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Implementing Reproducible Research

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accurately reproduce a scientific result
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B01=Friedrich Leisch
B01=Roger D. Peng
B01=Victoria Stodden
BioMed Central
Category1=Non-Fiction
Category=GPS
Category=PBT
Category=PS
Category=UFM
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Cloud Computing
cloud computing in reproducible research
cloud-based scientific workflows
Code Chunks
computational science methods
conducting and distributing reproducible research
COP=United States
Current Intellectual Property Law
Delivery_Delivery within 10-20 working days
Distributed Version Control Systems
DVCS
empirical research replication
Encode Consortium
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Full Text Open Access
HEC Paris
IPython Notebook
Language_English
National Library
open data licensing
Open Source
Open Source Model
Open Source Software
PA=Available
PDF Publication
Price_€50 to €100
PS=Active
Pull Request
Replication Teams
reproducibility in computational science
reproducible computational experiments guide
Reproducible Research
reproducible research in bioinformatics and large-scale data analyses
Reproducible Research Standard
Research Articles
scientific data sharing
scientific programming practices
softlaunch
STM Publishing
VCS
Version Control
Version Control Systems

Product details

  • ISBN 9781466561595
  • Weight: 748g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Apr 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

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.

About
Victoria Stodden