The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences
Justin Kitzes, Daniel Turek, Fatma Deniz
Univ of California Press, Oct 17, 2017 - Science - 337 pages
The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research.
Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible.
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The Basic Reproducible Workflow Template
Building toward a Future Where Reproducible
HIGHLEVEL CASE STUDIES
A Reproducible R Notebook Using Docker
Turning Simulations of Quantum Many
Feature Extraction and Data Wrangling
A Dissection of Computational Methods
Reproducibility in Human Neuroimaging
Reproducible Computational Science
LOWLEVEL CASE STUDIES
ProblemSpecific Analysis of Molecular
Producing a Journal Article on Probabilistic
Generation of Uniform Data Products
Developing a Reproducible Workflow
Other editions - View all
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