Top 3 highlights:
- Clarivate’s Highly Cited Researchers (HCR) list is widely recognized as a measure of global research excellence. However, emerging evidence suggests that the list disproportionately represents research from high-income, English-speaking regions, and men, raising concerns about equity and methodological transparency.
- While prior studies have highlighted geographic and gender disparities, no comprehensive analysis has integrated demographic, bibliometric, and collaboration data across multiple years. This study addresses this gap by systematically assessing diversity and representation within HCR lists from 2014-2024, using the PROGRESS-Plus framework to guide equity focused analyses. Findings will reveal gender, geographic, and institutional disparities among HCRs, highlighting collaboration and social capital patterns.
- This project is integrating artificial intelligence (AI) components for partial automation and process streamlining. Data extraction involves a combination of manual verification and automated methods, including tightly scoped large language model (LLM)-assisted tasks, with all AI-derived outputs subject to human validation.
What to look out for next:
- Once data analysis is completed, the study findings will be prepared as a manuscript for publication, and the structured dataset will be shared openly to support transparency, reproducibility, and support future research. Results can guide institutions, funders, and policymakers to promote equitable research evaluation, support underrepresented researchers, and foster inclusive mentorship and collaboration networks. This project will also inform future projects within the research integrity initiative which is composed of multiple interrelated projects that focus on evaluating equity, diversity, and integrity in highly cited research.
- Our aims are to assess representation in research publications, explore research practices among highly-cited researchers to develop practical tools and guidance sheets for institutions and students (PhD, post docs) to identify potentially problematic scientists and behaviours. These projects will utilize mixed-methods research, citation analysis, and network analysis to assess principles of open science, academic mentoring, and research sustainability.