Can we store code and expect to ever get it to run again? Perhaps for a year or two but what about 10? 20? How we can begin to reason about the problem domain? Taking a page from climate science, this talk explores models to better frame the problem which could provide insight into how to design systems that can outlast us.

Link: Video and Transcript

This talk provides an overview of some key differences that scientific data sets have from more common data sets, such as business analytics. These differences impact correctness of results and add complexity to handling, processing, visualization and even understandability of these kinds of data. Using climate data as a touchstone, we will explore these differences with AWS/Clojurescript examples to illustrate what makes visualizing large scientific data sets challenging while demonstrating promising alternative approaches.

This talk provides an overview of some key differences that scientific data sets have from more common data sets, such as business analytics. These differences impact correctness of results and add complexity to handling, processing, visualization and even understandability of these kinds of data. Using climate data as a touchstone, we will explore these differences with AWS/Clojurescript examples to illustrate what makes visualizing large scientific data sets challenging while demonstrating promising alternative approaches.