Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10353209
• 负责人: Li Luo
• 金额: $ 15.98万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10353209
• 关键词: Address; Adult; Adverse effects; Affect; Air; Autoimmunity; Basic Science; Bayesian Method; Behavior; Biological Markers; Biometry; Biostatistical Methods; Characteristics; Chemicals; Chronic; Classification; Collaborations; Communities; Community Health; Complex; Complex Analysis; Contracts; Data; Data Analyses; Data Analytics; Data Collection; Data Set; Data Storage and Retrieval; Databases; Development; Diabetes Mellitus; Disease; Documentation; Educational workshop; Elements; Ensure; Environmental Exposure; Environmental Health; Etiology; Exposure to; FAIR principles; Faculty; Food; Fostering; Foundations; Generations; Goals; Health; Health Disparities Research; Hypertension; Immune; Immune response; Infrastructure; Ingestion; Inhalation; Interdisciplinary Study; Intervention; Joints; Kidney Diseases; Life Style; Link; Machine Learning; Manuscripts; Mediating; Metadata; Metal exposure; Metals; Methodology; Methods; Modeling; Modernization; Monitor; Native Americans; Navajo; Outcome; Participant; Pathway interactions; Phase; Population Study; Positioning Attribute; Practice Management; Process; Productivity; Protocols documentation; Psychosocial Stress; Quality Control; Regulation; Reporting; Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Research Support; Risk; Risk Factors; Role; Safety; Sample Size; Scientist; Security; Socioeconomic Status; Source; Standardization; Statistical Data Interpretation; Statistical Methods; Statistical Models; Superfund; System; Techniques; Toxic effect; Toxicant exposure; Training; Training Activity; Translating; Translational Research; Uranium; Water; Work; base; biological research; community engagement; data integration; data interoperability; data management; data quality; data repository; data reuse; data sharing; data visualization; design; disorder prevention; disorder risk; environmental health disparity; experience; gut microbiome; high dimensionality; improved; innovation; large datasets; lectures; member; metal complex; metal poisoning; microbiome; multidisciplinary; novel; population health; preventive intervention; programs; public health relevance; quality assurance; sound; tool; tribal lands; wasting

PROJECT SUMMARY – Data Management and Analysis Core (DMAC) The Data Management and Analysis Core (DMAC) provides state-of-the-art data management support and cutting-edge statistical and geospatial analysis assistance for the Research Projects and Cores of UNM METALS Phase 2 Center. The DMAC will be co-directed by Li Luo and Yan Lin with complementary and collaborative experience in biostatistical and geospatial analyses to manage all aspects of the Core. DMAC members include eight quantitative scientists with complementary expertise in data management and statistical and geospatial analysis for a variety of scientific domains. DMAC members have backgrounds and expertise in modern data management, biostatistical methods, geospatial analysis, Bayesian methods, causal inference, as well as analyses of data from basic science, translational research, population studies, and environmental health disparities research. The team members have collaborated for over five years on multiple environmental health projects, and have streamlined the workflow from data collection, quality control, statistical and geospatial analysis support, and methodology developments to enhance the analysis of complex metal mixtures. The DMAC will implement and provide data management support that is compliant with four foundational principles: Findability, Accessibility, Interoperability, and Reusability (FAIR). DMAC will develop and utilize a centralized data repository that connects different types of data storage systems and FAIR platforms to faciliate data sharing and integration. The data and analytical support provided by the Core will contribute to many aspects of the research process including, but not limited to, efficient study design, appropriate monitoring of data safety, enhanced data management capacity, state-of-the-art statistical and geospatial analyses, assistance in the development of study protocols and contract proposals, as well as sample size and power calculations. In addition, DMAC members will assist Research Projects and Cores in preparing summary analytical reports and manuscripts. Furthermore, DMAC will develop integrated statistical and geospatial modeling to refine the risk classification based on study participants’ comprehensive environmental exposure profile, and to understand the mechanisms of the adverse effects of toxic exposures on the health outcomes mediated through intermediate biomarkers. Core faculty will also provide educational opportunities to Center investigators and trainees through lectures, workshops, and hands-on training. The collaborative efforts of Core members will contribute not only to effectively pursuing hypothesis-driven research questions but also to developing novel research questions and methods for complex analyses of large datasets. Core members will work closely with METALS Research Projects and Cores to develop and integrate new methodologies into various research projects. The DMAC is well-positioned to promote multidisciplinary research collaboration and develop analytical strategies to contribute to the successful completion and integration of Research Projects and Cores within the METALS Center.

数据管理和分析核心（DMAC） 数据管理和分析核心（DMAC）提供最先进的数据管理支持， 为UNM METALS的研究项目和核心提供尖端的统计和地理空间分析援助 二期中心。DMAC将由李洛和颜林共同执导， 在生物统计和地理空间分析方面的经验，以管理核心的各个方面。DMAC成员包括 八名定量科学家，在数据管理、统计和地理空间方面具有互补专长 分析各种科学领域。DMAC成员拥有现代数据的背景和专业知识 管理，生物统计方法，地理空间分析，贝叶斯方法，因果推理，以及 基础科学、转化研究、人口研究和环境健康数据分析 差距研究。团队成员在多个环境健康领域合作了五年多 项目，并简化了从数据收集、质量控制、统计和地理空间 分析支持和方法开发，以加强复杂金属混合物的分析。的 DMAC将实施并提供符合四项基本原则的数据管理支持： 可查找性、可访问性、互操作性和可重用性（FAIR）。DMAC将开发和利用一个集中的 连接不同类型的数据存储系统和FAIR平台以促进数据共享的数据存储库 和融合。核心小组提供的数据和分析支持将有助于在许多方面促进 研究过程，包括但不限于有效的研究设计，适当的数据安全性监测， 数据管理能力得到加强，最新的统计和地理空间分析， 制定研究方案和合同提案，以及样本量和功效计算。在 此外，国防军事咨询委员会成员将协助研究项目和核心小组准备分析报告摘要， 手稿此外，DMAC将开发综合统计和地理空间建模，以细化风险 根据研究参与者的综合环境暴露概况进行分类，并了解 毒性暴露对健康结果的不利影响的机制通过中间介导 生物标志物。核心教师还将通过以下方式为中心的研究人员和学员提供教育机会： 讲座、研讨会和实践培训。核心成员的合作努力不仅有助于 有效地追求假设驱动的研究问题，但也开发新的研究问题， 以及用于大型数据集的复杂分析的方法。核心成员将与METALS Research密切合作 项目和核心，以开发和整合新的方法到各种研究项目。DMAC是 有能力促进多学科研究合作，并制定分析战略， 成功完成并整合金属中心的研究项目和核心。