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Data Management and Analysis Core

数据管理与分析核心

基本信息

批准号：
10112917
负责人：
Brent Andrew Coull
金额：
$ 14.21万
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/10112917
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 Ecology Ensure Environmental Engineering technology Environmental Health 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

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/文摘