交互式可重复分析的容器化方法综述
A Review of Containerization for Interactive and Reproducible Analysis
摘要：
近几十年来，数据分析变得越来越计算化。相应地，这也改变了科学和统计工作的分担方式。例如，现在常见的是，底层分析代码和数据与期刊出版物和会议演讲一起提供。不幸的是，共享代码面临着一些挑战。首先，通常很难从一台计算机上获取代码并在另一台计算机上运行。代码配置、版本和依赖性问题通常使这一点具有挑战性。其次，即使代码运行，通常也很难理解或与分析交互。这使得很难评估守则及其结果，例如，在同行审查过程中。在这篇综述中，我们描述了两种计算技术的组合，有助于使分析共享，交互式和完全可重现。这些技术是（1）分析容器化，它利用虚拟化将分析、数据、代码和依赖关系完全封装成一种交互式和可共享的格式，以及（2）代码笔记本，一种用于与分析交互的文字编程格式。这两种技术的融合比单独使用任何一种技术都具有显著的优势。这篇评论调查了这种组合如何增强代码、分析和想法的可访问性和可重复性。
英文摘要：
In recent decades the analysis of data has become increasingly computational. Correspondingly, this has changed how scientific and statistical work is shared. For example, it is now commonplace for underlying analysis code and data to be proffered alongside journal publications and conference talks. Unfortunately, sharing code faces several challenges. First, it is often difficult to take code from one computer and run it on another. Code configuration, version, and dependency issues often make this challenging. Secondly, even if the code runs, it is often hard to understand or interact with the analysis. This makes it difficult to assess the code and its findings, for example, in a peer review process. In this review we describe the combination of two computing technologies that help make analyses shareable, interactive, and completely reproducible. These technologies are (1) analysis containerization, which leverages virtualization to fully encapsulate analysis, data, code and dependencies into an interactive and shareable format, and (2) code notebooks, a literate programming format for interacting with analyses. The fusion of these two technologies offers significant advantages over using either individually. This review surveys how the combination enhances the accessibility and reproducibility of code, analyses, and ideas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
