Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10349759
• 负责人: Efstratios Pistikopoulos
• 金额: $ 23.43万
• 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/10349759
• 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 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; Infrastructure; Knowledge; Libraries; Lung; Machine Learning; Mass Spectrum Analysis; Mathematics; Measurement; Measures; Metadata; Methodology; Methods; Modeling; Multivariate Analysis; Online Systems; Outcome; Output; Pattern; Performance; Population; Pregnancy; Problem Solving; 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; Supervision; System; Techniques; Testing; Texas; Toxicokinetics; Training; Translating; Translational Research; Universities; Work; analysis pipeline; base; community engagement; 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; tool; toxicant; transcriptomics

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