Monitoring, Modeling & Forecasting Tools for Fostering Innovative S&T Workforce

项目描述（由申请人提供）：本项目旨在发展监测、建模和预测的方法和工具，以培养创新型科技人才。包括资金、出版物、专利和职位空缺等学术活动的大规模数据集将被分析和建模。统计力学、非线性动力学、网络理论和进化理论中的现有模型将被应用、综合和扩展，以捕捉美国科学劳动力的结构和动态。我们特别感兴趣的是将个人和团队的“多样性”（性别、种族、学科和机构——学术、工业、政府）作为创新和创新社区自发出现的主要预测因素。这些模型及其分析预测将使用经验数据进行严格验证，并应用于预测不同政策干预和资助决策的影响。最能满足科学决策者需求的最具预测性的计算模型将作为一种定制工具提供，以支持干预措施和培训计划的制定和管理，指导计划设计和管理所需数据的收集和分析，并向相关利益攸关方传达总体趋势。

DESCRIPTION (provided by applicant): 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.