The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences

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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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Contents

The Basic Reproducible Workflow Template
19
Lessons Learned
41
Building toward a Future Where Reproducible
61
Glossary
71
HIGHLEVEL CASE STUDIES
93
A Reproducible R Notebook Using Docker
109
Turning Simulations of Quantum Many
125
Feature Extraction and Data Wrangling
139
A Dissection of Computational Methods
215
Reproducibility in Human Neuroimaging
233
Reproducible Computational Science
241
LOWLEVEL CASE STUDIES
263
ProblemSpecific Analysis of Molecular
277
Producing a Journal Article on Probabilistic
291
Generation of Uniform Data Products
305
Developing a Reproducible Workflow
311

Analyzing Bat Distributions in a Human
155
Analyzing Cosponsorship Data to Detect
169
Achieving Full Replication of Our
191

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About the author (2017)

Justin Kitzes is Assistant Professor of Biology at the University of Pittsburgh.

Daniel Turek is Assistant Professor of Statistics at Williams College.

Fatma Deniz is Postdoctoral Scholar at the Helen Wills Neuroscience Institute and the International Computer Science Institute, and Data Science Fellow at the University of California, Berkeley.

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