This is the open, online version of the book The Practice of Reproducible Research, to be published in print form by the University of California Press in 2018.
This book contains a collection of 31 case studies of reproducible research workflows, written by academic researchers in the data-intensive sciences. Each case study describes how the author combined specific tools, ideas, and practices in order to complete a real-world research project. Emphasis is placed on the practical aspects of how the author organized his or her research to make it as reproducible as possible.
The Introduction and Part I of the book present general information about working reproducibly and synthesizes common themes from across the case studies. This summary section can be read as a stand alone introduction for beginners wishing to learn more about the general practices of reproducible research. Parts II and III of the book contain the 31 case study chapters themselves.
Please cite The Practice of Reproducible Research as:
Kitzes, J., Turek, D., & Deniz, F. (Eds.). (2018). The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Oakland, CA: University of California Press.
Many of the chapters in this book were written by authors affiliated with one of the three Moore-Sloan Data Science Environments: the Berkeley Institute for Data Science at UC Berkeley, the eScience Institute at the University of Washington, and the Center for Data Science at New York University. The editors and authors are particularly grateful for the financial and intellectual support of the the Gordon and Betty Moore Foundation (Grant GBMF3834 to UC Berkeley) and the Alfred P. Sloan Foundation (Grant 2013-10-27 to UC Berkeley).
The contents of this book are copyright University of California Press. Please feel free to share links to this website and to use these online materials for non-commercial, educational purposes. For other uses, including redistribution or reprinting, please contact Justin Kitzes.