Data Management & Analysis Core (DMAC)

数据管理

• 批准号: 10116397
• 负责人: Donald E Mercante
• 金额: $ 13.33万
• 依托单位: LOUISIANA STATE UNIV A&M COL BATON ROUGE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116397
• 关键词: Address; Adopted; Adoption; Architecture; Archives; Biometry; Cardiovascular system; Code; Collaborations; Communities; Complex; Computer software; Computers; Coupled; Custom; Data; Data Analyses; Data Collection; Data Management Resources; Data Security; Data Set; Data Storage and Retrieval; Database Management Systems; Development; Dimensions; Educational workshop; Elements; Ensure; Environment; Environmental Health; Experimental Designs; Exposure to; FAIR principles; Faculty; Free Radical Formation; Free Radicals; Functional disorder; Funding; Goals; Health; Human; Image; Individual; Informatics; Interdisciplinary Study; Investigation; Knowledge; Lung; Mediation; Methodology; Methods; Mission; Modeling; Pathway interactions; Persons; Preparation; Protocols documentation; Public Health; Publications; Quality Control; Randomized; Reproducibility; Research; Research Design; Research Methodology; Research Personnel; Research Project Grants; Resources; Sampling; Science; Secure; Services; Signal Recognition Particle; Source; Speed; Standardization; Statistical Data Interpretation; Statistical Models; Superfund; Survival Analysis; System; Technology; Testing; Time; TimeLine; Training; Work; base; complex data; computerized tools; data acquisition; data management; data quality; data reduction; data sharing; data visualization; data warehouse; design; experience; experimental study; graduate student; improved; innovation; investigator training; kinetic model; large datasets; lectures; machine learning method; member; multidimensional data; multidisciplinary; novel; novel strategies; open data; prevent; programs; quality assurance; respiratory health; semiparametric; skills; success; theories; tool

Project Summary/Abstract: Data Management and Analysis Core (DMAC) The Data Management and Analysis Core (DMAC) is designed to enhance the LSU Superfund Research Program's (SRP’s) understanding of how environmentally persistent free radicals (EPFRs) induce pulmonary/cardiovascular dysfunction, and how to prevent formation, enhance decay, and limit exposure to EPFRs, with the ultimate goal of improving human health and the environment. The five Projects and supporting Cores in the LSU SRP present considerable data management and biostatistical challenges that are crucial to the overall success of the Center. The DMAC’s Specific Aims are to (1) Develop and implement a comprehensive data management plan for LSU SRP; (2) Develop and implement informatics solutions, including data collection, distribution, and analysis tools and secure storage for data generated by LSU SRP Projects and Cores; (3) Provide statistical expertise to SRP Projects and Cores; (4) Provide expertise in the application and development of novel statistical models and methodology for analysis of complex multidimensional data; (5) Provide educational initiatives and resources to serve a wide audience of graduate students, postdoctoral researchers, and junior faculty. DMAC members possess the knowledge, skills, and experiences necessary for tackling the complex multi-disciplinary issues to be addressed by the LSU SRP. We will implement a comprehensive data management strategy leveraging recent advances within the LSU system in high-speed computing and data distribution, along with stable and secure data collection, management, and storage platforms for facilitating multi-disciplinary collaborations. Our Core is committed to promoting transparent and reproducible research through the adoption of software, providing time-stamped version control over documents, files, and code, such as the Open Science Framework and the workflowr R package for statistical analysis. The DMAC biostatisticians will expand the toolsets available to the Superfund research community by developing novel approaches and methods for understanding the relationship between EPFR exposures and respiratory health effects using (multivariate) multiple mediation analysis, as well as the use of reliable machine learning methods for dimension reduction in the investigation of the microstructural pathway of EPFR formation and decay mechanisms, among other advancements. Last, the DMAC will develop and promote a wide array of initiatives in various formats and venues for educating SRP investigators, postdoctoral researchers, and graduate students on topics such as effective data management strategies, study design principles, and on conducting transparent, valid, generalizable, and repeatable research.

项目摘要/摘要：数据管理和分析核心 (DMAC) 数据管理和分析核心 (DMAC) 旨在增强路易斯安那州立大学超级基金研究 项目 (SRP) 对环境持久性自由基 (EPFR) 如何诱发的理解 肺/心血管功能障碍，以及如何预防形成、促进腐烂和限制接触 EPFR 的最终目标是改善人类健康和环境。五个项目及 LSU SRP 中的支持核心提出了相当大的数据管理和生物统计挑战， 对中心的整体成功至关重要。 DMAC 的具体目标是 (1) 制定并实施 LSU SRP 综合数据管理计划； (2) 制定并实施信息学解决方案， 包括数据收集、分发和分析工具以及 LSU SRP 生成的数据的安全存储 项目和核心； (3) 为 SRP 项目和核心提供统计专业知识； (4) 提供专业知识 应用和开发新的统计模型和方法来分析复杂的 多维数据； （五）提供教育举措和资源，服务广大研究生 学生、博士后研究人员和初级教师。 DMAC 成员拥有知识、技能和 解决路易斯安那州立大学 SRP 所要解决的复杂的多学科问题所必需的经验。我们 将利用路易斯安那州立大学系统内的最新进展实施全面的数据管理策略 高速计算和数据分发，以及稳定、安全的数据采集、管理和处理 促进多学科合作的存储平台。我们的核心致力于促进 通过采用软件进行透明且可重复的研究，提供带时间戳的版本 控制文档、文件和代码，例如开放科学框架和工作流 R 包 用于统计分析。 DMAC 生物统计学家将扩展超级基金研究可用的工具集 社区通过开发新的途径和方法来理解 EPFR 之间的关系 使用（多变量）多重中介分析以及使用 在微观结构路径研究中用于降维的可靠机器学习方法 EPFR 形成和衰减机制等进展。最后，DMAC 将开发并 以各种形式和场所促进一系列广泛的举措，以教育 SRP 研究人员、博士后 研究人员和研究生的主题包括有效的数据管理策略、研究设计 原则，以及进行透明、有效、可推广和可重复的研究。