Welcome

Reproducibility is central to the conduct of science.  In order to verify, refute, or build upon someone else's work, it must first be possible to reproduce what they have done.  In years past, this was accomplished by the maintenance of a written lab notebook, in which all the steps of an experiment were painstakingly entered, so that others could follow along later.

(DaVinci's Lab Notebook)

Today, the computer has replaced the scientific notebook for measuring, documenting, and (in some cases) experimentation itself.  In principle, computers should make reproducibility simpler, because a program should execute identically every time it runs.  The reality is much more complicated, because computing environments are dynamic, chaotic, and poorly described.

As a result, a scientific code written to run correctly on a computer owned by person A has a surprisingly low chance of running correctly on a computer owned by person B.  Or even on person A's computer one year later!

In this blog, we will explore what it means to achieve reproducibility in the context of scientific computing.  Your hosts are a panel of experts with experience in computer science and scientific computing:

• Ashish Gehani - SRI International
• Daniel Katz - Computational Institute, University of Chicago
• Tanu Malik - Computation Institute, University of Chicago
• Eric Rozier - University of Cincinnati
• Kristin Rozier - University of Cincinnati
• Douglas Thain  - University of Notre Dame
• Gordon Watts - University of Washington

This will be a sort of virtual panel discussion where we discuss what causes these problems, what can be done to improve the situation, and share pointers to ideas, technologies, and meetings.

Ok, let's get started!