Data Management and Analysis Core

数据管理与分析核心

基本信息

批准号：
10559479
负责人：
Brent Andrew Coull
金额：
$ 16.08万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
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/10559479
关键词：
Acceleration Algorithms Area Bioinformatics Biometry Case Study Code Cognitive aging Collaborations Communities Complex Computer software Core Facility Data Data Analyses Data Commons 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 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 Visualization 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 next generation open data phenotypic data pre-doctoral programs quality assurance remediation skills sound statistics student training success

项目摘要

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，生物统计学，生物信息学，以及地理信息系统（GIS）支持并确保资源共享和复制科学该计划支持的所有四个项目和所有核心。除了这种支持外，核心教师和研究人员将参与与任务相关的研究，以开发整合高维曝光的方法，分子和表型数据。数据管理和资源共享活动将跨越三个原则可重复性：（1）数据可重复性，其中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）分析可重复性，这将分析协议预先指定，标准化和严格，以及（3）通过资源共享计划实现的结果可复制性这符合可发现，可访问，可互操作和可重复使用的所需数据原理（公平）在NIH数据科学战略计划中引入。 DMAC将直接支持四个中心中的每个中心项目，帮助行政和CEC核心翻译从尖端获得的复杂研究结果易于理解和易于审视的结果摘要的方法，并与RETCC合作提供培训以中心教师，学生和研究员的培训，从事生物统计学领域中心相关项目的培训，定量基因组学和数据科学，包括促进繁殖和可复制性的方法。通过 DMAC的活动将促进和启用中心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这个中心与更广泛的科学界，加速了中心研究的影响。