Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10332726
• 负责人: Brent Andrew Coull
• 金额: $ 14.92万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-21 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10332726
• 关键词: Algorithms; Area; Bioinformatics; Biometry; Case Study; Code; Cognitive aging; Collaborations; Communities; Complex; Computer software; Core Facility; Data; Data Analyses; Data Commons; Data Management Resources; Data Science; Development; Ensure; Environmental Engineering technology; Environmental Health; Environmental Science; FAIR principles; Faculty; Fostering; Generations; Genetic; Genomics; Geographic Information Systems; Image; Infrastructure; Internet; Metals; Methodology; Methods; Mission; Molecular; Phase; Phenotype; Postdoctoral Fellow; Procedures; Protocols documentation; Public Health Schools; Quality Control; Reproducibility; Reproducibility of Results; Research; Research Methodology; Research Personnel; Resource Sharing; Resources; Science; Scientist; Services; Source; Specific qualifier value; Standardization; Statistical Computing; Statistical Methods; Statistical Models; Strategic Planning; Students; Superfund; Support System; Teacher Professional Development; Testing; Training; Training and Education; Translating; Translational Research; United States National Institutes of Health; Work; community engagement; computerized data processing; computerized tools; data infrastructure; data interoperability; data management; data sharing networks; database of Genotypes and Phenotypes; design; genome sciences; genome-wide; genomic data; graduate student; high dimensionality; meetings; member; molecular phenotype; next generation; open data; phenotypic data; pre-doctoral; programs; quality assurance; remediation; skills; sound; statistics; student training; success

SUMMARY/ ABSTRACT The Data Management and Analysis Core (DMAC) will provide data management, biostatistical, bioinformatics, and geographical information system (GIS) support and ensure resource sharing and reproducible science for all four projects and all cores supported by the program. In addition to this support, core faculty and researchers will engage in mission-related research that will develop methods to integrate high dimensional exposure, molecular, and phenotypic data. Data management and resource sharing activities will span three tenets of reproducibility: (1) data reproducibility, which will include data management plans and other quality assurance / quality control procedures for all Center data, (2) analysis reproducibility, which will analysis protocols that are pre-specified, standardized, and rigorous, and (3) result replicability, accomplished by a resource sharing plan that meets the desired principle of data being Findable, Accessible, Interoperable, and Reusable (FAIR) introduced in the NIH Data Science Strategic Plan. The DMAC will directly support each of the four Center projects, help the Administrative and CEC Cores translate complex research findings obtained from cutting-edge methodology to easily-understood and easily-visualized result summaries, and work with the RETCC to provide training to Center faculty, students, and fellows working on Center-related projects in the areas of biostatistics, quantitative genomics, and data science, including methods to promote reproducibility and replicability. Through its activities, DMAC will foster and enable the interoperability of data and methods between the Center’s biomedical and environmental science and engineering projects as well as between this Center and the broader scientific community, accelerating the impact of the Center's research.

摘要/摘要 数据管理和分析核心（DMAC）将提供数据管理、生物统计、生物信息学、 地理信息系统（GIS）支持并确保资源共享和可重复的科学 该计划支持的所有四个项目和所有核心除了这种支持之外，还有核心教师和研究人员。 将从事与任务相关的研究，开发整合高维暴露的方法， 分子和表型数据的数据管理和资源共享活动将涵盖三个原则： 再现性：（1）数据再现性，其中将包括数据管理计划和其他质量保证/ 所有中心数据的质量控制程序，(2) 分析重现性，这将分析以下协议： 预先指定、标准化和严格，以及（3）结果可复制性，通过资源共享计划来实现 满足数据可查找、可访问、可互操作和可重用 (FAIR) 的预期原则 NIH 数据科学战略计划中引入的 DMAC 将直接支持这四个中心。 项目，帮助行政和 CEC 核心转化从尖端技术获得的复杂研究成果 方法论易于理解和易于可视化的结果摘要，并与 RETCC 合作提供 为中心教员、学生和在生物统计学领域从事中心相关项目的研究员提供培训， 定量基因组学和数据科学，包括提高可重复性和可复制性的方法。 在其活动中，DMAC 将促进并实现中心各部门之间数据和方法的互操作性 生物医学和环境科学与工程项目以及该中心与更广泛的领域之间的合作 科学界，加速该中心研究的影响。