Data Management Core

摘要/摘要： 数据管理和分析核心(DMAC)将提供耶鲁超级基金研究计划 (YSRTP)拥有最先进的数据管理、生物信息学和环境统计基础设施 将作为跨YSRTP项目和核心的综合数据管理和分析的协调中心。这个 拟议的YSRTP研究项目将产生大量数据，这些数据具有一系列类型和特征 将需要强大的数据管理组件，对各种组学数据进行系统的生物信息学分析， 和复杂的统计分析，包括但不限于空间和时空的方法 相关数据、高维、大数据集和缺失性(例如，检测极限)。因此，DMAC将 对YSRTP的运作至关重要，并利用耶鲁大学公共卫生学院的专业知识 生物统计部及其卫生信息学部以及由 其附属中心：耶鲁分析科学中心、耶鲁医学信息学中心和耶鲁中心 用于统计基因组学和蛋白质组学。鉴于这些资源，DMAC将提供对油井的随时访问- 合格的、经验丰富的临床信息学家、生物统计学家和生物信息学家 与YSRTP中的许多调查人员建立了成功的合作关系。所提供的专业知识将 包括与博士级别教师就数据捕获和管理以及设计和 项目分析。DMAC将管理此YSRTP计划内部和外部的数据流，并将工作 与所有核心和项目科学家密切合作，整合所有项目的数据，以开发和测试科学 假设。Dmac还将作为向项目科学家提供反馈的中心，以推动进一步的科学研究。 调查和创新，以推进青年发展计划的宗旨和目标。为了实现这些目标， DAMC将侧重于三个具体目标：目标1：与项目和核心协调；目标2：促进数据 共享和互操作性和目标3：数据质量保证、质量控制和数据集成。 这些目标和目标的实现有望大大加快青年发展方案的总体目标 促进和开展研究，以改进检测、毒理学评估、风险评估、缓解 以及对饮用水中新出现的污染物的预测。

Summary/Abstract: The Data Management and Analysis Core (DMAC) will provide the Yale Superfund Research Program (YSRTP) with a state of the art data management, bioinformatics and environmental statistics infrastructure that will serve as a focal point for integrated data management and analytics across YSRTP projects and cores. The proposed YSRTP research projects will yield a significant amount of data with a range of types and features that will require a robust data management component, systematic bioinformatics analyses for various omics data, and sophisticated statistical analyses including but not limited to methods for spatially and spatiotemporally correlated data, high dimensionality, large datasets, and missingness (e.g., limits of detection). Thus, DMAC will be critical to the functioning of the YSRTP and leverages the expertise from the Yale School of Public Health Department of Biostatistics and its Division of Health Informatics along with value-added expertise provided by their affiliated centers: Yale Center for Analytical Sciences, Yale Center for Medical Informatics and Yale Center for Statistical Genomics and Proteomics. Given these resources, DMAC will provide ready access to well- qualified, experienced clinical informaticians, biostatisticians and bioinformaticians who have previously established, successful collaborations with many of the investigators in the YSRTP. The expertise provided will include a full spectrum of consultations with PhD-level faculty for data capture and management, and design and analysis of projects. DMAC will manage the data flow within and beyond this YSRTP program and will work closely with all cores and project scientists to integrate the data from all projects to develop and test scientific hypotheses. DMAC will also serve as a hub for providing feedback to project scientists to drive further scientific investigations and innovation to advance the aims and objectives of the YSRTP. To achieve these objectives, DAMC will focus on three specific aims: Aim 1: Coordination with Projects and Cores; Aim 2: Fostering Data Sharing and Interoperability and Aim 3: Data Quality Assurance, Quality Control and Data Integration. Achievement of these aims and objectives promises to significantly accelerate the overall objective of the YSRTP to foster and conduct research that improves the detection, toxicological evaluation, risk assessment, mitigation and forecasting of emerging contaminants in drinking water.