Core C: Data Management and Analysis Core (DMAC)

核心C：数据管理和分析核心（DMAC）

• 批准号: 10570849
• 负责人: Stanley Carlton Ahalt
• 金额: $ 23.54万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-20 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570849
• 关键词: Address; Archives; Bioinformatics; Biostatistical Methods; Communities; Data; Data Analyses; Data Science; Deposition; Development; Diabetes Mellitus; Disease; Disease susceptibility; Dose; Ensure; Environmental Impact; Environmental Monitoring; Evaluation; Event; Excision; Experimental Designs; Exposure to; FAIR principles; Filtration; Fostering; Generations; Genetic; Genome; Goals; Health; Human Resources; Metabolic Diseases; Metabolic dysfunction; Metadata; Methods; MicroRNAs; Mission; Molecular; Mouse Strains; North Carolina; Obesity; Ontology; Population; Program Research Project Grants; Protocols documentation; Public Health; Quality Control; Renaissance; Research; Research Activity; Research Design; Research Personnel; Research Support; Risk; Risk Assessment; Role; Sampling; Science; Scientist; Source; Statistical Methods; Sum; Superfund; System; Technology; Toxicology; Translational Research; Vision; Work; cheminformatics; contaminated water; data integration; data management; data sharing; design; disorder prevention; experience; gut microbiome; hazard; insight; interoperability; microbiome; microbiome signature; novel; population health; programs; quality assurance; remediation; response; statistics; tool; transcription factor; transcriptome; verification and validation

ABSTRACT: DATA MANAGEMENT AND ANALYSIS CORE The Data Management and Analysis Core (DMAC) provides critical support for UNC-SRP researchers to manage and analyze data related to the theme, “Identifying novel methods to reduce iAs exposure and elucidating mechanisms underlying iAs-induced metabolic dysfunction with a vision for disease prevention.” The goal of the DMAC is to support the data management, integration, and analysis needs of the UNC-SRP researchers to reveal multi-factorial determinants of iAs-induced metabolic dysfunction/diabetes. The data management component of DMAC is housed in the Renaissance Computing Institute (RENCI). The analytical support of the DMAC is made possible through the UNC-Department of Statistics. Each of the projects in the SRP will work closely with DMAC for bioinformatic, statistical, and data science needs. By making data Findable, Accessible, Interoperable and Reusable (FAIR), the DMAC will maximize the impact and optimize the path to identifying high impact insights. The UNC-SRP scientists in Biomedical Projects 1-3 will generate a broad suite of data types that includes iAs-associated microRNAs, transcription factors, and bacterial presence in the gut microbiome. Environmental Projects 4-5 will generate data relevant to iAs levels in North Carolina (NC) and iAs reduction via filtration. The DMAC will provide state-of-the-art data management, stewardship, and analysis for these diverse types of data. Specifically, the DMAC will: first, develop UNC-SRP-wide comprehensive Data Management Plan to include high quality data generation and systems that foster sharing and interoperability; second, facilitate UNC-SRP Project-specific research activities by implementing state-of-the-art bioinformatic and biostatistical methods; and third, integrate UNC-SRP data across Projects 1-5 to understand risks of iAs exposure and mechanisms underlying iAs-associated metabolic dysfunction/diabetes. Together, the DMAC will provide the UNC-SRP with essential expertise in data management, bioinformatics, statistics, and data integration and critically contribute to the team fulfilling its mission- to develop new solutions for iAs reduction and disease prevention through mechanistic and translational research.

摘要：数据管理和分析核心 数据管理和分析核心（DMAC）为UNC-SRP研究人员提供了重要的支持 管理和分析与主题相关的数据，“识别新的方法来减少IAS暴露和 阐明IAS诱导的代谢功能障碍的基础机制具有预防疾病的愿景。” DMAC的目标是支持UNC-SRP的数据管理，集成和分析需求 研究人员揭示了IAS诱导的代谢功能障碍/糖尿病的多因素决定者。 DMAC的数据管理部分位于文艺复兴时期计算研究所（Renci）中。这 DMAC的分析支持是通过统计信息分开使DMAC的。每个项目 在SRP中，将与DMAC紧密合作，以满足生物信息学，统计和数据科学需求。通过制作数据 DMAC可找到，​​可访问，可互操作和可重复使用（公平），将最大化影响并优化 识别高影响力见解的途径。生物医学项目中的UNC-SRP科学家1-3将产生广泛的 包括IAS相关的microRNA，转录因子和细菌存在的数据类型的套件 肠道微生物组。环境项目4-5将生成与北卡罗来纳州（NC）和 通过过滤减少IAS。 DMAC将提供最先进的数据管理，管理和分析 对于这些潜水员类型的数据。具体而言，DMAC将：首先，开发UNC-SRP范围的全面数据 管理计划包括促进共享和互操作性的高质量数据生成和系统； 其次，通过实施最先进的生物信息学，最喜欢的UNC-SRP项目特定研究活动 和生物统计学方法；第三，跨项目1-5的集成UNC-SRP数据，以了解IAS的风险 IAS相关代谢功能障碍/糖尿病的暴露和机制。在一起，DMAC将 向UNC-SRP提供数据管理，生物信息学，统计和数据的基本专业知识 集成和批判性为实现其任务的团队做出贡献 - 为减少IAS的新解决方案 通过机械和翻译研究预防疾病。