Core D: Data Analysis and Research Translation Core

核心D：数据分析和研究翻译核心 摘要 该计划项目的一个主要主题是开发新的统计方法，这些方法 外部调查人员在分析其数据时可以理解和使用。为了完成 这项计划的两个主要目标是演示新方法在实际数据中的应用， 并使我们的方法能够为广大研究人员所用。核心D将促进 这些目标。首先，核心将为新的统计数据的基准建立“主”数据库 方法以及发表新的生物学发现。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， 并与核心B合作，将从外部数据资源提取的数据合并到主数据库中。 第二，核心将协助实施数据分析的最佳做法并提供咨询意见，重点是 再现性。为了确保重现性，我们将利用嵌入式技术，例如容器(例如， Docker)、版本控制软件(例如Git)和数据共享工具(例如Zenodo)。最后，我们将发展 教育材料和举办训练营和黑客节，向外部调查人员传授 从该程序生成的软件工具和数据集。这样的论坛将有助于1)评估套餐， 2)加速科学发现，3)将开发的方法推广到更广泛的科学领域 社区。

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.