The Practice of Reproducible Research - Info and Reading Options
Case Studies and Lessons from the Data-Intensive Sciences
By Justin Kitzes

"The Practice of Reproducible Research" was published by University of California Press in Oct 17, 2017 - Oakland, California and it has 364 pages.
“The Practice of Reproducible Research” Metadata:
- Title: ➤ The Practice of Reproducible Research
- Author: Justin Kitzes
- Number of Pages: 364
- Publisher: University of California Press
- Publish Date: Oct 17, 2017
- Publish Location: Oakland, California
“The Practice of Reproducible Research” Subjects and Themes:
- Subjects: ➤ Electronic data processing - Research - Statistics, data processing - Reproducible research - Statistical methods - Statistics - Data processing
Edition Specifications:
- Format: paperback
Edition Identifiers:
- The Open Library ID: OL27408816M - OL20218762W
- Online Computer Library Center (OCLC) ID: 975364966
- Library of Congress Control Number (LCCN): 2017028437
- ISBN-13: 9780520294752 - 9780520967779
- ISBN-10: 0520294750
- All ISBNs: 0520294750 - 9780520294752 - 9780520967779
AI-generated Review of “The Practice of Reproducible Research”:
"The Practice of Reproducible Research" Description:
Open Data:
"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."--Provided by publisher
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