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A Course in Reproducible Research

A Course in Reproducible Research. Some content is generic, some is domain specific Generic content must be illustrated by domain specific examples Meta issues

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A Course in Reproducible Research

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  1. A Course in Reproducible Research • Some content is generic, some is domain specific • Generic content must be illustrated by domain specific examples • Meta issues • Where should these topics be taught? Single generic course is unlikely to gain traction (at least in established institutions), so piecemeal inclusion into other computationally oriented courses is more likely to work • How can we share course material that is developed? Creative commons (but be careful about NC restrictions) • Examples: Software Carpentry bootcamps, reproducible science winter school in Geilo Norway, HPC course at U Washington, computational science course at KAUST, others?

  2. Hands-on Topics (1) • Software Carpentry • version control • scripting • databases • build systems • unit testing • Testing • system / regression • V&V / UQ • continuous integration • Provenance • Reproducibility in statistical and probabilistic computations

  3. Hands-on Topics (2) • Research documentation • lab notebook • research compendium • literate programming • Programming (not in SWC) • debuggers • how to write good code • floating point / nondeterminism • documentation • Big challenges • high performance computing • big data • cloud computing • complicated SW stacks / toolchains (solution is VMs)

  4. Lecture Topics • IP / licensing • Citation / attribution • examples of RR (both good & bad; ideally domain specific) • publishing / repositories / archives • generic scientific software requirements

  5. Participants • Juliana Freire • Andrew Davison • David Ketcheson • AronAhmadia • Randall LeVeque • Ian Mitchell • YihuiXie • Andre Brodtkorb

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