Last Friday, we held a symposium to launch the online version of our new book The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences (to be published in print by the University of California Press later this year).
This book is the product of several years of effort by a team of authors from the Moore-Sloan Data Science Environments at UC Berkeley, the University of Washington, and New York University. Our main goal was to collect concrete examples of reproducible research "in the field," focusing on how scientists and engineers working in the data-intensive sciences actually organize their work to try to meet the goals of reproducibility.
The book itself is centered around a collection of 31 such examples of contributed case studies in reproducible research practices. Each case study presents the specific approach that the author used to achieve reproducibility in a real-world research project, including a discussion of the overall project workflow, major challenges, and key tools and practices used to increase the reproducibility of the research. Accompanying these case studies are several summary chapters (found in Part I of the book) that provide general lessons on reproducible research and synthesize common "lessons learned" and "pain points" from across the individual case study chapters.
We hope that you enjoy reading the book, and please feel free to get in touch with the editors and authors to share your comments and ideas.
Finally, we're still continuing to collect additional case studies of reproducible research workflows. If you're interested in contributing a case study to our growing online collection, you can head over to our GitHub repository for instructions on writing and submitting a case study.
