Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names

Abstract

Background:Team science research includes the number of coauthors in publications. Many papers have discussed the ordering of author names and the contributions of authors to a paper. However, no paper addresses the relation between authors’ research domains and personal impact factors (PIF) with the ordering of author names. We aimed to apply Gini coefficient (GC) to evaluate the author research domains associated with the PIF and the ordering of author names on academic papers.Methods:By searching the PubMed database (Pubmed.com), we used the keyword “medicine” [journal] and downloaded 10,854 articles published from 1969 to 2018. A total number of 7502 articles labeled with complete author's countries/areas were included in data analysis. We also proposed a PIF index and jointly applied social network analysis (SNA), the GC, and Google Maps to report the following data with visual representations: the trend of author collaboration in Medicine; the dominant nations and keywords in Medicine; and the author research domains in Medicine associated with the PIF and the ordering of author names on academic papers.Results:The trend of author collaboration in Medicine is slightly declining (= −0.06) based on the number of authors per article. The mean number of individuals listed as authors in articles is 7.5. Most first authors are from China (3649, 48.64%) and Taiwan (847, 11.29%). The median of GC (0.32) and PIF (0.74) for the middle authors are obviously less than those for the first (0.53, 2.19) and the last authors (0.42, 2.61). A perfect positive linear relation with a large effect exists between GC and PIF because the correlation coefficient is 0.68 (>0.50, t = 2.48, n = 9).Conclusion:Results suggest that the corresponding author is submitting the manuscript to the target journal with a core author's academic background and the personal impact factor related to the research domain and the journal scope in the future. As such, peer reviewers can quickly determine whether the manuscript is a potentially citable research paper.

The Most Cited Mesh Terms and Authors who Published Papers in Pubmed Central on the Topic of Medicine and Health Using Bibliometric Analyses

“…The bibliometric indices are dependent on both quantity (i.e., the number of publications) and quality (i.e., the number of articles being cited), which are suitable for use on the topics of medicine and health as we did in this study. In comparison to the author-based bibliometrics [20,21], the MeSH-based metrics gain higher values than the author indices because of a larger number of occurrences in MeSH terms than authors. On Figs.…”

“…We programmed Microsoft Excel VBA (visual basic for applications) modules [20,21] to extract abstracts and their corresponding coauthor names as well as the countries/areas of the first authors for each article on January 20, 2019, from Pubmed Central (PMC) based on the topic of medicine and health. Only those abstracts published by the keyword medicine and health [title] and labeled with journal article were included.…”

“…The way for quantifying MeSH terms would be defined first in this study. As the author-weighted scheme [20,21], we define the MeSH weighted scheme as Wi=1/n and ci=Wi*pi for citations and publications, respectively, where n=number of articles, ci, and pi as citations and publications on the i-th article.…”

“…The algorithm of community partition was performed to separate clusters. Each MeSH was assigned to the corresponding cluster which was similar to the author analysis [20,21] and sized with the highest degree centrality(DC) within the cluster. As such, each MeSH contributes equally proportional parts (i.e., 1/n) in an article and then shows the total DC in the cluster network with the respective metrics.…”

Choropleth Map Legend Design for Visualizing the Most Influential Areas on the Topic of Anesthesiology: Bibliometric Analyses

“…where D is the absolute difference of each pair element between classes, Xij is the value of the data element, Xij-bar is the mean of all elements across all classes, q is the number of classes, and q/(q-1) is the adjustment for the use on the number of classes to reach 1.0. Otherwise, the maximal GC reaches 0.8 if the adjustment is removed from the Eq.1 [15]. The alarming level of GC for denoting inequality is set at 0.4 [15][16][17].…”