Core D: Data Analysis and Research Translation Core

核心D：数据分析与研究翻译核心

• 批准号: 10707479
• 负责人: KIMBERLY D SIEGMUND
• 金额: $ 21.32万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707479
• 关键词: Acceleration; Benchmarking; Biological; Biological Factors; Biological Process; Budgets; Cancer Etiology; Cancer Prognosis; Clinical Research; Colorectal Cancer; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Education; Educational Materials; Educational process of instructing; Ensure; FAIR principles; Genetic; Genomics; Goals; Grant; Happiness; Human Resources; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Multiomic Data; Outcome; Policies; Process; Prognosis; Program Accessibility; Publishing; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Running; Scientist; Series; Software Tools; Statistical Methods; Study Subject; Technology; Training; Translational Research; United States National Institutes of Health; Work; cancer risk; complex data; computing resources; data resource; data sharing; epidemiology study; falls; high dimensionality; insight; member; novel; programs; research study; tool; training project

Core D: Data Analysis and Research Translation Core ABSTRACT An overarching theme of this Program Project is to develop novel statistical methods that are understandable and useable by outside investigators in the analysis of their data. In order to accomplish this, two of the major goals of this Program are to demonstrate the application of new methods to real data, and to make our methods accessible to a broad community of researchers. Core D will facilitate both of these goals. First, the Core will generate ‘master’ databases for the benchmarking of new statistical methods as well as publishing novel biological findings. The Core will synthesize essential variables (specific outcomes, predictors, covariates, etc.) from public resources as well as ongoing research studies, and work with Core B to merge data drawn from external data resources into the master databases. Second, the Core will assist and advise on implementing best practices for data analysis with a focus on reproducibility. To ensure reproducibility we will exploit embed technologies such as containers (e.g., Docker), version control software (e.g., Git), and data sharing tools (e.g., Zenodo). Finally, we will develop educational material and run training boot camps and hack fests to teach outside investigators use of the software tools and datasets generated from this program. Such forums will help 1) evaluate the packages, 2) accelerate scientific discoveries, and 3) promote the developed methods to the broader scientific community.

核心D：数据分析与研究翻译核心 抽象的 该计划项目的首要主题是开发新颖的统计方法 外部研究人员在分析数据时可以理解和使用。为了实现 该计划的两个主要目标是演示新方法在实际数据中的应用， 并使我们的方法可供广大研究人员使用。核心D将促进两者 这些目标。首先，核心将生成“主”数据库，用于新统计数据的基准测试 方法以及发表新颖的​​生物学发现。核心将综合基本变量 （具体结果、预测因素、协变量等）来自公共资源以及正在进行的研究， 并与Core B合作将从外部数据资源提取的数据合并到主数据库中。 其次，核心将协助实施数据分析最佳实践并提供建议，重点是 再现性。为了确保可重复性，我们将利用容器等嵌入技术（例如， Docker）、版本控制软件（例如 Git）和数据共享工具（例如 Zenodo）。最后我们将开发 教育材料并举办训练营和黑客盛宴，以教导外部调查人员如何使用 从该程序生成的软件工具和数据集。此类论坛将有助于 1) 评估软件包， 2) 加速科学发现，3) 将已开发的方法推广到更广泛的科学领域 社区。