Universal research index: An inclusive metric to quantify scientific research output

Keshavarz-Fathi M., Yazdanpanah N., Kolahchi S., Ziaei H., Darmstadt G. L., Dorigo T., ...More

Journal of Academic Librarianship, vol.49, no.3, 2023 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1016/j.acalib.2023.102714
  • Journal Name: Journal of Academic Librarianship
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, FRANCIS, Periodicals Index Online, Applied Science & Technology Source, Business Source Elite, Business Source Premier, EBSCO Education Source, Education Abstracts, Index Islamicus, Information Science and Technology Abstracts, INSPEC, Library and Information Science Abstracts, Library Literature and Information Science, Library, Information Science & Technology Abstracts (LISTA), MLA - Modern Language Association Database, DIALNET
  • Keywords: Citations, CiteScore, h-index, Impact factor, Leading author, Research impact, Scholarly impact, Scholarly output, Scientometrics, UR-Index, USERN
  • Istanbul Technical University Affiliated: Yes

Abstract

Scientometrics and bibliometrics, the subfields of library and information science, deal with the quantity and quality of research outputs. Currently, various scientometric indices are being used to quantify and compare research outputs. The most widely known is the h-index. However, this index and its derivatives suffer from dependence on the mere count of a scholar's highly cited publications. To remedy this deficiency, we developed a novel index, the Universal Research Index (UR-Index) (https://usern2021.github.io/UR-Index/) by which every single publication has its own impact on the total score. We developed this index by surveying international top 1 % cited scientists in various disciplines and included additional component variables such as publication type, leading role of a scholar, co-author count, and source metrics to this scientometric index. We acknowledge that unconscious biases built into the component variables included in the UR-Index might put research from specific groups at a disadvantage, thus continued efforts to improve equitable scholarly impact in science and academia are encouraged.