Funded by the National Institutes of Health
Award number U01 GM112623-01
Modeling scientific workforce dynamics using social network analysis
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 are developing 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 is 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 his or her career trajectory. In particular, we will look at differences in the social networks of women and underrepresented minorities that predict advancement and retention.
Public Health Relevance
The scientific workforce is increasingly relying on teams to solve the most critical intellectual and social problems that confront us today. 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. This research will 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.
SONIC Team Members:
Lungeanu, A., Carter D.R., DeChurch, L.A., & Contractor, N.S. (2018). How team interlock ecosystems shape the assembly of scientific teams: A hypergraph approach. Communication Methods and Measures, 12(2-3), 174-198.