Data Management and Analysis Core

项目摘要 肯塔基州大学超级基金研究中心（UK-SRC）数据管理和分析核心 （DMAC）的目标是提供一个总体技术和研究支持基础设施， 数据和信息资产的管理和集成。鉴于跨学科研究需要 研究人员使用来自一系列学科的方法和数据，这一目标解决了以下关键需求： 跨学科研究，以克服阻碍进展的特定学科方法所带来的障碍 当许多学科试图共享数据时。DMAC的设计与FAIR（Findable， 可评估、可互操作和可重复使用）数据原则，以鼓励数据使用者之间的互动， 数据具体目标是：1）项目和核心的协调; 2）促进数据集成，共享， 互操作性; 3）确保数据质量保证和质量控制（QA/QC）。要管理的数据集 并在DMAC内策划，涵盖基础研究到转化工作的全方位，从动物- 从评估有害物质补救的实验研究到化学分析， 社区参与活动。DMAC将定期与项目/核心领导人接触， 优先考虑数据集。它将简化分析资源、数据管理、数据质量验证和数据 跨项目和核心的集成，以提高效率和可重复性，增强项目协调， 促进资源共享。DMAC将通过纳入FAIR指南来促进互操作性 数据仪表板、数据存档及其查询探索界面中的原则， 更好地融合。通过制定共同术语、数据词典、培训等，DMAC将 促进共同的数据资源，以促进英国SRC内外的共享。促进互动 与调查人员和受训人员将有机会（现场和电子）与 生物统计学家，数据科学家和信息学家，可以进行正式和非正式培训。这些 活动将与研究经验培训协调核心密切协调和宣传 （RETCC）。DMAC将创建一个新的“为什么环境”模块，以使项目和核心能够参与 在研究者发起的研究翻译活动中，使他们能够形成新的问题和假设， 可以在项目和核心中进行测试，并在UK-SRC范围之外共享。最后，DMAC将 纳入地球数据观测网络（DataONE.org）概述的最佳做法。将建立 制定数据质量保证/质量控制计划，以便UK-SRC研究不会遇到与质量差相关的陷阱 数据

PROJECT SUMMARY The University of Kentucky Superfund Research Center (UK-SRC) Data Management and Analysis Core (DMAC) has a goal to provide an overarching technology and research support infrastructure for the management and integration of data and information assets. Given that interdisciplinary research requires researchers to use methods and data from a range of disciplines, this goal addresses a critical need for interdisciplinary research to overcome hurdles posed by discipline-specific methods that impede progress when many disciplines attempt to share data. The DMAC is designed in alignment with FAIR (Findable, Assessable, Interoperable, and Reusable) data principles to encourage interaction of data users and sharing of data. The specific aims are: 1) coordination of projects and cores; 2) foster data integration, sharing, and interoperability; 3) ensure data quality assurance and quality control (QA/QC). The datasets to be managed and curated within the DMAC, encompass the full range of basic research to translational work, from animal- based studies to chemical analysis, from experimental studies evaluating remediation of hazardous substances to community engagement activities. The DMAC will engage regularly with project/core leaders to prioritize data sets. It will streamline analytic resources, data management, data quality validation, and data integration across projects and cores to improve efficiencies and reproducibility, enhance project coordination, and promote resource sharing. The DMAC will promote interoperability by incorporating FAIR guiding principles in its data dashboards, data archives, and its query exploration interface to permit projects and cores to better integrate. By developing common terminologies, data dictionaries, training, etc., the DMAC will facilitate a common data resource to foster sharing within, and beyond the UK-SRC. To facilitate interaction with investigators and trainees there will be (on-site and electronic) opportunities for regular interaction with biostatisticians, data scientists, and informaticians where formal and informal training can occur. These activities will be coordinated and advertised closely with the Research Experience Training Coordination Core (RETCC). The DMAC will create a new WHY ENVIRONMENT module to enable projects and cores to engage in investigator-initiated research translation activities to allow them to form new questions and hypotheses that can be tested in projects and cores and shared beyond the confines of UK-SRC. Lastly, the DMAC will incorporate best practices outlined by Data Observation Network for Earth (DataONE.org). It will establish a plan for data QA/QC so that UK-SRC research will not be subjected to pitfalls associated with poor quality data.