Making science more reproducible has the potential to advance scientific research and make researchers' work more effective and productive. For computational and data-intensive research, which is increasingly pervasive across the sciences, this is particularly true, and yet is often seen as difficult to achieve. In this 2-day bootcamp-style hands-on workshop, we will teach a number of tools, resources, and practices that can be used today to make one's computational science more reproducible.
The course and the curriculum were developed by the participants of the Reproducible Science Curriculum Hackathon held at the National Evolutionary Synthesis Center (NESCent) in December 2014. The hackathon and instructor travel are supported by the National Science Foundation (NSF).
Who: The course is aimed at graduate students, postdocs, and other researchers who perform computational analysis or work. The material on automation uses basic R for teaching and illustrating the key concepts. Advanced knowledge of R is not needed, but some familiarity with R will make the workshop more enjoyable.
Requirements: Participants must bring a laptop with a few specific software packages installed (listed below). They are also required to abide by our Code of Conduct, which we have adopted from Software Carpentry.
The course is free but requires registration. We ask that as a courtesy to others you cancel as early as possible if you register and subsequently are prevented from taking your seat.
Contact: Please email email@example.com for more information.
|09:00||Introduction to Reproducible Research|
|10:45||Organizing your project to facilitate Reproducible Research|
|10:45||Automating your workflows|
|13:00||Automating your workflows|
|15:30||Sharing and publishing your research workflow|
We will use this Etherpad for chatting, taking notes, and sharing URLs and bits of code.
A checklist to evaluate and stimulate thoughts about the reproducility of your project.
Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. See the instructions below for your operating system. Windows and Mac Users (with OSX 10.9+) will install the GitHub GUI, while Linux and Mac users with older operating systems will install the command line utility.
If you don't already have a GitHub account, please create one.
Please download the GitHub GUI here.
For OS X 10.9 and higher, install the GitHub GUI for Mac by downloading and running the installer from here. For older versions of OS X (10.5-10.8) use the most recent available installer labelled "snow-leopard" available here.
If Git is not already available on your machine you can try to
install it via your distro's package manager. For Debian/Ubuntu run
sudo apt-get install git and for Fedora run
sudo yum install git.
R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio. After installing both R and RStudio, start RStudio and install some packages that we will need for the workshop (see bottom of the page).
Start RStudio, and type (or copy and paste) at the console:
install.packages(c("knitr", "rmarkdown", "ggplot2", "dplyr"))