Data-driven correlation mapping of H-index and its variants in mathematics

Abstract Evaluating the scientific impact of researchers is a complex and tedious task, requiring careful consideration. The scientific community proposes various bibliometric indicators to rank most influential scholar. Each indicator offers a distinct methodology for ranking researchers, leading to diverse outcomes. The current study investigates the correlations between more than sixty different indices by utilizing a comprehensive dataset of researchers. Most h-index variants exhibit high positive correlations (0.80–0.99), whereas productivity-based indicators such as Cite/Paper and Author/Paper show negative correlations (–0.25 to − 0.82) with cumulative indices, highlighting the distinct dimensions of research impact they capture. These findings provide valuable insights for award nominating societies and decision-makers, equipping them with the knowledge and tools to make informed choices when selecting the most appropriate index for evaluating and ranking researchers.