Data Management and Analysis Core (DMAC)

项目摘要/摘要-数据管理和分析核心(DMAC) 为了了解全氟辛烷磺酸暴露与疾病之间的联系，有必要从广泛的角度整合数据 科学学科的范围，并让研究人员认识到 在超出其直接研究目标的背景下的数据。其长期目标是建立一门数据科学 促进最佳做法的基础设施，即可查找、可访问、可互操作和 可重用(公平)，这将很容易应用于其他跨学科团队项目。DMAC的总体 目标是与所有项目成员密切合作，为他们配备低成本、方便使用、公平的- 综合流程，以及尖端的统计和计算方法。在团队的指导下 经验，DMAC将追求四个具体目标：(I)开发、协调和监测一个用户友好、容易- 用于创建、存储和共享数据和元数据的可访问基础架构和流程，无论 内部和公开的规模，(2)满足所有陡峭的研究数据产品的元数据需求，(3) 提供综合方法和计算支助，并开发面向任务的方法， 以及(4)制定标准并提供数据质量保证和质量控制(QA/QC) 项目。这种方法是创新的，因为它不同于现状，它提供：(I)一种易于- 实施符合所有公平原则的现代化集成数据管理基础架构 和QA/QC，(Ii)尖端统计方法(例如，因果推断、贝叶斯和时间序列模型) 从复杂的数据结构(例如，非随机的、纵向的)中得出数学上精确的推论，以及 (三)高性能计算资源。这项拟议的研究意义重大，因为预计它将 推进和扩大公平合规研究在环境健康领域的使用。最终，这样的 实践有可能为政策制定者提供准确可靠的结果，并有助于减少 重现性危机。斯特雷特的DMAC将通过以下具体目标实现这些目标： 具体目标1：发展和支持共享数据和元数据的基础设施和进程 具体目标2：满足所有陡峭研究数据产品的元数据需求： 具体目标3：提供综合统计支助 具体目标4：制定数据质量保证和质量控制标准并提供数据质量保证和质量控制 (QA/QC)跨陡峭的研究项目

PROJECT SUMMARY/ABSTRACT – DATA MANAGEMENT AND ANALYSIS CORE (DMAC) To understand the link between PFAS exposure and disease, there is a need for data integration from a broad range of scientific disciplines and for researchers to acknowledge the importance of the entire lifecycle of the data in a context beyond their immediate research objective. The long-term goal is to establish a data science infrastructure that promotes best practice, i.e., high-quality data that are Findable, Accessible, Interoperable, and Reusable (FAIR), and that will be easily applicable to other interdisciplinary team projects. DMAC’s overall objective is to work closely with all STEEP project members and equip them with low-cost, user-friendly, FAIR- integrated processes, as well as cutting-edge statistical and computing methods. Guided by the team’s experience, DMAC will pursue four specific aims: (i) develop, coordinate, and monitor a user-friendly, easily- accessible infrastructure and processes for creating, storing, and sharing data and metadata, irrespective of size, both internally and publicly, (ii) address metadata needs across all STEEP research data products, (iii) provide integrative methodological and computational support, as well as develop mission-oriented methods, and (iv) develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP projects. The approach is innovative because it departs from the status quo by providing: (i) an easy-to- implement, modern, and integrative data management infrastructure that is compliant with all FAIR principles and QA/QC, (ii) cutting-edge statistical methods (e.g., causal inference, Bayesian, and time series models) to draw mathematically-precise inferences from complex data structures (e.g., non-randomized, longitudinal), and (iii) high-performance computing resources. The proposed research is significant because it is expected to advance and expand the use of FAIR-compliant research in the field of environmental health. Ultimately, such practice has the potential to inform policy makers with precise and reliable findings and help reduce the reproducibility crisis. STEEP’s DMAC will pursue these goals via these Specific Aims: Specific Aim 1: Develop and support infrastructure and processes for sharing data and metadata Specific Aim 2: Address metadata needs across all STEEP research data products: Specific Aim 3: Provide integrative statistical support Specific Aim 4: Develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP research projects