Quantifying hierarchy in scientific teams

TL;DR

This paper introduces a method to quantify the implicit hierarchy within scientific teams using publication features and evaluates how this hierarchy impacts scientific output, providing insights into team dynamics and innovation.

Contribution

It presents a novel approach to measure team hierarchy in scientific collaborations and assesses its influence on research productivity and impact.

Findings

Hierarchy correlates with research impact

Quantitative metrics for team structure

Implications for fostering innovation

Abstract

This paper provides a detailed description of the data collection and machine learning model used in our recent PNAS paper "Flat Teams Drive Scientific Innovation" Xu et al. [2022a]. Here, we discuss how the features of scientific publication can be used to estimate the implicit hierarchy in the corresponding author teams. Besides, we also describe the method of evaluating the impact of team hierarchy on scientific outputs. More details will be updated in this article continuously. Raw data and Readme document can be accessed in this GitHub repository Xu et al. [2022b].