CORE--Data Management and Statistic

数据管理和生物统计学（DMS）核心将维护一个集中的综合数据库，其中包含过去八年中在哥伦比亚儿童环境卫生中心（CCCEH）曼哈顿北部700多名儿童和母亲队列中收集的所有暴露、生物标志物、哮喘和其他与健康相关的问卷调查数据，包括将在拟议的DISCOVER项目下获得的关于这些儿童的广泛附加信息。DMS核心将作为高质量数据管理、队列跟踪和协调、统计规划、数据分析和出版的中心资源

The Data Management and Biostatistics (DMS) Core will maintain a centralized, integrated database containing all exposure, biomarker, asthma, and other health-related questionnaire data collected in the past eight years on the Columbia Center for Child Environmental Health (CCCEH) northern Manhattan cohort of 700+ children and mothers, including the extensive additional information that will be acquired about these children under the proposed DISCOVER project. The DMS Core will serve as a central resource for high quality data management, cohort tracking and coordination, statistical planning, data analysis and publication review for all four projects. The DMS Core will also provide technical support for the translational and data sharing initiatives organized by the Administrative Core. The CCCEH northern Manhattan cohort database currently includes information from birth through age 5 from multiple data sources, including repeated measurement of exposures that will be extended under the DISCOVER project, as well as many new measures that will be collected on cohort children for the first time. In order to ensure continuity of observation of the cohort, the DMS Core will work closely with the Administrative Core by providing a state-of-the-art interactive tracking information system separate from the research database that allows direct, secure access to extensive contact information and indicators of information types collected in previous followups, and allows direct updating of current information. In order to ensure the highest quality of stored cohort research information in the DISCOVER initiative, and to facilitate the availability of cohort data for statistical analysis and translational activities, the DMS core will: 1) maintain a comprehensive quality assurance program; 2) establish standardized protocols for electronic data submission from participating projects and laboratories; 3) use SIR2002 relational database management software to create a self-documenting central database and to quickly produce analytic-ready statistical files with appropriate content, format and internal documentation for statistical analysis; 4) establish mechanisms for coordinating all project publication efforts and publication review.