A Training Module for Reproducible Data Science Research

摘要 科学进步取决于科学家交流其研究成果细节的能力 调查，允许其他人学习新的技术和程序并进行批判性审查 导致任何重大发现的过程。然而，这一基础方面 科学进程面临重大挑战。计算技术的快速发展导致了 将高通量数据收集与复杂统计的应用相结合 用于数据分析的算法。因此，几乎不可能描述 科学过程中精确使用传统的沟通方式。使之复杂化 沟通数据分析的复杂性问题是传统教育的无能为力 计划，以跟上技术和方法的变化。数据的短缺 关于科学过程的分析技能和相应的缺乏透明度的问题在 当今科学的可再生性和复制危机的核心。为了解决 科学上的不可再生性问题，需要在善的基本方面进行培训 数据分析和可重复研究。这样的培训需要超越传统的 侧重于开发统计方法工具箱的方法。虽然知道 工具及其属性是良好的数据分析所必需的，但这远远不够。其他内容 要将这些工具结合在一起，以透明的方式生成可靠的数据分析，需要知识 举止。此外，我们必须超越传统的课堂学习方法，以便 接触到整个科学工作者。我们将构建培训模块，以提高数据科学水平 通过利用约翰霍普金斯数据科学实验室成员最近所做的工作进行研究。 我们将重点关注两个主要方面：(1)可复制数据科学的战略，包括 设计好的数据分析、识别差的数据分析的更高级别的原则，以及 对数据分析提供适当的批评；以及(2)技术和工作流程，包括 以可重复、可分发和可重复使用的方式进行数据分析的软件工具。 在这个项目中开发的材料将补充 生物医学数据科学领域，并将完全开源，供其他人使用和 适应。

Abstract Scientific progress depends on the ability of scientists to communicate the details of their investigations, allowing others to learn new techniques and procedures and to critically review the process leading to any significant findings. However, this foundational aspect of the scientific process faces significant challenges. Rapid advances in computing technology have led to high-throughput data collection coupled with the application of complex statistical algorithms for data analysis. As a result, it has become nearly impossible to describe the scientific process precisely using traditional methods of communication. Compounding the problem of communicating data analytic complexity is the inability of traditional educational programs to keep up with technological and methodological changes. The shortage of data analytic skills and the corresponding lack of transparency regarding the scientific process is at the very core of the reproducibility and replication crisis in science today. In order to address the problem of scientific irreproducibility, training is needed in the fundamental aspects of good data analysis and reproducible research. Such training needs to go beyond traditional approaches which focus on developing a toolbox of statistical methods. While knowledge of tools and their properties is necessary for good data analysis, it is far from sufficient. Additional knowledge is required to combine those tools to produce a sound data analysis in a transparent manner. Furthermore, we must go beyond traditional methods of classroom learning in order to reach the entire scientific workforce. We will build training modules for improving data science research by leveraging recent work done by members of the Johns Hopkins Data Science Lab. We will focus on two primary tracks: (1) strategies for reproducible data science, which include the higher-level principles for designing good data analyses, recognizing poor data analysis, and providing a proper critique of a data analysis; and (2) technologies and workflows, which cover the software tools for doing data analysis in a reproducible, distributable, and reusable manner. The materials developed in this project will supplement traditional training programs in biomedical data science fields and will be made entirely open source for others to use and adapt.