Characterization of the highly cited articles published by a genetics research department: an exploratory study.

Abstract

OBJECTIVES: To identify and assess highly cited papers included in the Web of Science Essential Science Indicators, this study looked at authors affiliated with a specific department at a School of Medicine from 2010-2019. For this study, we examined authorship characteristics, including female authorship trends, subject trends, and intramural and extramural co-authorship networks. This approach aims to highlight research impact trends to inform the department's leaders in decision-making for future publication and research strategy development directions. METHODS: We conducted a bibliometric analysis of publications from faculty in a specific department at a School of Medicine over the last ten years (2010-2019). The searches were conducted in June 2020. We used a three-phase approach to find those departmental articles ranked as "highly cited papers" in the Web of Science (WoS) "Essential Science Indicators" database: Phase 1. We queried Scopus to gather publications listing the author's departmental affiliation; Phase 2. Queried the WoS Core Collection for all the citations resulted in the Scopus search and limited the search to return only the publications identified as "Highly Cited" papers; Phase 3. Used PubMed to compile funding information due to its more standardized format of reported funding support. We utilized the OpenRefine tool to perform cleanup and cluster the author name lists and Excel to work with datasets of bibliometric data. CONCLUSIONS: For the 2010-2019 time frame, a total of 1,077 articles (original articles and review articles) were published by this department, with 37 documents identified as Highly Cited, categorized by WoS Essential Indicators. Identified documents were categorized under ten research fields and were published in 17 journals, from diverse WoS subject categories, including Neuroscience, Oncology, and Genetics research fields. The results show that Highly cited articles were published in 17 high-impact journals ranked in Q1 and Q2. Indicative of that, the highly cited papers have a strong relationship between the impact factors. 38% of the documents correspond to case-control studies. Topics covering "Genome-wide Association Study", "Genetic predisposition to disease," and Polymorphism, single nucleotide" are among the most used MeSH terms ."