Authors: Greg Wilson, D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, Katy Huff, Ian M. Mitchell, Mark Plumbley, Ben Waugh, Ethan P. White, Paul Wilson
Science increasingly rests on a computational foundation, yet scientists are never taught to efficiently write reusable code or write automated tests. I will discuss our personal experiences with adopting and adapting "good enough" practices, taken largely from the open source and agile software development world, to the practice of computational science. Each practice will be motivated by practical considerations of efficiency as well as the ultimate goal of producing reproducible and remixable software for a better tomorrow.
Titus Brown received his BA in Math from Reed College in 1997, and his PhD in Developmental Biology at Caltech in 2006. He has worked in digital evolution, climate measurements, molecular and evolutionary developmental biology, and both regulatory genomics and transcriptomics. His current focus is on using novel computer science data structures and algorithms to explore big sequencing data sets from metagenomics and transcriptomics.
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