Data Management & Analysis Core

核心摘要-数据管理和分析核心 现实生活中暴露在超级基金网站上的危险物质是多种污染物的混合物。至 超级基金研究项目改善和保护人类健康不受危险物质影响 (SRPS)必须将生物医学研究与环境科学和工程相结合。整合了 如果我们要充分了解SRP中不同科学学科之间的联系，那么来自不同科学学科的数据至关重要 暴露和疾病，并防止不利的健康后果。因此，SRP生成的数据 代表一个重要的研究产品，需要质量保证、传播和 互操作性。数据管理和分析核心(DMAC)的主要目标是发现、 实施和公布最佳做法，以促进和促进数据在 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 整个超级基金研究中心。我们将协调发展和完善一体化的 整个中心的数据管理计划。我们将与项目和核心领导密切合作，以确定数据 共享平台，并确定在整个计划中共享的数据集的优先顺序。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和实验提供专家统计数据 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、重要样本元数据和下游分析管道的工作流程。我们会 继续模拟剂量-反应曲线和生物反应数据。我们将定制试验性的 为所有项目提供数据模板的数据管理系统。我们还将实施新的数据可视化 在我们的Superfund Analytics工具中，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过的数据流，将PAH暴露与结果联系起来，并最终预测 多环芳烃混合物对生物系统的影响。我们将与项目和核心领导密切合作，确保 在数据生成的整个生命周期中实现高数据质量。我们将审查数据、文档质量控制 说明试验性、技术性或系统性问题并在每一步解决问题的程序 数据生命周期的一部分。我们将整合所有研究项目和核心的成果，并将培训下一个 一代毒物学家分析自己的数据。

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.