Data Management & Analysis Core

核心摘要-数据管理和分析核心 在现实生活中，超级基金场所的有害物质暴露是许多污染物的混合物。到 改善和保护人类健康免受有害物质的暴露，超级基金研究计划 （SRPs）必须将生物医学研究与环境科学和工程相结合。的整合 如果我们要充分理解SRPs中不同科学学科的数据之间的联系， 暴露和疾病，并预防不良健康后果。因此，SRP生成的数据 是一项重要的研究成果，需要最佳做法来保证质量、传播和 互用性数据管理和分析核心（DMAC）的主要目标是发现， 实施和颁布最佳做法，以促进和实现数据的互操作性， 生物医学研究项目和环境科学与工程项目，以实现以下目标： 超级基金研究中心我们将协调发展和完善一个综合的 整个中心的数据管理计划。我们将与项目和核心领导人密切合作， 共享平台，并对数据集进行优先级排序，以便在整个程序中共享。我们将建立数据共享 指导方针和时间表。我们还将继续为实验设计提供专家统计数据， 多变量数据分析我们将继续开发和维护提供综合数据的软件， 原始实验数据、重要样品元数据和下游分析管道的工作流程。我们将 继续模拟剂量-反应曲线和生物反应数据。我们将定制实验 数据管理系统，为所有项目提供数据模板。我们还将实施新的数据可视化 在我们的超级基金分析工具，以促进跨项目和核心的数据共享。我们将应用新的深度- 学习算法到所有处理的数据流，将PAH暴露与结果联系起来，并最终预测 PAH混合物对生物系统的影响。我们将与项目和核心领导人密切合作，以确保 在数据生成的整个生命周期内实现高数据质量。我们将审查数据、文件质量控制 说明实验、技术或系统问题并在每一步解决问题的程序 数据生命周期。我们将整合所有研究项目和核心的结果，并培训下一个 一代毒理学家来分析他们自己的数据。

CORE SUMMARY – DATA MANAGEMENT AND ANALYSIS CORE Real-life exposures to hazardous substances from Superfund sites occur as mixtures of many contaminants. To improve and protect human health from exposure to hazardous substances, Superfund Research Programs (SRPs) must integrate biomedical research with environmental science and engineering. The integration of data from the diverse scientific disciplines in SRPs is critical if we are to fully understand the link between exposures and disease, and prevent adverse health outcomes. Therefore, the data generated by the SRP represents an important research product that requires best practices for quality assurance, dissemination and interoperability. The primary objective of the Data Management and Analysis Core (DMAC) is to discover, implement and promulgate best practices for fostering and enabling the interoperability of data between biomedical research projects and environmental science and engineering projects to accomplish the goals of the overall Superfund Research Center. We will coordinate the development and refinement of an integrated data management plan for the entire Center. We will work closely with project & core leaders to identify data sharing platforms and to prioritize datasets for sharing across the program. We will establish data sharing guidelines and timelines. We will also continue to provide expert statistics for experimental design and multivariate data analyses. We will continue to develop and maintain software that provides an integrated data workflow for raw experimental data, important sample metadata, and downstream analysis pipelines. We will continue to model dose-response curves and biological response data. We will customize the Experimental Data Management System with data templates for all projects. We will also implement new data visualizations in our Superfund Analytics Tool to facilitate data sharing across projects and cores. We will apply novel deep- learning algorithms to all processed data streams in to link PAH exposure to outcome, and to ultimately predict the effects of PAH mixtures on biological systems. We will work closely with project and core leaders to ensure high data quality throughout the lifecycle of data generation. We will review data, document quality control procedures that account for experimental, technical, or systematic problems, and resolve problems at each step of the data life cycle. We will integrate results across all research projects and cores, and we will train the next generation of toxicologists to analyze their own data.