Data Management Core

数据管理核心

• 批准号: 10698039
• 负责人: HONGYU ZHAO
• 金额: $ 24.66万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698039
• 关键词: Acceleration; Achievement; Address; Applications Grants; Bioinformatics; Biometry; Clinical; Collaborations; Communication; Consultations; Data; Data Analyses; Data Analytics; Data Collection; Data Correlations; Data Security; Data Set; Detection; Development; Doctor of Philosophy; Evaluation; Experimental Designs; Faculty; Feedback; Fostering; Funding; Genomics; Goals; Health; Individual; Information Systems; Infrastructure; Investigation; Link; Location; Medical Informatics; Medical center; Methodology; Methods; Monitor; Ontology; Play; Policies; Postdoctoral Fellow; Preparation; Program Research Project Grants; Proteomics; Public Health Informatics; Public Health Schools; Qualifying; Quality Control; Recommendation; Research; Research Personnel; Research Project Grants; Research Training; Resources; Risk; Risk Assessment; Role; Sample Size; Schedule; Science; Scientist; Secure; Statistical Data Interpretation; Suggestion; Superfund; Terminology; Testing; Toxicology; Training Programs; Training and Education; Water Pollutants; Work; biomedical informatics; data access; data integration; data interoperability; data management; data quality; data repository; data sharing; data standards; design; detection limit; drinking water; experience; high dimensionality; improved; innovation; innovative technologies; interoperability; large datasets; meetings; open data; pre-doctoral; programs; quality assurance; spatiotemporal; statistical center; statistics

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.

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）具有最先进的数据管理，生物信息学和环境统计基础设施的状态 将作为跨YSRTP项目和核心综合数据管理和分析的焦点。这 拟议的YSRTP研究项目将产生大量数据，并具有一系列类型和功能 将需要强大的数据管理组件，系统的系统生物信息学分析，以进行各种OMICS数据， 以及复杂的统计分析，包括但不限于空间和时空的方法 相关数据，高维度，大数据集和缺失（例如检测限制）。因此，DMAC会 对于YSRTP的运作至关重要，并利用耶鲁公共卫生学院的专业知识 生物统计学部及其健康信息学部以及增值专业知识 他们的附属中心：耶鲁分析科学中心，耶鲁大学医学信息学和耶鲁中心中心 用于统计基因组学和蛋白质组学。鉴于这些资源，DMAC将为良好访问 有资格的，经验丰富的临床信息家，生物统计学家和生物信息学家以前 与YSRTP的许多研究人员建立了成功的合作。提供的专业知识将 包括与博士级教职员工进行数据捕获和管理以及设计以及设计和 项目分析。 DMAC将管理此YSRTP程序内外的数据流，并将工作 与所有核心和项目科学家紧密联系，以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以推动进一步科学的枢纽 调查和创新，以促进YSRTP的目标和目标。为了实现这些目标， DAMC将重点关注三个特定目标：目标1：与项目和核心的协调；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显着加速YSRTP的整体目标 促进和进行改进检测，毒理学评估，风险评估，缓解措施的研究 并预测饮用水中新兴污染物。