Science Core

科学核心摘要-科学核心(SC)旨在为与公爵有关联的学者提供 人口研究中心(DPRC)以低成本提供高质量、灵活的计算和技术服务- 有效的方式；包括数据访问和分析工具。在过去的五年中，DPRC有(1) 投资于SC服务，利用杜克大学丰富的IT基础设施来确保资源调配和无缝 访问计算资源、新的分析工具、支持高强度网络采样和 模拟工具，(2)帮助为复杂人口分析确定适当的技术支持，以及(3) 提供新技术方面的培训，包括分析诸如基于网络的数据等“大数据”来源的技术。在……里面 在接下来的五年里，SC将：(1)为DPRC提供并保持出色的计算资源 学者，以及(2)开发可获得和可重复使用的工具、方法和专业知识，用于编辑、整理、 链接和分析日益增长的人口管理和医疗保健数据 研究，这是一个DPRC学者已经参与但仍面临巨大挑战的领域 他们。为了实现后一个目标，DPRC将把杜克大学的学者聚集在一起，与当地 政府机构和区域机构建立达勒姆人口实验室(DPL)，这一倡议将 允许检查导致健康轨迹不同的社会和环境特征 种族和民族多元化的人口。DPL将作为一个测试“厨房”，整合新的数据和 方法，利用大的总体规模进行非随机抽样推断，并为 快速评估以证据为基础的政策制定，同时推进NicDh人口动态 分部对开发“大”人口和健康科学的新工具和新技术的普遍兴趣。

Science Core Abstract -- The Science Core (SC) is designed to provide scholars affiliated with the Duke Population Research Center (DPRC) with high quality, flexible computing and technical services in a cost- effective manner; including both data access and analytic tools. Over the past five years DPRC has (1) invested in SC services that leverage the rich IT infrastructure at Duke to ensure provision and seamless access to computational resources, new analysis tools, support for high-intensity network sampling and simulation tools, (2) helped to identify appropriate technical support for complex population analysis, and (3) provided training in new techniques, including those to analyze “big” data sources such as web-based data. In the next five years, the SC will: (1) build on and maintain excellent computational resource provision for DPRC scholars, and (2) develop accessible and reproducible tools, methods, and expertise for compiling, curating, linking, and analyzing an ever growing volume of “big” administrative and healthcare data for population research, an area in which DPRC scholars are already involved but still face significant challenges to use them. To accomplish the latter goal, DPRC will bring together scholars at Duke in partnerships with local government agencies and regional institutions to build the Durham Population Lab (DPL), an initiative that will permit the examination of social and environmental features that contribute to divergent health trajectories in a racially and ethnically diverse population. The DPL will function as a test “kitchen,” incorporating new data and approaches, leveraging large population size for non-random sampling inference, and provide opportunities for rapid assessment for evidence-based policy making, while advancing the NICDH Population Dynamics Branch’s general interests in developing new tools and techniques for “big” population and health science.