论文标题
量化科学团队的层次结构
Quantifying hierarchy in scientific teams
论文作者
论文摘要
本文提供了我们最近的PNAS论文“ Flat Teams Drive Scientific Innovation”中使用的数据收集和机器学习模型的详细说明。 [2022a]。在这里，我们讨论了如何使用科学出版物的特征来估计相应作者团队中的隐式层次结构。此外，我们还描述了评估团队层次结构对科学产出的影响的方法。更多详细信息将在本文中不断更新。可以在此Github存储库中访问原始数据和读数文档。 [2022b]。
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].
