Data Management and Analysis Core

数据管理与分析核心

基本信息

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
项目类别：
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

项目摘要

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.

数据管理和分析核心 (DMAC) 摘要德克萨斯农工大学超级基金研究中心旨在开发描述性模型和工具预测环境紧急情况期间化学品暴露可能产生的危险后果，同时提供强大的解决方案，可以减轻其对人类健康的负面影响。分析核心 (DMAC) 是该中心的关键组件之一，它将支持其所有项目和核心数据管理、分析、质量控制需求由 Efstratios N. Pistikopoulos 博士指导并合作。 DMAC 将与联合调查员 Fred A. Wright 博士、Lan Zhou 博士和 Candice Brinkmeyer-Langford 博士一起通过协助中心的研究人员提供一些基本服务是实现这一目标的关键四个具体目标下的环境和生物医学成果：(i) 提供新的数据平台整个中心的管理和共享，(ii) 对中心数据应用最佳实践分析方法，(iii) 开发解决项目中提出的问题迫切需要的新方法，以及（iv） DMAC 将建立一个数据宇宙，维护该中心的研究和数据质量控制协议。（“dataverse”）用于数据共享、集成和协作“dataverse”将用于管理中心。数据集，其中每个组件将通过基于网络的平台安全地存储和访问数据，并确保中心生成的数据符合可查找、可访问、可互操作和可重用 (FAIR) 原则。 DMAC 还将在开发和利用先进数据科学方法方面提供额外帮助将原始实验数据转化为所有项目的可操作的见解和预测模型。对复杂环境进行并优化离子迁移谱和质谱分析样本；DMAC 将提供地理空间采样、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 带来的呼吸风险；用于评估呼吸风险的浓度反应模型、非线性和空间建模技术项目 3 将解决接触有害物质对怀孕的风险影响。通过开发胎儿-母体接口器官芯片模型，DMAC 将提供假设方面的专业知识；项目 4 将进行用于分析促炎细胞因子测量的测试、回归分析和方差分析测试。利用体外培养和反向毒代动力学分析来表征环境混合物的危害； DMAC将提供分析高内涵筛选数据、高通量转录组数据和将进行人口变异性分析项目 5 将研究减轻对健康的不利影响。 DMAC 将通过广泛作用的吸附材料提供化学物质的实验设计和服务； DMAC 与研究经验和培训协调核心合作，最后，DMAC 将为中心人员提供数据科学培训研讨会。项目计划涵盖中心所有组成部分的质量保证和控制的各个方面。