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In-silico analysis now makes up a large part of the scientific process but is often surprisingly haphazard. If you've worked in the space, you'll be familiar with the process of running repeated iterations on experiments, duplicating processing and generating innumerable variants on outputs, only to have to backtrack and discover how the final results were reached.
Experiments run on one machine may not repeat on another, or even when run another time on the same architecture. During this session, you will learn about this and other problems encountered building scientific pipelines, and how Haskell is well positioned to tackle them. Nicholas will introduce funflow, a Haskell workflow tool developed by Tweag, and how it's being used in production to develop genome analysis pipelines.
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Reproducible Scientific Workflows in Haskell
Nicholas is currently a software engineer at Tweag I/O, working on a range of projects from biotech to blockchains. In past lives, he led the development of a cluster management system for Seagate and worked in bioinformatics at the Sanger Institute.