankey diagrams were applied to highlight prominent entities. We examined the MeSH prediction effect on article citations using its correlation coefficients.

RESULTS

The most frequent article types and topic categories were research support by institutes (46%) and drug therapy (88%), respectively. The most productive countries were the United States (38%), followed by Germany (13%) and Japan (12%). Most articles were published in Br J Dermatol (13%) and J Invest Dermatol (11%). MeSH terms were evident in the prediction power of the number of article citations (correlation coefficient=0.45, t=4.99).

CONCLUSIONS

The breakthrough was made by developing one dashboard to display PVBA100. MeSH terms can be used for predicting article citations in PVBA100. These visualizations of PVBA100 could be applied to future academic pursuits and applications in other academic disciplines.

The Ascent of Bitcoin: Bibliometric Analysis of Bitcoin Research

Bitcoin, as the first decentralized cryptocurrency, pioneers the cryptocurrency markets, both in terms of market capitalization and scientific interest. In this paper, we performed a comprehensive bibliometric study of the Bitcoin-related literature. Using the Scopus database, we created a sample that comprises 4495 documents written in the 2011–2020 period. Furthermore, we provided insights about dimensions such as the change in the number of publications over the course of years, the main research areas, types of published documents, most important platforms and sources of Bitcoin publications, highly cited studies, productive authors, author’s countries, and finally main funders of Bitcoin-related research. Lastly, our bibliometric study manifests the current state and future path of Bitcoin literature from distinct perspectives.

A Bibliometric Analysis on Dengue Outbreaks in Tropical and Sub-Tropical Climates Worldwide Since 1950

Severe dengue outbreaks (DOs) affect the majority of Asian and Latin American countries. Whether all DOs always occurred in sub-tropical and tropical areas (STTA) has not been verified. We downloaded abstracts by searching keywords “dengue (MeSH Major Topic)” from Pubmed Central since 1950, including three collections: country names in abstracts (CNA), no abstracts (WA), and no country names in abstracts (Non-CNA). Visualizations were created to present the DOs across countries/areas in STTA. The percentages of mentioned country names and authors’ countries in STTA were computed on the CNA and Non-CNA bases. The social network analysis was applied to highlight the most cited articles and countries. We found that (1) three collections are 3427 (25.48%), 3137 (23.33%), and 6884 (51.19%) in CNA, WA, and Non-CNA, respectively; (2) the percentages of 94.3% and 79.9% were found in the CNA and Non-CNA groups; (3) the most mentioned country in abstracts were India, Thailand, and Brazil; (4) most authors in the Non-CNA collections were from the United States, Brazil, and China; (5) the most cited article (PMID = 23563266) authored by Bhatt et al. had 2604 citations since 2013. Our findings provide in-depth insights into the DO knowledge. The research approaches are recommended for authors in research on other infectious diseases in the future, not just limited to the DO topic.

Using Kano diagrams to display the most cited article types, affiliated countries, authors and MeSH terms on spinal surgery in recent 12 years

Background

Citation analysis has been increasingly applied to assess the quantity and quality of scientific research in various fields worldwide. However, these analyses on spinal surgery do not provide visualization of results. This study aims (1) to evaluate the worldwide research citations and publications on spinal surgery and (2) to provide visual representations using Kano diagrams onto the research analysis for spinal surgeons and researchers.

Methods

Article abstracts published between 2007 and 2018 were downloaded from PubMed Central (PMC) in 5 journals, including Spine, European Spine Journal, The Spine Journal, Journal of Neurosurgery: Spine, and Journal of Spinal Disorders and Techniques. The article types, affiliated countries, authors, and Medical subject headings (MeSH terms) were analyzed by the number of article citations using x-index. Choropleth maps and Kano diagrams were applied to present these results. The trends of MeSH terms over the years were plotted and analyzed.

Results

A total of 18,808 publications were extracted from the PMC database, and 17,245 were affiliated to countries/areas. The 12-year impact factor for the five spine journals is 5.758. We observed that (1) the largest number of articles on spinal surgery was from North America (6417, 37.21%). Spine earns the highest x-index (= 82.96). Comparative Study has the highest x-index (= 66.74) among all article types. (2) The United States performed exceptionally in x-indexes (= 56.86 and 44.5) on both analyses done on the total 18,808 and the top 100 most cited articles, respectively. The most influential author whose x-index reaches 15.11 was Simon Dagenais from the US. (3) The most cited MeSH term with an x-index of 23.05 was surgery based on the top 100 most cited articles. The most cited article (PMID = 18164449) was written by Dagenais and his colleagues in 2008. The most productive author was Michael G. Fehlings, whose x-index and the author's impact factor are 13.57(= √(13.16*14)) and 9.86(= 331.57/33.64), respectively.

Conclusions

There was a rapidly increasing scientific productivity in the field of spinal surgery in the past 12 years. The US has extraordinary contributions to the publications. Furthermore, China and Japan have increasing numbers of publications on spinal surgery. This study with Kano diagrams provides an insight into the research for spinal surgeons and researchers.

Using the bootstrapping method to verify whether hospital physicians have different h-indexes regarding individual research achievement: A bibliometric analysis

BACKGROUND

Individual researchers' achievements (IRA) are determined by both personal publications and article citations such as Author Impact Factor, h-index, and x-index. Due to those indicators not truly supporting a normal distribution, the traditional t-test and Analysis of variance are not allowed for RA comparison in groups. The objective of this study is to use the bootstrapping method to verify whether hospital physicians have different h-indexes.

METHODS

We downloaded 63,266 journal articles with their corresponding citations for 2128 researchers from a Taiwan university website on December 10, 2019. Their IRAs were assessed using the bibliometric h-index. A pyramid plot was used to compare the h-index patterns between institutes. The x-index and the Kano model were found to be complemental to the h-index for identifying the group IRA characteristics and rankings, including colleges and departments in the university study, the School of Medicine, and the Affiliated Hospital. The bootstrapping method was applied with an estimated 95% confidence interval (CI) to distinguish the differences in physicians between the Internal Medicine and Surgery departments. The stronger-than-the-next coefficient (SC) for the highest represents the RA strength.

RESULTS

The highest h-indices were found in the College of Engineering, School of Medicine, and the Department of Internal Medicine in groups of colleges (SC = 0.71), all departments (SC = 0.83), the School of Medicine (SC = 0.74), and the Affiliated Hospital (SC = 0.56), respectively. No difference in h-index for hospital physicians was found between departments in Internal Medicine (Mean = 2.14, 95% CI = 1.02,3.26) and Surgery (mean = 2.5, 95%CI = 1.48, 3.52).

CONCLUSIONS

The x-index and the Kano models can complement the h-index for identifying group IRA characteristics. The bootstrapping method allows estimation of the sampling distribution for almost any statistic using random sampling methods and gains measures of accuracy (as defined by 95% CI). The finding of no difference in h-index for hospital physicians between departments in Internal Medicine and Surgery requires further investigation in the future.

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

BACKGROUND

Team science research includes authors from various fields collaborating to publish their work on certain topics. Despite the numerous papers that discussed the ordering of author names and the contributions of authors to an article, no paper evaluatedIn addition, few researchers publish academic articles without co-author collaboration. Whether the bibliometric indexes (eg, h-/x-index) of sole-author researchers are higher than those of other types of multiple authors is required for comparison. We aimed to evaluate a productive author who published 114 sole-author articles with exceptional RA and RD in academics.

METHODS

By searching the PubMed database (Pubmed.com), we used the keyword of (Taiwan[affiliation]) from 2016 to 2017 and downloaded 29,356 articles. One physician (Dr. Tseng from the field of Internal Medicine) who published 12 articles as a single author was selected. His articles and citations were searched in PubMed. A comparison of various types of author ordering placements was conducted using sensitivity analysis to inspect whether this sole author earns the highest metrics in RA. Social network analysis (SNA), Gini coefficient (GC), pyramid plot, and the Kano diagram were applied to gather the following data for visualization: RESULTS:: We observed that CONCLUSIONS:: The metrics on RA are high for the sole author studied. The author's RD can be denoted by the MeSH terms and measured by the GC. The author-weighted scheme is required for quantifying author credits in an article to evaluate the author's RA. Social network analysis incorporating the Kano diagrams provided insights into the relationships between actors (eg, coauthors, MeSH terms, or journals). The methods used in this study can be replicated to evaluate other productive studies on RA and RD in the future.

The Most-Cited Authors Who Published Papers in JMIR mHealth and uHealth Using the Authorship-Weighted Scheme: Bibliometric Analysis

Background

Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison.

Objective

This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research.

Methods

We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied.

Results

We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps.

Conclusions

The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.

Whether productive authors using the national health insurance database also achieve higher individual research metrics: A bibliometric study

BACKGROUND

Many researchers use the National Health Insurance Research Database (HIRD) to publish medical papers and gain exceptional outputs in academics. Whether they also obtain excellent citation metrics remains unclear.

METHODS

We searched the PubMed database (http://www.ncbi.nlm.nih.gov/pubmed) using the terms Taiwan and HIRD. We then downloaded 1997 articles published from 2012 to 2016. An authorship-weighted scheme (AWS) was applied to compute coauthor partial contributions from the article bylines. Both modified x-index and author impact factor (AIF) proved complementary to Hirsch's h-index for calculating individual research achievements (IRA). The metrics from 4684 authors were collected for comparison. Three hundred eligible authors with higher x-indexes were located and displayed on Google Maps dashboards. Ten separate clusters were identified using social network analysis (SNA) to highlight the research teams. The bootstrapping method was used to examine the differences in metrics among author clusters. The Kano model was applied to classify author IRAs into 3 parts.

RESULTS

The most productive author was Investigator#1 (Taichung City, Taiwan), who published 149 articles in 2015 and included 803 other members in his research teams. The Kano diagram results did not support his citation metrics beyond other clusters and individuals in IRAs.

CONCLUSION

The AWS-based bibliometric metrics make individual weighted research evaluations possible and available for comparison. The study results of productive authors using HIRD did not support the view that higher citation metrics exist in specific disciplines.

Applying an Author-Weighted Scheme to Identify the Most Influential Countries in Research Achievements on Skin Cancer: Observational Study

Background

Skin cancers are caused by the development of abnormal cells that can invade or spread to other parts of the body. The countries whose authors contribute the most amount of articles on skin cancer to academia is still unknown.

Objective

The objectives of this study are to apply an author-weighted scheme (AWS) to quantify the credits for coauthors on an article byline and allocate the author weights to the country-level credits in articles.

Methods

On July 20, 2019, we obtained 16,804 abstracts published since 1938, based on a keyword search of “skin cancer” in PubMed. The author names, countries/areas, and journals were recorded. International author collaborations on skin cancer were analyzed based on country-level credits in articles. We aimed to do the following: (1) present country distribution for the first authors and the most popular journals, (2) show choropleth maps to highlight the most influential countries, and (3) draw scatter plots based on the Kano model to characterize the features of country-level research achievements. We programmed Excel Visual Basic for Applications (Microsoft Corp) routines to extract data from PubMed. Google Maps was used to display graphical representations.

Results

Our results suggest that researchers in the United States have published most frequently, accounting for 30.37% (5103), while Germany accounts for 7.34% (1234), followed by Australia (997, 5.93%). The top three continents for the proportion of published articles are North America, Europe, and Asia, accounting for 32.29%, 31.71%, and 10.41%, respectively.

Conclusions

This study offers an objective picture of the representativeness and evolution of international research on the topic of skin cancer. The research approaches used here have the potential to be applied to other areas besides skin cancer.

Whether article types of a scholarly journal are different in cited metrics using cluster analysis of MeSH terms to display: A bibliometric analysis

BACKGROUND

Many authors are concerned which types of peer-review articles can be cited most in academics and who were the highest-cited authors in a scientific discipline. The prerequisites are determined by: (1) classifying article types; and (2) quantifying co-author contributions. We aimed to apply Medical Subject Headings (MeSH) with social network analysis (SNA) and an authorship-weighted scheme (AWS) to meet the prerequisites above and then demonstrate the applications for scholars.

METHODS

By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 5,636 articles published from 2012 to 2016. A total number of 9,758 were cited in Pubmed Central (PMC). Ten MeSH terms were separated to represent the journal types of clusters using SNA to compare the difference in bibliometric indices, that is, h, g, and x as well as author impact factor(AIF). The methods of Kendall coefficient of concordance (W) and one-way ANOVA were performed to verify the internal consistency of indices and the difference across MeSH clusters. Visual representations with dashboards were shown on Google Maps.

RESULTS

We found that Kendall W is 0.97 (χ = 26.22, df = 9, P < .001) congruent with internal consistency on metrics across MeSH clusters. Both article types of methods and therapeutic use show higher frequencies than other 8 counterparts. The author Klaus Lechner (Austria) earns the highest research achievement(the mean of core articles on g = Ag = 15.35, AIF = 21, x = 3.92, h = 1) with one paper (PMID: 22732949, 2012), which was cited 23 times in 2017 and the preceding 5 years.

CONCLUSION

Publishing article type with study methodology and design might lead to a higher IF. Both classifying article types and quantifying co-author contributions can be accommodated to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated in the future.

Choropleth map legend design for visualizing the most influential areas in article citation disparities: A bibliometric study

BACKGROUND

Disparities in health outcomes across countries/areas are a central concern in public health and epidemiology. However, few authors have discussed legends that can be complemental to choropleth maps (CMs) and merely linked differences in outcomes to other factors like density in areas. Thus, whether health outcome rates on CMs showing the geographical distribution can be applied to publication citations in bibliometric analyses requires further study. The legends for visualizing the most influential areas in article citation disparities should have sophisticated designs. This paper illustrates the use of cumulative frequency (CF) map legends along with Lorenz curves and Gini coefficients (GC) to characterize the disparity of article citations in areas on CMs, based on the quantile classification method for classes.

METHODS

By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 7042 articles published from 1945 to 2016. A total number of 41,628 articles were cited in Pubmed Central (PMC). The publication outputs based on the author's x-index were applied to plot CM about research contributions. The approach uses two methods (i.e., quantiles and equal total values for each class) with CF legends, in order to highlight the difference in x-indices across geographical areas on CMs. GC was applied to observe the x-index disparities in areas. Microsoft Excel Visual Basic for Application (VBA) was used for creating the CMs.

RESULTS

Results showed that the most productive and cited countries in Medicine (Baltimore) were China and the US. The most-cited states and cities were Maryland (the US) and Beijing (China). Taiwan (x-index = 24.38) ranked behind Maryland (25.97), but ahead of Beijing (16.9). China earned lower disparity (0.42) than the US (0.49) and the rest of the world (0.53) when the GCs were applied.

CONCLUSION

CF legends, particularly using the quantile classification for classes, can be useful to complement CMs. They also contain more information than those in standard CM legends that are commonly used with other classification methods. The steps of creating CM legends are described and introduced. Bibliometric analysts on CM can be replicated in the future.