Reproducible Research and the Cloud
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Research results in peer-reviewed publications are reproducible, right? If only it was so clear cut. With high profile paper retractions and pushes for better data sharing by funders, publishers and the community, the spotlight is now focussing on the whole way research is conducted around the world. This talk from the Software Sustainability Institute's Collaborations Workshop 2014 describes how cloud computing, with Microsoft Azure, is helping researchers realize the goals of scientific reproducibility. Find out more at www.azure4research.com
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Reproducible Research and the Cloud
- 1. Reproducible Research and the Cloud Dr Kenji Takeda (Kenji.Takeda@Microsoft.com) Microsoft Research @azure4research
- 3. Scientific Discovery Credit: ROYAL INSTITUTION OF GREAT BRITAIN / SCIENCE PHOTO LIBRARY 𝜌 𝐷𝑣 𝐷𝑡 = −𝛻𝑝 + 𝛻 ∙ 𝜯 + 𝒇
- 4. The Research Lifecycle Data Acquisition & modelling Collaboration and visualisation Analysis & data mining Dissemination & sharing Archiving and preserving fourthparadigm.org
- 5. Believe it or not: how much can we rely on published data on potential drug targets? “at least 50% of published studies, even those in top-tier academic journals, can’t be repeated with the same conclusions by an industrial lab” Osherovich, L. Hedging against academic risk. SciBX 14 Apr 2011 (doi:10.1038/scibx.2011.416).
- 13. • Computational experiments should be recomputable for all time • Recomputation of recomputable experiments should be very easy • It should be easier to make experiments recomputable than not to • Tools and repositories can help recomputation become standard • The only way to ensure recomputability is to provide virtual machines • Runtime performance is a secondary issue Ian Gent , Alexander Konovalov and Lars Kotthoff Steven Crouch, Devasena Inupakutika
- 15. Zanadu.IO Patrick Henaff and Claude Martini
- 16. Zanadu.IO
- 17. khmer-protocols: • Effort to provide standard “cheap” assembly protocols for cloud machines. • Entirely copy/paste; ~2-6 days from raw reads to assembly, annotations, and differential expression analysis. Est ~$150 per data set • Open, versioned, forkable, citable. Open Science C. Titus Brown, @ctitusbrown http://ged.cse.msu.edu/ http://ivory.idyll.org/
- 18. Explicitly a “protocol” – explicit steps, copy-paste, customizable, versioned; not black box. No requirement for computational expertise or significant computational hardware. ~1-5 days to teach a bench biologist to use. $100-150 of rental compute (“cloud computing”)… …for $1000 data set. Now adding in quality control and internal validation steps. Some thoughts… Reproducible computing environment (Azure) Publicly available data (MMETSP) Open and versioned protocol Provenance tracking and registration (Synapse?)
- 19. Distribution Modeller <compute + data> Middle ground between: Exploratory science Procedural science Black box that can be cracked open and modified
- 21. • Reproducing my own results • Replicating other people’s results • Reproducing other people’s results Repeatability, Replicability, Reproducibility, Reuse “reviewers have no time and no resources to reproduce data and to dig deeply into the presented work. “ Life Sci VC: Academic bias & biotech failures: http:// lifescivc.com/2011/03/academic-bias- biotech-failures/#0_ undefined,0_ Photo:leechantmcarthur,CC-BY
- 22. Windows Azure for Research • Azure Research Awards • Windows Azure for Research Training Courses – Manchester, 3-4 April’14 • Webinars • Technical resources & curriculum • Research community engagements www.azure4research.com
- 23. THANK YOU Kenji.Takeda@Microsoft.com www.azure4research.com Windows Azure for Research Group @azure4research