The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling

Author(s)

Prati, Ronaldo C. ; Rodrigues, Bruno S. M. ; Aragao, Iberis ; Soares, Thereza A. ; Quiles, Marcos G. ; Da Silva, Juarez L. F.

• Document type: Journal article
• Source: JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 64, n. 4, p. 5-pg., 2024-02-12.

Abstract

There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical Information and Modeling (JCIM), where there has been a gradual increase in the number of papers that embrace this diversity. In this viewpoint, we analyze the evolution of the profile of papers published in JCIM from 1996 to 2022 addressing three diversity criteria, namely interdisciplinarity, geographic and gender distributions, and their impact on citation patterns. We used natural language processing tools for the classification of main areas and gender, as well as metadata, to analyze a total of 7384 articles published in the categories of research articles, reviews, and brief reports. Our analyses reveal that the relative number of articles and citation patterns are similar across the main areas within the scope of JCIM, and international collaboration and publications encompassing two to three research areas attract more citations. The percentage of female authors has increased from 1996 (less than 20%) to 2022 (more than 32%), indicating a positive trend toward gender diversity in almost all geographic regions, although the percentage of publications by single female authors remains lower than 20%. Most JCIM citations come from Europe and the Americas, with a tendency for JCIM papers to cite articles from the same continent. Furthermore, there is a correlation between the gender of the authors, as JCIM manuscripts authored by females are more likely to be cited by other JCIM manuscripts authored by females. (AU)

• FAPESP's process: 22/09285-7 - Chemical space exploration via semi-supervised learning for design of new materials
• Grantee: Marcos Gonçalves Quiles
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/11631-2 - CINE: computational materials design based on atomistic simulations, meso-scale, multi-physics, and artificial intelligence for energy applications
• Grantee: Juarez Lopes Ferreira da Silva
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 18/21401-7 - Multi-User Equipment approved in grant 2017/11631-2: cluster computational de alto desempenho - ENIAC
• Grantee: Juarez Lopes Ferreira da Silva
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 21/04283-3 - A multi-resolution approach to cation-induced polymorphism of lipopolysaccharide aggregates
• Grantee: Thereza Amélia Soares da Silva
• Support Opportunities: Regular Research Grants