This project aims to develop monitoring, modeling, and forecasting approaches and tools for fostering an innovative science and technology workforce. Large scale datasets of scholarly activity including funding, publications, patents, and job openings among others will be analyzed and modeled. Existing models in statistical mechanics, nonlinear dynamics, network theory, and evolutionary theory will be applied, synthesized and extended to capture the structure and dynamics of the US scientific workforce. We are particularly interested to model individual and team 'diversity'(in gender, ethnicity, disciplinarity, and institutions-academic, industry, government) as a main predictor of innovation and the spontaneous emergence of communities of innovation. The models and their analytical predictions will be rigorously validated using empirical data and applied to forecast implications of different policy interventions and funding decisions. The most predictive computational models that best address science policy maker needs will be made available as a custom tool to support development and management of interventions and training programs, to guide the collection and analysis of data necessary for program design and management, and to communicate general trends to relevant stakeholders.
The proposed modeling approach to the study of scientific workforce dynamics will inform our collective understanding of workforce dynamics in biomedicine and support empirically grounded science policy making imperative to develop cures, prevent diseases, and to protect and improve public health.
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