A Training Module for Reproducible Data Science Research

摘要 科学的进步取决于科学家将他们的研究细节 调查，让其他人学习新的技术和程序，并批判性地审查 导致任何重大发现的过程。然而，这一基本方面的 科学发展面临重大挑战。计算技术的快速发展导致了 高通量数据收集加上复杂的统计应用， 用于数据分析的算法。因此，几乎不可能描述 科学的过程中，精确地使用传统的沟通方法。复配 沟通数据分析复杂性的问题是传统教育无法 计划，以跟上技术和方法的变化。数据的短缺 分析技能和相应的缺乏透明度的科学过程是在 当今科学中再现性和复制性危机的核心。为了解决 科学的不可复制性问题，需要在良好的基本方面进行培训， 数据分析和可重复性研究。这种培训需要超越传统的 这些方法侧重于开发统计方法工具箱。虽然知识 工具及其属性是良好的数据分析所必需的，但这远远不够。额外 知识是需要的联合收割机这些工具，以产生一个健全的数据分析，在一个透明的 方式此外，我们必须超越传统的课堂学习方法， 覆盖整个科学劳动力。我们将建立提高数据科学的培训模块 利用约翰霍普金斯数据科学实验室成员最近的工作进行研究。 我们将重点关注两个主要方面：（1）可重复数据科学的策略，包括 设计好的数据分析、识别差的数据分析的高级原则，以及 提供对数据分析的适当评论;（2）技术和工作流程，包括 用于以可复制、可分发和可重用的方式进行数据分析的软件工具。 本项目开发的材料将补充传统的培训方案， 生物医学数据科学领域，并将完全开源供他人使用， 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.