Data Management Core

数据管理核心

• 批准号: 10697344
• 负责人: Justin Guinney
• 金额: $ 134.38万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-26 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10697344
• 关键词: Acceleration; Accent; Address; Area; Automobile Driving; Bioinformatics; Biomedical Research; Clinical; Clinical Data; Collaborations; Collection; Communities; Complex; Consensus; Data; Data Analyses; Data Collection; Data Coordinating Center; Data Discovery; Data Management Resources; Data Pooling; Development; Disease; Disparity; Down Syndrome; Elements; Ensure; Environment; FAIR principles; Funding; Goals; Human Resources; Image; Individual; Ingestion; Investigation; Leadership; Longevity; Maps; Metadata; Mission; Molecular; Multiomic Data; Pathway interactions; Persons; Phenotype; Play; Population; Positioning Attribute; Process; Publishing; Qualifying; Quality Control; Reporting; Research; Research Project Grants; Resources; Role; Secure; Site; Source; Standardization; Stress Tests; Structure; Technology; United States National Institutes of Health; Validation; Vision; Work; biobank; cloud based; cohort; community building; comorbidity; computerized data processing; dashboard; data ecosystem; data exchange; data harmonization; data ingestion; data management; data modeling; data portal; data quality; data sharing; data sharing networks; data standards; diverse data; experience; human disease; improved outcome; innovation; insight; interoperability; member; multiple data types; novel; outreach; repository; trait; usability; virtual; working group

PROJECT SUMMARY - Data Management Core. The key objective of the Data Management Core (DMC) of the INCLUDE Data Coordinating Center (DCC) is to develop and sustain data sharing pathways to fulfill the Center’s mission to study a large cohort of people with Down syndrome (DS) to accelerate research in the mechanisms driving and modulating comorbidities in this population. The DMC will build upon the processes and platforms we have deployed in prior DCCs to facilitate the data ingestion and harmonization of high priority clinical, biospecimen, and molecular data identified by the INCLUDE community. Our vision - shared and augmented by our partner teams in the DCC cores - is to ensure that data from INCLUDE cohorts are highly accessible and (re)usable by the research community to drive integrative and innovative approaches to DS research, and to spark insights from and within adjacent domains with shared biomolecular and phenotypic traits. To accomplish this ambitious goal, we will first develop a data governance structure to maximize use and re-use of INCLUDE data in order to address disparities and improve outcomes for people with DS . Second, in coordination with INCLUDE stakeholders, we will document use-cases to ensure the activities of the DCC reflect the priorities and concerns of the DS community, and map these use-cases to data models and standards to support the harmonization and interoperability of INCLUDE data. We will leverage and extend existing data models from the biomedical commons communities, and define quality control metrics for INCLUDE data types. Third, we will enhance our platform interfaces to support the transfer, validation, and aggregation of INCLUDE data within our data management system. DMC personnel will work hand-in-hand with data contributors to ensure the rapid and conformant transfer of multiple data types. Fourth, we will develop bioinformatic workflows and pipelines for the standardized processing of INCLUDE data within an integrated and highly scalable compute environment. Fifth, we will build and deploy a virtual biorepository to enable federated biospecimen sharing across the INCLUDE network and the DS research community. The DMC team - in close coordination with the Administrative and Outreach Core and the Data Portal Core - will leverage our deep experience in data management and coordination, community building, and biomedical research to create a world-class resource for data sharing, access, and integrative analysis that would enable novel investigations into DS and its comorbidities across the lifespan.

项目摘要-数据管理核心。 INCLUDE数据协调中心（DCC）的数据管理核心（DMC）的主要目标是 开发和维持数据共享途径，以履行中心的使命，研究一大批患有 唐氏综合征（DS），以加速研究机制的驱动和调节合并症，在这方面， 人口DMC将建立在我们在以前的DCC中部署的流程和平台的基础上， 数据摄取和协调高优先级的临床、生物标本和分子数据， 包括社区。我们的愿景-与DCC核心的合作伙伴团队共享并增强-是确保 来自INCLUDE队列的数据是研究界高度可访问和（可重复）使用的， 综合和创新的方法，以DS研究，并激发洞察力，并在相邻领域 具有共同的生物分子和表型特征。 为了完成这一雄心勃勃的目标，我们将首先制定一个数据治理结构，以最大限度地利用和重复利用 INCLUDE数据，以解决差异并改善DS患者的结果。二是 与INCLUDE利益相关者协调，我们将记录用例，以确保DCC的活动反映 DS社区的优先级和关注点，并将这些用例映射到数据模型和标准， 支持INCLUDE数据的协调和互操作性。我们将利用和扩展现有数据 模型，并定义INCLUDE数据类型的质量控制指标。 第三，我们将增强我们的平台接口，以支持INCLUDE的传输、验证和聚合 我们的数据管理系统中的数据。DMC人员将与数据贡献者携手合作， 确保多种数据类型的快速一致传输。第四，我们将开发生物信息学工作流程 和管道，用于在集成的高度可扩展的计算中对INCLUDE数据进行标准化处理 环境第五，我们将建立和部署一个虚拟生物储存库，以实现联合生物标本共享 在INCLUDE网络和DS研究社区中。 灾害管理委员会小组将与行政和外联核心以及数据门户核心密切协调， 利用我们在数据管理和协调、社区建设和生物医学方面的丰富经验， 研究，以创建一个世界级的数据共享，访问和综合分析资源，使 对DS及其在整个生命周期内的合并症进行的新研究。