Monday, 7 January 2013: 4:30 PM
Room 12B (Austin Convention Center)
Some of today's greatest challenges to the scientific community are big data, reproducibility/transparency and code sharing. The state-of-the-art Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) environment addresses the first two issues with new visualizations and techniques to address big data and provenance. This talk addresses code re-sharing and re-distribution by introducing the UV-CDAT Re-sharable Analyses and Diagnoses (U-ReAD). U-ReAD will offer scientists a complete set of tools (framework) based on the Python programming language along with a code repository. U-ReAD's goal is to use structured documentation to help build the interface between UV-CDAT and a diagnostic, with few or no changes to the original code. This framework will allow scientists to quickly and seamlessly re-implement their diagnostics so that they will fit perfectly into the UV-CDAT environment. As a result U-ReAD-enhanced diagnostics will be automatically provenance-enabled, making it easy to reproduce any set of results exactly and transparently, a crucial functionality considering today's increased scrutiny toward scientific results. This talk aims to demonstrate how easy it can be to plug any diagnostic into UV-CDAT using U-ReAD. We will show how few changes are necessary to create these plugins and how augmented the diagnostics are in return. U-ReAD's developers also hope to create a central repository of U-ReAD-enhanced tools so that scientists can easily share their tools. This talk will show what is in store along these lines.
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