EAGER: The impact of mentorship networks on academic research

师徒关系对科研人员的专业发展具有重要影响。大多数研究人员花费数年时间在一两个研究生和/或博士后导师的指导下进行培训，这表明这少量的关系可以对随后的职业生涯产生很大的影响。师徒关系既可以通过学习新技能和新概念给受训者带来直接的智力收益，也可以通过参与导师的社会网络给受训者带来间接的社会收益。导师和学员的网络可以用一个类似于传统家谱的方向图来表示。本项目开发了一个大型的学术师徒关系数据库和分析师徒关系对科学职业影响的工具。该数据库的开发解决了科学的科学和创新政策计划的使命。这些数据将开放给公众使用，为研究学术研究领域的动态提供新的资源。更广泛地说，学术家谱代表了一个成功的众包项目，并为其他让公众参与科研项目的努力提供了一个榜样。本项目使用语义和图论分析来描述师徒网络对职业成功和研究领域演变的影响。这个项目使用众包来扩展学术家谱，这是一个公共的、基于网络的数据库，涵盖了所有学术研究领域的正式指导关系（博士和博士后）。指导数据库中的研究人员链接到多个出版数据库中的出版元数据和联邦报告数据库中的资助数据。利用语义和图论分析来确定师徒网络的哪些特征会影响学员的后续职业生涯。这些分析以至少有两名导师的学员为重点，评估导师专业知识的直接智力影响（通过对出版物的潜在语义分析来衡量）和间接社会影响（通过两名导师在更大的学术谱系中的谱系相似性来衡量）。研究考虑了学员专业成功的两个衡量标准：他们自己指导的学员数量和他们获得研究经费的能力。

Mentorship provides an important influence on how scientific researchers develop professionally. Most researchers spend several years training under just one or two graduate and/or postdoctoral mentors, suggesting that this small number of relationships can have large impact on subsequent careers. Mentorship can have both direct intellectual benefits to the trainee through the learning of new skills and concepts and indirect social benefits through engagement with the social network of the mentor. Networks of mentors and trainees can be represented by a directional graph resembling a traditional family tree. This project develops a large database of academic mentorship relationships and tools for analyzing the impact of mentorship on scientific careers. Development of this database addresses the mission of the Science of Science & Innovation Policy program. The data will be made open-access for general use by the public, providing a new resource for studying the dynamics of academic research fields. More generally, the Academic Family Tree represents a successful crowdsourcing project and provides an example for other efforts to engage the public in scientific research projects.This project uses semantic and graph theoretic analyses to characterize the impact of mentorship networks on professional success and on the evolution of research fields. This project uses crowdsourcing to expand the Academic Family Tree, a public, web-based database of formal mentoring relationships (doctoral and postdoctoral) across all fields of academic research. Researchers in the mentorship database are linked to publication metadata in multiple publication databases and to funding data in the Federal RePORTER database. Semantic and graph theoretic analyses are used to determine what features of the mentorship network influence the subsequent careers of trainees. Focusing on trainees with at least two mentors, these analyses evaluate direct intellectual impact of mentor expertise, measured by latent semantic analysis of publications, and indirect social impact, measured by the similarity of the lineage of the two mentors in the larger academic genealogy. Two measures of trainees' professional success are considered: the number of trainees they themselves mentor and their ability to obtain research funding.