JMIR Publications

ABSTRACT

Background:

The World’s Top 2% Scientists database provides citation-based rankings derived from Scopus data but does not readily support integrated comparisons across countries, institutions, and disciplines or visualization of individual collaboration structures. A reproducible workflow is needed to complement ranked lists with interpretable bibliometric profiles.

Objective:

This study demonstrated ScopusCluster, a browser-based application, for Rasch-based comparison of nursing scholars in the 2025 World’s Top 2% Scientists database and for bibliometric visualization of individual scholars and JMIR Nursing publications.

Methods:

Ten self-citation-excluded citation and authorship indicators were standardized and transformed into five ordered categories. A Rasch Rating Scale Model was used to estimate group and individual measures with standard errors, displayed as forest plots. Absolute Advantage Coefficients (AACs) were calculated from the three leading first/corresponding-author publication counts to assess collaboration concentration. ScopusCluster also processed Scopus CSV metadata to generate summary reports, Kano plots, and geographic choropleth maps. The application was illustrated using nursing scholars, Margarete Sandelowski’s Scopus profile, and JMIR Nursing records.

Results:

The country-level forest plot displayed Rasch measures and uncertainty for nursing scholars across participating countries and positioned Sandelowski as the selected individual case. Among the top 100 nursing scholars evaluated using PubMed records, the mean AAC was 0.687 and the median was 0.732; therefore, collaboration concentration varied substantially among highly ranked scientists. Sandelowski’s Scopus profile included 172 documents and an h-index of 63. Her primary-author collaboration network had an AAC of 0.91, based on publication counts of 107, 8, and 6 for the three leading authors, indicating strong concentration around the leading author. The JMIR Nursing dataset comprised 210 Scopus-indexed documents (h=20); summary reports and choropleth maps described its author, country, institutional, keyword, document-type, and geographic collaboration patterns.

Conclusions:

ScopusCluster transforms Scopus metadata into reproducible Rasch forest plots and bibliometric visualizations. The framework complements conventional citation rankings by enabling integrated group comparisons and by describing primary-author collaboration concentration through AAC. These outputs are descriptive and should not be interpreted as direct evidence of research quality, leadership, or mentorship.