The Practice of Reproducible Research
Language: English
Published by University of California Press, US, 2017
- Hardcover
- New

Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
AbeBooks seller since June 10, 2025
Condition: New
US$ 113.72
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Add to basketItem description from seller
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. Each of the thirty-one case studies in this volume describes the workflow that an author used to complete a real-world research project, highlighting how particular tools, ideas, and practices have been combined to support reproducibility. Authors emphasize the very practical how, rather than the why or what, of conducting reproducible research. Part 1 contains 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. 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.
Seller Inventory # LU-9780520294745
- Title
- The Practice of Reproducible Research
- Author
- Justin Kitzes
- Publisher
- University of California Press, US
- Publication year
- 2017
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 0520294742
- ISBN 13
- 9780520294745
- Item weight
- 635 grams
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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