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A&M大学超级基金研究中心旨在开发描述性模型和工具， 预测在环境紧急情况下接触化学品可能产生的危险后果， 提供强有力的解决方案，减轻其对人类健康的负面影响。数据管理& 分析核心（DMAC）是中心的关键组成部分之一，将支持所有项目和核心， 数据管理、分析、质量控制需求。导演：Efstratios N. Pistikopoulos和合作 与共同研究员弗雷德·A·Wright、Lan Zhou博士和Candice Brinkmeyer-Langford博士，DMAC将 为中心的研究人员提供一些基本服务，帮助他们实现关键目标。 环境和生物医学成果的四个具体目标：（一）提供一个新的数据平台， 管理和共享整个中心，（ii）应用最佳实践分析方法的中心数据，（iii） 开发迫切需要的新方法来解决项目中提出的问题，以及（iv） 维护中心的研究和数据质量控制协议。DMAC将建立一个数据宇宙 （“DataVerse”）进行数据共享、集成和协作。“数据宇宙”将用于管理中心 数据集，其中每个组件将通过基于Web的平台安全地存款和访问数据，并确保 中心生成的数据符合可查找、可解释、可互操作和可重用（FAIR）原则。的 DMAC还将在开发和利用先进的数据科学方法方面提供额外的援助 将原始实验数据转化为可操作的见解和所有项目的预测模型。项目1将 执行和优化复杂环境的离子迁移谱和质谱分析 DMAC将提供有关地理空间采样、特征选择和分类分析的指导。 项目2将开发体外儿科肺模型，以表征VOC的呼吸风险; DMAC将执行 浓度-响应建模、非线性和空间建模技术，以评价呼吸风险 环境VOC。项目3将讨论接触危险物质对怀孕的风险影响 通过开发胎儿-母体界面器官芯片模型; DMAC将提供假设方面的专业知识 检验、回归分析和ANOVA检验，用于分析促炎细胞因子测量。项目4将 利用体外培养和反向毒代动力学分析来确定环境混合物的危害; DMAC将提供分析高内容筛选数据，高通量转录组学数据， 将进行群体变异性分析。项目5将研究如何减轻对健康的不利影响 通过广泛作用的吸附材料的化学品; DMAC将提供服务的实验设计和 统计检验DMAC与研究经验和培训协调核心合作， 将为中心人员提供数据科学培训研讨会。最后，DMAC将制定质量保证 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。