Collaborative Research: Social Dynamics of Knowledge Transfer Through Scientific Mentorship and Publication

师徒关系在科研人员的职业生涯中起着至关重要的作用。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，这表明这些少数关系可以对科学事业产生重大影响。师徒关系可以通过学习新技能和新概念为受训者提供直接的智力利益，也可以通过参与导师的社会网络为受训者提供间接的社会利益。鉴于指导在科学职业发展中的重要性，这方面的培训可能在确定代表性不足的群体是否有机会从事科学职业方面发挥重要作用。本研究的目的是描述人口变量（如性别、种族和社会经济背景）对科学培训结果的影响。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。该项目开发了一个大型的学术师友关系众包数据库，并将这些数据与衡量科学生产力（出版物和资助）和人口变量的数据库联系起来。图论和语义工具将用于确定与导师和学员相关的人口变量是否以及如何影响科学生产力。对性别的初步分析重复了先前的报告，即对男性研究人员的代表性存在偏见，尤其是在更高级的职业阶段。准确模拟人口变量的影响需要考虑影响科学生产力指标的其他变量，即领域之间的差异和长期时间趋势。本项目将使用出版物数据的语义分析来发展导师的“知识社区”概念。并将其纳入职业发展的模型中。数据将开放供公众使用，为研究研究领域的动态提供新的资源。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Mentorship plays a critical role in the careers of scientific researchers. Most researchers spend several years training under just one or two graduate and/or postdoctoral mentors, suggesting that these few relationships can have a large impact on scientific careers. Mentorship can provide 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. Given the importance of mentorship in scientific career development, this aspect of training may play an important role in determining access of underrepresented groups to scientific careers. The purpose of this study is to characterize the impact of demographic variables--such as gender, ethnicity and socio-economic background--on the outcome of scientific training.Networks of mentors and trainees can be represented by a directional graph resembling a traditional family tree. This project develops a large crowdsourced database of academic mentorship relationships, and links that data to databases that measure scientific productivity (publications and grants) and demographic variables. Graph theoretic and semantic tools will be used to determine if and how demographic variables, associated with both of the mentor and trainee, impact scientific productivity. A preliminary analysis of gender replicates previous reports of bias toward representation male researchers, especially at more senior career stages. Accurately modeling effects of demographic variables requires accounting for other variables that impact scientific productivity metrics, namely differences between fields and long-term temporal trends. This project will use semantic analysis of publication data to develop the concept of the "intellectual neighborhood" of mentors. and incorporate this into the modeling of career outcomes. Data will be made open-access for general use by the public, providing a new resource for studying the dynamics of research fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.