Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10693813
• 负责人: Shesh N. Rai
• 金额: $ 16.82万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693813
• 关键词: Anacondas; Animal Experiments; Animals; Artificial Intelligence; Atmosphere; Basic Science; Bioinformatics; Biological Assay; Biometry; Cardiometabolic Disease; Characteristics; Clinical Investigator; Clinical Research; Clinical Trials; Code; Collaborations; Consultations; Data; Data Analyses; Data Collection; Data Files; Data Set; Data Storage and Retrieval; Databases; Development; Dimensions; Disease; Educational workshop; Ensure; Environment; Environmental Exposure; Evaluation; Experimental Designs; Exposure to; FAIR principles; Faculty; Fostering; Funding; Future; Gases; Geography; Hazardous Chemicals; Health; Health protection; Human; Human Resources; Individual; Institution; Linux; Machine Learning; Manuscripts; Maps; Mass Spectrum Analysis; Measurement; Methods; Modeling; Molecular; Operating System; Output; Participant; Population; Postdoctoral Fellow; Preparation; Prevention; Procedures; Process; Protocols documentation; Pythons; Quality Control; Research; Research Design; Research Personnel; Resources; Risk; Risk Assessment; Risk Estimate; Sampling; Scientist; Secure; Series; Services; Site; Source; Spatial Distribution; Specimen; Statistical Data Interpretation; Statistical Methods; Study Subject; Superfund; Testing; Toxic effect; Training; Translational Research; Universities; Validation; Variant; Visual; Work; archive data; cardiometabolism; central database; cohesion; computing resources; data curation; data dictionary; data exchange; data harmonization; data hub; data interoperability; data management; data quality; data sharing; data streams; data structure; data submission; design; detection limit; disorder risk; education resources; efficacy testing; epidemiology study; experience; experimental analysis; graduate student; improved; innovation; land use; large datasets; lipidomics; meetings; metabolomics; novel; novel strategies; operation; platform-independent; pollutant; portability; preclinical study; predictive modeling; privacy protection; quality assurance; remediation; repository; statistical service; tool; transcriptomics; usability; volatile organic compound

PROJECT SUMMARY The primary objective of the Sample and Data Management Core (DMAC) is to provide statistical support and data management and storage capabilities to all components of the University of Louisville Superfund Research Center (ULSRC). The functions of the Core are matched to the needs of each project and cover a full range of services from collaboration and routine service to protocol preparation to procedural review and oversight. The Core will provide two main services: it will establish a repository for the long-term storage and archiving of data; and it will assist Center investigators in the statistical analysis of their results. The core will assure the authenticity and quality of these specimens and it will prepare these samples for long-term storage, or dispense samples as needed. The Core will also provide scientifically valid and rigorous statistical analysis of data and support the development of innovative methods to enhance the basic and translational research efforts of Center investigators. It will provide state-of-the-art biostatistics and bioinformatics expertise and analytical support. Core biostatisticians will also develop new statistical methods, such as quantitative risk assessment models, multipollutant exposure analysis, and land-use regression models, for estimating cardiometabolic disease risk in exposed populations and for the analysis and evaluation of exposures and health effects. In collaboration with other projects and cores, DMAC, statisticians will harmonize the data by defining standards (e.g., creating searchable data dictionaries and defining variables relevant for each project. In the next funding cycle, we will continue to use these procedures. The core will serve as a unique educational resource for training graduate students, post-doctoral fellows, residents and junior faculty and in collaboration with the Training and Administration Cores. This Core will allow Center investigators easy access to high quality, centralized data management and statistical services, and thereby will strengthen the organizational cohesion of the Center. This comprehensive and integrated service permits one or more biostatisticians to be involved from the initial planning stage of a project (when statistical consultation is most beneficial) throughout its implementation, analysis and completion. The Core will provide a stable and collegial environment that fosters long-term working relationships between biostatisticians and investigators, and continues to promote sophisticated approaches to experimental design and analysis. The biostatisticians of this Core are knowledgeable about clinical research and have broad expertise in statistical applications for epidemiological investigations, clinical trials, pre-clinical studies, and prevention and control research. Their interdisciplinary interactions with basic scientists and clinical investigators will add a new dimension to the interpretation of experimental results, one that is only possible when collaborators share a mutual appreciation of problems and issues.

项目摘要 样本和数据管理核心（DMAC）的主要目标是提供统计支持和 路易斯维尔大学超级基金研究的所有组成部分的数据管理和存储功能 中心（ULSRC）。核心的功能与每个项目的需求相匹配，并涵盖了一个完整的范围 从协作和常规服务到协议准备的服务，再到程序审查和监督。这 Core将提供两个主要服务：它将建立一个用于长期存储和数据存档的存储库； 它将帮助中心研究人员对其结果的统计分析。核心将确保真实性 这些标本的质量，它将准备这些样品以进行长期存储，或分配样本作为 需要。核心还将提供有关数据的科学有效和严格的统计分析，并支持 开发创新方法来增强中心的基本和转化研究工作 调查人员。它将提供最先进的生物统计学和生物信息学专业知识和分析支持。核 生物统计学家还将开发新的统计方法，例如定量风险评估模型， 多币暴露分析和土地利用回归模型，用于估计心脏代谢疾病风险 暴露种群以及暴露和健康影响的分析和评估。与 其他项目和核心，DMAC，统计学家将通过定义标准来协调数据（例如，创建 可搜索的数据字典和定义与每个项目相关的变量。在下一个资金周期中，我们将 继续使用这些程序。核心将成为培训毕业生的独特教育资源 学生，博士后研究员，居民和初级教师，并与培训和 管理核心。该核心将允许中心调查人员轻松获取高质量的集中数据 管理和统计服务，从而增强中心的组织凝聚力。这 全面和综合的服务允许一名或多个生物统计学家在初始计划中参与 整个实施，分析和 完成。核心将提供一个稳定且合作的环境，以建立长期工作关系 生物统计学家和研究人员之间，并继续促进实验的先进方法 设计和分析。该核心的生物统计学家对临床研究知识渊博，并且已经广泛 在流行病学研究，临床试验，临床前研究和统计应用方面的专业知识 预防和控制研究。他们与基础科学家和临床研究人员的跨学科互动 将为实验结果的解释增加一个新的维度，只有在合作者时才可以 对问题和问题共享相互欣赏。