Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10707480
• 负责人: Efstratios Pistikopoulos
• 金额: $ 22.27万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707480
• 关键词: Address; Adopted; Affect; Algorithms; Analysis of Variance; Area; Benchmarking; Bioinformatics; Biological; Calibration; Chemical Exposure; Chemicals; Childhood; Classification; Code; Collaborations; Communities; Complex; Computer Analysis; Computer software; Data; Data Analyses; Data Analytics; Data Collection; Data Coordinating Center; Data Management Resources; Data Science; Data Science Core; Data Set; Deposition; Descriptor; Detection; Dimensions; Disasters; Documentation; Educational workshop; Elements; Emergency Situation; Ensure; Environmental Hazards; Evaluation; Event; Experimental Designs; Exposure to; FAIR principles; Frequencies; Funding; Generations; Hazardous Substances; Health; Human; Human Resources; In Vitro; Individual; Inflammatory; Infrastructure; Knowledge; Libraries; Lung; Machine Learning; Mass Spectrum Analysis; Mathematics; Measurement; Measures; Metadata; Methodology; Methods; Modeling; Online Systems; Outcome; Output; Pattern; Performance; Population; Pregnancy; Procedures; Protocols documentation; Publications; Quality Control; Recipe; Regression Analysis; Research; Research Personnel; Research Project Grants; Risk; Sampling; Science; Secure; Services; Spectrometry; Statistical Data Interpretation; Statistical Methods; Superfund; System; Techniques; Testing; Texas; Toxicokinetics; Training; Translating; Translational Research; Universities; Work; community engagement; complex data; computational platform; cytokine; data access; data dissemination; data management; data quality; data repository; data sharing; data standards; data visualization; detection method; experience; exposure pathway; feature selection; insight; interoperability; ion mobility; member; nonlinear regression; novel; organ on a chip; predictive modeling; quality assurance; respiratory; response; screening; shared repository; tool; toxicant; transcriptomics; web platform

Data Management & Analysis Core (DMAC) ABSTRACT The Texas A&M University Superfund Research Center aims to develop descriptive models and tools that can predict the possible hazardous outcomes of chemical exposure during environmental emergencies while providing powerful solutions that can mitigate their negative effects on human health. The Data Management & Analysis Core (DMAC) is one of the key components of the Center that will support all projects and cores in their data management, analysis, quality control needs. Directed by Dr. Efstratios N. Pistikopoulos and in collaboration with co-Investigators Dr. Fred A. Wright, Dr. Lan Zhou, and Dr. Candice Brinkmeyer-Langford, the DMAC will provide a number of essential services to the Center’s researchers by assisting them is achieving key environmental and biomedical outcomes under four specific aims: (i) providing a new platform for data management and sharing across the Center, (ii) applying best-practice analysis methods to Center data, (iii) developing new methods that are urgently needed to solve the problems posed in the Projects, and (iv) maintaining research and data quality control protocols for the Center. The DMAC will establish a data universe (“dataverse”) for data sharing, integration, and collaboration. The “dataverse” will be used to manage Center datasets where each component will securely deposit and access data through a web-based platform and ensure Center generated data comply with Findable, Accessible, Interoperable, and Reusable (FAIR) principles. The DMAC will also provide additional assistance in developing and utilizing advanced data science methodologies for translating raw experimental data into actionable insights and predictive models for all projects. Project 1 will perform and optimize ion mobility spectrometry and mass spectrometry analyses of complex environmental samples; DMAC will provide guidance on geospatial sampling, feature selection, and classification analysis. Project 2 will develop in vitro pediatric lung model to characterize respiratory risks from VOCs; DMAC will perform concentration-response modeling, nonlinear, and spatial modeling techniques to evaluate the respiratory risks from ambient VOCs. Project 3 will address pregnancy risk implications of exposures to hazardous substances by developing a feto-maternal interface organ-on-a-chip model; DMAC will provide expertise in hypothesis testing, regression analysis, and ANOVA testing for analyzing proinflammatory cytokine measures. Project 4 will utilize in vitro cultures and reverse toxicokinetic analysis to characterize hazards of environmental mixtures; DMAC will provide service in analyzing high-content screening data, high-throughput transcriptomics data, and will perform population variability analyses. Project 5 will study the mitigation of adverse health effects of chemicals through broad-acting sorption materials; DMAC will provide services for experimental design and statistical testing. The DMAC, working in concert with the Research Experience & Training Coordination Core, will provide data science training workshops for Center personnel. Finally, DMAC will develop Quality Assurance Project Plans to cover all aspects of quality assurance and control for all Center components.

数据管理与分析核心（DMAC）摘要 德克萨斯州A＆M大学超级基金研究中心旨在开发可描述性模型和工具 预测环境紧急情况下化学暴露的可能危险结果 提供强大的解决方案，可以减轻其对人类健康的负面影响。数据管理和 分析核心（DMAC）是该中心的关键组成部分之一，它将支持其所有项目和核心 数据管理，分析，质量控制需求。 Efstratios N. Pistikopoulos博士和合作 DMAC与共同研究员Fred A. Wright博士，Lan Zhou博士和Candice Brinkmeyer-Langford博士一起 通过协助他们为中心的研究人员提供许多基本服务正在实现关键 环境和生物医学结果以四个具体目的：（i）为数据提供新的平台 （ii）在中心进行管理和共享，（ii）将最佳实践分析方法用于中心数据，（iii） 开发迫切需要解决项目中问题的新方法，（iv） 维持中心的研究和数据质量控制协议。 DMAC将建立数据宇宙 （“数据”）用于数据共享，集成和协作。 “数据词”将用于管理中心 每个组件将通过基于Web的平台安全存入和访问数据的数据集并确保 中心生成的数据符合可发现的，可访问的，可互操作的和可重复使用的（公平）原则。这 DMAC还将为开发和使用高级数据科学方法提供其他帮助 将原始的实验数据转化为所有项目的可行见解和预测模型。项目1将 进行并优化复杂环境的离子迁移率和质谱分析 样品； DMAC将为地理空间采样，特征选择和分类分析提供指导。 项目2将开发体外的小儿肺模型，以表征VOC的呼吸道风险； DMAC将执行 浓度响应建模，非线性和空间建模技术，以评估呼吸风险 来自环境VOC。项目3将解决对危险物质暴露的妊娠风险的影响 通过开发feto-tromnal界面器官芯片模型； DMAC将在假设方面提供专业知识 测试，回归分析和ANOVA测试进行分析促炎性细胞因子测量。项目4 Will 利用体外培养物并反向毒性分析来表征环境混合物的危害； DMAC将在分析的高通用筛选数据，高通量转录组学数据和 将执行人口变异性分析。项目5将研究缓解不良健康影响的 通过广泛的吸附材料化学物质； DMAC将为实验设计提供服务， 统计测试。 DMAC与研究经验与培训协调核心合作， 将为中心人员提供数据科学培训研讨会。最后，DMAC将建立质量保证 项目计划涵盖所有中心组件的质量保证和控制的各个方面。