|
Abstract:
|
Modern data analysis is typically quite computational. Correspondingly, sharing scientific and statistical work now often means sharing code and data in addition writing papers and giving talks. This type of code sharing faces several challenges. For example, it is often difficult to take code from one computer and run it on another due to software configuration, version, and dependency issues. Even if the code runs, writing code that is easy to understand or interact with can be difficult. This makes it difficult to assess third-party code and its findings, for example, in a review process. In this talk we describe a 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. This talks reviews both the problems at the high-level and also provides concrete solutions to the challenges faced.
|
