The Gene of Scientific Success

TL;DR

This paper identifies and evaluates key causal factors influencing scientific success, using machine learning to analyze their importance, revealing that author and article factors are most relevant, and noting the similarity of h-indices within institutions.

Contribution

It introduces a comprehensive framework of five causal factors for scientific success and applies advanced machine learning methods to assess their impact.

Findings

Author-centered and article-centered factors are most relevant.

Scholars within the same institution have similar h-indices.

Machine learning effectively evaluates causal factors.

Abstract

This paper elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, and discovering potential cooperators etc. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard working. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.