Defining quantitative rules for identifying influential researchers: Insights from mathematics domain.

Area of Science:

• Mathematics
• Bibliometrics
• Scientometrics

Background:

• Identifying impactful researchers is challenging due to a lack of standardized criteria.
• Existing research relies on quantitative data from search engines (e.g., Google Scholar, Web of Science) but lacks consensus on evaluation metrics.
• There is a need for a systematic approach to nominate and recognize distinguished scientists based on measurable achievements.

Purpose of the Study:

• To develop a comprehensive set of rules for identifying impactful researchers in mathematics.
• To derive these rules from the top five quantitative parameters within specific categories.
• To provide guidance for researchers and the scientific community in evaluating and recognizing exceptional scientists.

Main Methods:

• Utilized a dataset of 525 awardees and 525 non-awardees in mathematics.
• Selected the five most pivotal quantitative parameters per category based on an importance score.
• Applied the Decision Tree Algorithm to develop classification rules for awardees and non-awardees.

Main Results:

• The Decision Tree Algorithm achieved an average accuracy of 70-75% in identifying mathematics awardees.
• Top-ranked parameters successfully elevated 50-55% of award recipients into the top 100 ranked researchers.
• The developed rules provide a data-driven method for ranking and identifying influential mathematicians.

Conclusions:

• The study offers a robust, data-driven framework for evaluating researcher impact in mathematics.
• These findings can guide individual researchers aiming for recognition and assist the scientific community in objective evaluations.
• The established criteria facilitate the fair and consistent recognition of exceptional contributions in the field.