This work covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. Each part presents contributions from leaders who have developed software and other products that have advanced the field, including Sweave, open source software packages, and good programming practices. Supplementary material is available online.

Tools: knitr: A Comprehensive Tool for Reproducible Research in R. Reproducibility Using VisTrails. Sumatra: A Toolkit for Reproducible Research. CDE: Automatically Package and Reproduce Computational Experiments. Reproducible Physical Science and the Declaratron. Practices and Guidelines: Developing Open-Source Scientific Practice. Reproducible Bioinformatics Research for Biologists. Reproducible Research for Large-Scale Data Analysis. Practicing Open Science. Reproducibility, Virtual Appliances, and Cloud Computing. The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility-Open Science Collaboration. What Computational Scientists Need to Know about Intellectual Property Law: A Primer. Platforms: Open Science in Machine Learning. RunMyCode.org: A Research-Reproducibility Tool for Computational Sciences. Open Science and the Role of Publishers in Reproducible Research. Index.