Data Management Core

数据管理核心

• 批准号: 10361891
• 负责人: Peter Natale Peduzzi
• 金额: $ 34.01万
• 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/10361891
• 关键词: Achievement; Address; Applications Grants; Bioinformatics; Biometry; Clinical; Collaborations; Communication; Consultations; Data; Data Analyses; Data Analytics; Data Collection; 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; Quality Control; 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; Time; 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.

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