New Book: The Practice of Reproducible Research

Interested in reproducible research practices? We have exciting news from Justin Kitzes of the Berkeley Institute for Data Science:


"I and several colleagues have just released the open, 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) -

http://www.practicereproducibleresearch.org

"The book is based around 31 case studies of research workflows, contributed by academic scientists and engineers from a variety of disciplines, in which each author describes the key practices, tools, and methods that they used to try to make their research as reproducible as possible. . . If reading 31 case studies sound like a bit much, we've also written a set of summary chapters (Part I of the book) that provides a basic overview of reproducible research and synthesizes lessons learned from across the contributed case studies."


Additionally, if you are looking to connect with other researchers interested in reproducible research practices, be sure to check out the eScience Insitute's Reproducibility and Open Science Working Group.

UW All-Campus Reproducibility Seminar: 2/10 @ 1:30pm

Ben Marwick, Assistant Professor of Archaeology, joins the All-Campus Reproducibility Seminar Series on February 10 at 1:30pm in the WRF Data Science Studio Meeting Room, 6th floor Physics/Astronomy Tower. He will give a talk titled: 

"Doing Reproducible Research with Docker"

Abstract:

A key obstacle to reproducible research that I frequently encounter when working with students and collaborators is keeping the toolkit simple, with managing dependencies being an especially time-consuming challenge. Virtual machines are one solution to these problems, but remain less than ideal because of relatively long start-up and shut-down times, their large size and performance demands, limited portability, and the need for the user to be familiar with a different desktop environment, amongst other concerns. In this talk I introduce Docker, a free and open source Linux container tool recently popular amongst commercial DevOps workers that provides lightweight virtual environments on Windows/OSX/Linux systems and has several advantages over regular virtual machines. I will describe the key elements of doing reproducible research with Docker and demonstrate dockerfiles, containers, images and registries (bring your laptop and follow along! If your using Windows/OSX then be sure to install http://boot2docker.io/ in advance). I will show how these help with dependencies and keeping things simple, especially when working with R or Python.

Please join us!