Modeling scientific workforce dynamics using social network analysis

使用社交网络分析对科学劳动力动态进行建模

• 批准号: 8798219
• 负责人: Griffin M Weber
• 金额: $ 41.2万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-15 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8798219
• 关键词: Academia; Applications Grants; Behavioral; Censuses; Characteristics; Collaborations; Collection; Communication; Communities; Complex; Computers; Consensus; Data; Data Aggregation; Data Set; Discipline; Effectiveness; Ethnic Origin; Faculty; Familiarity; Fostering; Funding; Grant; Graph; Health Personnel; Image; Individual; Industry; Legal patent; Link; Measures; Methods; Minority; Modeling; Motivation; Outcome; Pathway Analysis; Pathway interactions; Persons; Provider; PubMed; Publications; Race; Research; Research Personnel; Research Project Grants; Role; Schools; Science; Scientist; Social Network; Social Problems; Social Sciences; Sociology; Structure; Subgroup; Surveys; System; Techniques; Testing; Theoretical model; Time; Trademark; U-Series Cooperative Agreements; Underrepresented Minority; United States National Institutes of Health; Universities; Woman; Work; base; career; innovation; multilevel analysis; prevent; public health relevance; social; theories; trend

 DESCRIPTION (provided by applicant): The scientific workforce requires teams to solve the most critical intellectual and social problems that confront us today. Scientists and inventors are embedded in self-organizing communities, where they share ideas and act both as critics and fans for each other. Recent research has shown that team collaborations, a growing trend across all disciplines, yield publications with higher intellectual impact than single researchers; and, the careers of young scientists are influenced by relationships with others in the community. Furthermore, we have found differences in the networks of women and minorities that explain some of the disparities that exist in these subgroups. Thus, we propose to develop a systems-based approach to studying scientific workforce dynamics that models the mechanisms of how new collaborations form and how these influence both the effectiveness of teams and the career trajectories of individual scientists. Obtaining the data needed to test these models may seem to be a formidable challenge. However, through prior projects, we have already brought together a unique collection of longitudinal datasets, linked at the individual person level, which will be utilized for this new study: On a national scale, PubMed (publications), NIH ExPORTER (grants), USPTO (patents - US Patent and Trademark Office), NPPES (health care providers - National Plan & Provider Enumeration System), and BoardEx (company directors and executives) provide data about individuals and teams both in academia and in industry. On a local scale, within Harvard University, we have collected detailed career data on 35,000 faculty across multiple disciplines, including sensitive information (e.g., race/ethnicity, time to promotion, grant application review scores, etc.) that are typically much more difficult to obtain. The national and local data are complementary, enabling models at different scales. This project will be undertaken by computer scientists and a behavioral and social scientist at Harvard, who recently completed an NIH-funded project to study workforce inclusion and diversity, and social scientists from the Science of Networks in Communities (SONIC) lab at Northwestern University, who are leaders in the use of Social Network Analysis (SNA) to model the socio-technical motivations of collaboration. Three specific aims are planned: (1) Develop empirically validated theoretical models that predict how teams form within the scientific workforce. We have created a multi-theoretical multilevel (MTML) model describing the possible reasons why individuals choose to collaborate. We will use Exponential Random Graph Modeling (ERGM) to test which of these hypotheses best explain the emergence of networks in the scientific workforce. (2) Determine how the assembly mechanisms of teams within the scientific workforce influence their efficacy, such as producing highly cited publications or receiving funding. (3) Determine the influence of a scientist's collaborators on hi or her career trajectory. In particular, we will look at differences in the social networks of wome and underrepresented minorities that predict advancement and retention.

 科学劳动力需要团队来解决我们今天面临的最关键的智力和社会问题。科学家和发明家 嵌入自组织社区，在那里他们分享想法，并作为彼此的评论家和粉丝。最近的研究表明，团队合作，在所有学科的增长趋势，产生比单个研究人员更高的智力影响的出版物; 青年科学家的职业生涯受到与社区其他人的关系的影响。此外，我们还发现妇女和少数群体网络的差异，这解释了这些亚群体中存在的一些差异。因此，我们建议开发一种基于系统的方法来研究科学劳动力的动态模型的机制，如何形成新的合作，以及这些如何影响团队的有效性和个人科学家的职业轨迹。 获得测试这些模型所需的数据似乎是一个艰巨的挑战。然而，通过之前的项目，我们已经汇集了一系列独特的纵向数据集，这些数据集在个人层面上相关联，将用于这项新研究：在全国范围内，PubMed（出版物），NIH ExPORTER（赠款），USPTO（专利-美国专利商标局），NPPES（医疗保健提供者-国家计划和提供者枚举系统）和BoardEx（公司董事和高管）提供有关学术界和工业界个人和团队的数据。在本地范围内，在哈佛大学，我们收集了35,000名教师在多个学科的详细职业数据，包括敏感信息（例如，种族/民族、晋升时间、补助金申请审查分数等）这通常更难获得。国家和地方数据是互补的，可以在不同规模上建立模型。 该项目将由哈佛的计算机科学家和行为和社会科学家进行，他们最近完成了一个NIH资助的研究劳动力包容性和多样性的项目，以及西北大学社区网络科学（SONIC）实验室的社会科学家，他们是使用社会网络分析（SNA）来模拟合作的社会技术动机的领导者。计划有三个具体目标：（1）开发经验验证的理论模型，预测团队如何在科学劳动力中形成。我们已经创建了一个多理论多层次（MTML）模型，描述了个人选择合作的可能原因。我们将使用指数随机图模型（ERGM）来测试这些假设中哪一个最能解释科学劳动力中网络的出现。(2)确定科学劳动力团队的组装机制如何影响其效率，例如产生高引用的出版物或获得资金。(3)确定科学家的合作者对他或她的职业轨迹的影响。特别是，我们将研究女性和代表性不足的少数群体的社交网络的差异，这些差异预测了晋升和留任。