Data Management and Analysis Core

数据管理与分析核心(DMAC)抽象 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，这些模型和工具可以 预测在环境紧急情况下接触化学品可能产生的危险后果 提供了强大的解决方案，可以减轻它们对人类健康的负面影响。数据管理与 分析核心(DMAC)是该中心的关键组件之一，它将支持其 数据管理、分析、质量控制需求。导演：埃夫斯特罗斯·N·皮斯蒂科普洛斯博士 与弗雷德·A·赖特博士、兰·周博士和坎迪斯·布林克迈尔-兰福德博士共同调查，DMAC将 为中心的研究人员提供一系列基本服务，帮助他们实现关键 四个具体目标下的环境和生物医学成果：(1)提供一个新的数据平台 中心的管理和共享，(Ii)将最佳实践分析方法应用于中心数据，(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将建立一个数据世界 (“数据中心”)，用于数据共享、集成和协作。“数据中心”将用于管理中心 每个组件将通过基于Web的平台安全地存储和访问数据的数据集，并确保 中心生成的数据符合可查找、可访问、可互操作和可重复使用(公平)的原则。这个 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。 用于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率和质谱分析 样本；DMAC将在地理空间抽样、特征选择和分类分析方面提供指导。 项目2将开发体外儿科肺部模型，以表征VOCs的呼吸风险；DMAC将执行 浓度-反应模型、非线性和空间模型技术用于评估呼吸风险 来自环境中的VOCs。项目3将处理接触有害物质对怀孕风险的影响 通过开发母胎接口芯片上器官模型，DMAC将提供假说方面的专业知识 用于分析促炎细胞因子措施的检验、回归分析和方差分析。项目4将 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将提供分析高内容筛选数据、高通量转录数据和 将进行人口变异性分析。项目5将研究减少对健康的不良影响 通过广效吸附材料获得化学品；DMAC将为实验设计和 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讲习班。最后，DMAC将发展质量保证 项目计划涵盖所有中心组件的质量保证和控制的所有方面。

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.