Implementing Reproducible Research

Front Cover
Victoria Stodden, Friedrich Leisch, Roger D. Peng
CRC Press, Apr 14, 2014 - Mathematics - 448 pages

In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden.

Implementing Reproducible Research 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. It describes:

  • Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system
  • Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research
  • Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals

Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.

 

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Contents

knitr A Comprehensive Tool for Reproducible Research in R
3
Reproducibility Using VisTrails
33
Sumatra A Toolkit for Reproducible Research
57
CDE Automatically Package and Reproduce Computational Experiments
79
Reproducible Physical Science and the Declaratron
113
Practices and Guidelines
147
Developing OpenSource Scientific Practice
149
Reproducible Bioinformatics Research for Biologists
185
Reproducibility Virtual Appliances and Cloud Computing
281
The Reproducibility Project A Model of LargeScale Collaboration for Empirical Research on Reproducibility
299
What Computational Scientists Need to Know about Intellectual Property Law A Primer
325
Platforms
341
Open Science in Machine Learning
343
RunMyCodeorg AResearchReproducibility Tool for Computational Sciences
367
Open Science and the Role of Publishers in Reproducible Research
383
Back Cover
419

Reproducible Research for LargeScale Data Analysis
219
Practicing Open Science
241

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