Core D: Data Analysis and Research Translation Core

核心D：数据分析和研究翻译核心 摘要 该计划项目的一个首要主题是开发新的统计方法， 外部调查人员在分析其数据时可以理解和使用。为了实现 本计划的两个主要目标是展示新方法在真实的数据中的应用， 并使我们的方法为广大的研究人员所接受。核心D将促进 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。 方法以及发表新的生物学发现。核心会把基本变量 （具体结果、预测因素、协变量等）从公共资源以及正在进行的研究中， 并与Core B协作，将从外部数据资源提取的数据合并到主数据库中。 第二，核心小组将协助实施数据分析的最佳做法并提供咨询意见，重点是 再现性为了确保可重复性，我们将利用嵌入式技术，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，我们将开发 教育材料，并运行培训靴子营地和黑客节日教外部调查人员使用 软件工具和数据集从这个程序生成。这样的论坛将有助于1）评估软件包， 2)加速科学发现，3）促进发展的方法更广泛的科学 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.