Data Management and Analysis Core

项目摘要-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和 为联合国监测机制金属研究项目和核心提供前沿统计和地理空间分析援助 2期中心。由李洛和严林共同执导，互补合作 生物统计和地理空间分析方面的经验，以管理核心的所有方面。DMAC成员包括 八名在数据管理、统计和地理空间方面具有互补专业知识的量化科学家 对各种科学领域的分析。Dmac成员具有现代数据方面的背景和专业知识。 管理学、生物统计学方法、地理空间分析、贝叶斯方法、因果推断以及 对基础科学、转化研究、人口研究和环境健康方面的数据进行分析 差异研究。团队成员已经在多种环境健康方面合作了五年多 项目，并简化了从数据收集、质量控制、统计和地理空间的工作流程 分析支持和方法开发，以加强对复杂金属混合物的分析。这个 DMAC将实施并提供符合以下四个基本原则的数据管理支持： 可发现性、可访问性、互操作性和可重用性(公平)。DMAC将开发和使用集中式 连接不同类型的数据存储系统和公平平台的数据存储库，以促进数据共享 和融合。核心提供的数据和分析支持将有助于 研究过程包括但不限于有效的研究设计、适当的数据安全监测、 增强的数据管理能力、最先进的统计和地理空间分析、协助 制定研究方案和合同建议，以及样本量和功率计算。在……里面 此外，DMAC成员将协助研究项目和核心编制总结分析报告和 手稿。此外，DMAC将开发综合统计和地理空间建模，以完善风险 根据研究参与者的综合环境暴露情况进行分类，并了解 毒物暴露对健康结局不良影响的中介机制 生物标志物。核心教员还将通过以下方式为中心调查人员和实习生提供教育机会 讲座、研讨会和实践培训。核心成员的合作努力不仅将有助于 有效地追求假设驱动的研究问题，同时也开发新的研究问题 以及对大数据集进行复杂分析的方法。核心成员将与金属研究部门密切合作 项目和核心，以开发新的方法并将其纳入各种研究项目。DMAC是 有能力促进多学科研究协作并制定分析策略以做出贡献 为金属中心内的研究项目和核心的成功完成和整合。

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.