Core E: Data Management and Analysis Core (DMAC)

核心E：数据管理和分析核心（DMAC）

• 批准号: 10116390
• 负责人: SUSAN G. HILSENBECK
• 金额: $ 12.72万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-28 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116390
• 关键词: Area; Aromatic Polycyclic Hydrocarbons; Big Data; Bioinformatics; Biological; Biomedical Engineering; Biometry; Cancer Center; Chemicals; Chronic lung disease; Clinical; Code; Collaborations; Communities; Complex; Consultations; Data; Data Analyses; Data Engineering; Detection; Education; Educational workshop; Engineering; FAIR principles; Funding; Galaxy; Grant; Individual; Information Management; Infrastructure; Journals; Laboratories; Lead; Life Cycle Stages; Malignant Neoplasms; Metadata; Methods; Monitor; Multiomic Data; Neurocognitive Deficit; Outcome; Phenotype; Policies; Premature Birth; Prevention; Process; Program Research Project Grants; Proteomics; Reproducibility; Research; Research Methodology; Research Personnel; Resources; Rice; Role; STEM research; Sampling; Superfund; Technology; Testing; Training and Education; Work; arm; base; biobank; data centers; data format; data integration; data management; deep learning; early life exposure; experience; graduate student; informatics shared resource; insight; learning progression; meetings; member; metabolomics; multidisciplinary; multiple omics; novel; programs; quality assurance; remediation; repository; secondary analysis; sharing platform; statistics; success; symposium; tool; user-friendly; web portal

Project Summary Although this is a revised submission of the Baylor-Rice Superfund Research Program (SRP) grant, the Data Management and Analysis Core (DMAC) is a completely new, and indeed, required core. The team represented by this Core is not new to data management or to multi- project programs of research and has a strong track-record of experience and accomplishment. The Data Management and Analysis Core (DMAC) will act as the central hub for the SRP. The DMAC will provide for storage, annotation, and integration of multidisciplinary data generated by the biomedical projects P2, P3, P4 and the engineering projects P1 and P5. The DMAC has the following aims: 1) Implement a robust and comprehensive Data Management Plan; 2) Provide expert analysis in the specialized areas of statistics and bioinformatics for all Projects; 3) Develop new SRP-related data and management and data analysis methods; 4) Provide data management and analysis education and training for the graduate students and postdoctoral trainees connected with Superfund projects. The DMAC will act as the central resource for storage and cross-project access to chemical, physical, biological, and multi-omics data generated by the SRP projects and cores. In collaboration with the SRP investigators, the DMAC will accept a wide variety of data formats, not just ‘big data’, and will systematically incorporate metadata describing samples and laboratory conditions. The DMAC will leverage the extensive experience and infrastructure of the Duncan Cancer Center’s Biostatistics and Informatics Shared Resource, especially for biobanking and sample information management. We will work with the engineering projects to extend this infrastructure to the engineering projects. The DMAC will carry out primary and integrative data analysis for the biomedical projects and engineering projects. The analysis arm of the DMAC will provide advanced integrative methods spanning both omics and engineering data, including omics/phenotypes/chemical data multivariate regression and deep learning. In consultation with the Administrative Core and SRP investigators, the DMAC will enact data governance policies, enabling appropriate role-based access to data, primary and secondary analyses for investigators from the SRP, and from other SRPs nation-wide. The DMAC will submit the data to appropriate national repositories for each data type, and enhance the ability of the scientific community to find and retrieve the data, guided by the FAIR principles.

项目摘要 尽管这是贝勒-赖斯超级基金研究计划（SRP）的修订版 数据管理和分析核心（DMAC）是一个全新的，事实上， 需要核心。这个核心所代表的团队对数据管理或多 项目研究计划，并有很强的经验和成就的跟踪记录。 数据管理和分析核心（DMAC）将作为SRP的中心枢纽。的 DMAC将提供多学科数据的存储、注释和整合， 生物医学项目P2、P3、P4和工程项目P1和P5。DMAC拥有 以下目标：1）实施强大而全面的数据管理计划; 2）提供 为所有项目提供统计和生物信息学专业领域的专家分析; 3） 新的SRP相关数据和管理及数据分析方法; 4）提供数据 研究生和博士后的管理和分析教育和培训 与超级基金项目有关的受训人员。DMAC将作为中央资源， 存储和跨项目访问化学、物理、生物和多组学数据 由SRP项目和核心生成。与SRP调查人员合作，DMAC 将接受各种各样的数据格式，而不仅仅是“大数据”，并将系统地整合 描述样本和实验室条件的元数据。DMAC将利用广泛的 邓肯癌症中心生物统计学和信息学的经验和基础设施 资源，特别是用于生物库和样本信息管理。我们将与 工程项目，以扩大这一基础设施的工程项目。DMAC将携带 为生物医学项目和工程项目提供初步的综合数据分析。 DMAC的分析部门将提供先进的综合方法， 和工程数据，包括组学/表型/化学数据多元回归， 深度学习经与行政核心和SRP调查员协商，DMAC将 制定数据治理策略，实现对数据、主要数据和 SRP和全国其他SRP研究者的次要分析。所述DMAC 将把每种数据类型的数据提交给适当的国家储存库，并提高 在公平原则的指导下，科学界寻找和检索数据。