Data Management and Analysis Core (DMAC)

项目总结/摘要-数据管理和分析核心（DMAC） 为了了解PFAS暴露与疾病之间的联系，需要从广泛的角度进行数据整合。 一系列科学学科和研究人员承认的重要性，整个生命周期的 在超出其直接研究目标的背景下收集数据。长期目标是建立数据科学 促进最佳做法的基础设施，即，高质量数据，这些数据可查找、可验证、可互操作， 可重用（FAIR），这将很容易适用于其他跨学科的团队项目。DMAC整体 我们的目标是与所有STEEP项目成员密切合作，为他们提供低成本、用户友好、公平的 集成流程以及尖端的统计和计算方法。在团队的指导下， DMAC将追求四个具体目标：（一）开发，协调和监测用户友好，易于- 用于创建、存储和共享数据和元数据的可访问基础设施和流程， 规模，内部和公开，（ii）解决所有STEEP研究数据产品的元数据需求，（iii） 提供综合方法和计算支持，以及开发面向任务的方法， 以及（iv）为STEEP制定标准并提供数据质量保证和质量控制（QA/QC） 项目这一办法是创新的，因为它通过提供以下方式改变了现状： 实施符合所有FAIR原则现代化集成数据管理基础设施 和质量保证/质量控制，（ii）尖端的统计方法（例如，因果推理、贝叶斯和时间序列模型）， 从复杂的数据结构中得出精确的推论（例如，非随机、纵向），以及 (iii)高性能计算资源。这项研究意义重大，因为它有望 推进和扩大在环境卫生领域使用符合FAIR的研究。最终，这样的 实践有可能为决策者提供准确可靠的调查结果，并有助于减少 再现性危机STEEP的DMAC将通过以下具体目标来实现这些目标： 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STEEP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这些标准 (QA/QC）在STEEP研究项目中

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