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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

基本信息

批准号：
8994292
负责人：
Griffin M Weber
金额：
$ 39.78万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
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/8994292
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 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 social theories trend

项目摘要

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.