Ensuring Reproducibility with Research Objects
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presented at 1st First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany http://repscience2016.research-infrastructures.eu/
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Ensuring Reproducibility with Research Objects
- 1. What is Reproducibility? The R* brouhaha (and how Research Objects can help) Professor Carole Goble The University of Manchester, UK Software Sustainability Institute, UK ELIXIR-UK, FAIRDOMAssociation e.V. carole.goble@manchester.ac.uk First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany
- 2. Acknowledgements • Dagstuhl Seminar 16041 , January 2016 – http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16041 • ATI Symposium Reproducibility, Sustainability and Preservation , April 2016 – https://turing.ac.uk/events/reproducibility-sustainability-and-preservation/ – https://osf.io/bcef5/files/ • CTitus Brown • Juliana Freire • David De Roure • Stian Soiland-Reyes • Barend Mons • Tim Clark • Daniel Garijo • Norman Morrison
- 3. “When I use a word," Humpty Dumpty said in rather a scornful tone, "it means just what I choose it to mean - neither more nor less.” Carroll, Through the Looking Glass re-compute replicate rerun repeat re-examine repurpose recreate reuse restore reconstruct review regenerate revise recycle redo robustness tolerance verificationcompliancevalidation assurance remix
- 5. Computational Science http://tpeterka.github.io/maui-project/ From:The Future of ScientificWorkflows, Report of DOEWorkshop 2015, http://science.energy.gov/~/media/ascr/pdf/programdocuments/docs/workflows_final_report.pd 1. Observational, experimental 2. Theoretical 3. Simulation 4. Data intensive
- 7. Scientific publications goals: (i) announce a result (ii) convince readers its correct. Papers in experimental science should describe the results and provide a clear enough protocol to allow successful repetition and extension. Papers in computational science should describe the results and provide the complete software development environment, data and set of instructions which generated the figures. VirtualWitnessing* *Leviathan and theAir-Pump: Hobbes, Boyle, and the Experimental Life (1985) Shapin and Schaffer. Jill Mesirov David Donoho
- 8. Datasets, Data collections Standard operating procedures Software, algorithms Configurations, Tools and apps, services Codes, code libraries Workflows, scripts System software Infrastructure Compilers, hardware Systems of Systems Heterogeneous hybrid patchwork of tools and service evolving over time
- 9. 10 “Simple” Rules for Reproducible Computational Research: RACE 1. For Every Result, Keep Track of How It Was Produced 2. Avoid Manual Data Manipulation Steps 3. Archive the Exact Versions of All External Programs Used 4. Version Control All Custom Scripts 5. Record All Intermediate Results, When Possible in Standardized Formats 6. For Analyses That Include Randomness, Note Underlying Random Seeds 7. Always Store Raw Data behind Plots 8. Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected 9. Connect Textual Statements to Underlying Results 10. Provide Public Access to Scripts, Runs, and Results Sandve GK, Nekrutenko A,Taylor J, Hovig E (2013)Ten Simple Rules for Reproducible Computational Research. PLoS Comput Biol 9(10): e1003285. doi:10.1371/journal.pcbi.1003285 Record Everything Automate Everything Contain Everything Expose Everything
- 10. Preparation pain independent testing trials and tribulations [Norman Morrison] replication hostility no funding, time, recognition, place to publish resource intensive access to the complete environment
- 11. Lab Analogy: Witnessing “Datascopes” Input Data Software Output Data Config Parameters Methods techniques, algorithms, spec. of the steps, models Materials datasets, parameters, algorithm seeds Instruments codes, services, scripts, underlying libraries, workflows, , ref resources Laboratory sw and hw infrastructure, systems software, integrative platforms computational environment
- 13. Repeat, Replicate, Robust [CTitus Brown] https://2016-oslo-repeatability.readthedocs.org/en/latest/repeatability-discussion.html Why the differences? Reproduce,Trust
- 14. “an experiment is reproducible until another laboratory tries to repeat it” Alexander Kohn Repeatability: “Sameness” Same result 1 Lab 1 experiment Reproducibility: “Similarity” Similar result > 1 Lab > 1 experiment Validate Verify
- 15. Method Reproducibility the provision of enough detail about study procedures and data so the same procedures could, in theory or in actuality, be exactly repeated. Result Reproducibility (aka replicability) obtaining the same results from the conduct of an independent study whose procedures are as closely matched to the original experiment as possible What does research reproducibility mean? Steven N. Goodman, Daniele Fanelli, John P. A. Ioannidis ScienceTranslational Medicine 8 (341), 341ps12. [doi: 10.1126/scitranslmed.aaf5027] http://stm.sciencemag.org/content/scitransmed/8/341/341ps12.full.pdf
- 17. reviewers want additional work statistician wants more runs analysis needs to be repeated post-doc leaves, student arrives new/revised datasets updated/new versions of algorithms/codes sample was contaminated better kit - longer simulations new partners, new projects Personal & Lab Productivity Public Good Reproducibility
- 18. “Datascope” Lab Analogy Methods techniques, algorithms, spec. of the steps, models Materials datasets, parameters, algorithm seeds Instruments codes, services, scripts, underlying libraries, workflows, ref datasets Laboratory sw and hw infrastructure, systems software, integrative platforms computational environment
- 19. “Datascope” Lab Analogy Methods techniques, algorithms, spec. of the steps, models Materials datasets, parameters, algorithm seeds Instruments codes, services, scripts, underlying libraries, workflows, ref datasets Laboratory sw and hw infrastructure, systems software, integrative platforms computational environment Form Function
- 20. “Datascope” Practicalities Methods techniques, algorithms, spec. of the steps, models Materials datasets, parameters, algorithm seeds Instruments codes, services, scripts, underlying libraries, workflows, ref datasets Laboratory sw and hw infrastructure, systems software, integrative platforms computational environment Living Dependencies Science, methods, datasets questions stay, answers change breakage, labs decay, services and techniques come and go, new instruments, updated datasets, services, codes, hardware One offs, streams, stochastics, sensitivities, scale, non-portable data black boxes supercomputer access non-portable software licensing restrictions unreliable resources black boxes complexity
- 21. T1 T2 evolving ref datasets, new simulation codes Environment Archived vs Active Contained vs Distributed Regimented vs Free-for-all Who owns the dependencies? Dependencies -> Manage Black boxes -> Expose Dynamics -> Fixity Reliability
- 22. Replicate harder than Reproduce? Repeating the experiment or the set up? Container Conundrum Results willVary ReplicabilityWindow All experiments become less replicable over time Prepare to repair
- 23. Levels of Computational Reproducibility Coverage: how much of an experiment is reproducible OriginalExperimentSimilarExperimentDifferentExperiment Portability Depth: how much of an experiment is available Binaries + Data Source Code / Workflow + Data Binaries + Data + Dependencies Source Code / Workflow + Data + Dependencies Virtual Machine Binaries + Data + Dependencies Virtual Machine Source Code / Workflow + Data + Dependencies Figures + Data [Freire, 2014] Minimum: data and source code available under terms that permit inspection and execution.
- 24. Measuring Information Gain from Reproducibility Research goal Method/Alg. Platform/Exec Env Data Parameters Input data Actors Information Gain Implementation/Code No change Change Don’t care https://linkingresearch.wordpress.com/2016/02/21/dagstuhl-seminar-report-reproducibility-of-data-oriented-experiments-in-e-scienc/ http://www.dagstuhl.de/16041
- 25. How? Preserve by Reporting, Reproduce by Reading Archived Record Description Zoo standards, common metadata
- 26. How? Preserve by Maintaining, Repairing, Containing Reproduce by Running, Emulating, Reconstructing Active Instrument Byte level Buildability Zoo
- 27. provenance portability, preservation robustness, versioning access description standards common APIs licensing, identifiers standards, common metadata change variation sensitivity discrepancy handling packaging, containers FAIR RACE Reproducibility Dimensions dependencies steps
- 28. Research Object Standards-based metadata framework for logically and physically bundling resources with context, http://researchobject.org Bigger on the inside than the outside external referencing
- 29. Manifest Construction Aggregates link things together Annotations about things & their relationships Container Research Object Standards-based metadata framework for logically and physically bundling resources with context, http://researchobject.org Packaging content & links: Zip files, BagIt, Docker images Catalogues & Commons Platforms: FAIRDOM Manifest Description Dependencies what else is needed Versioning its evolution Checklists what should be there Provenance where it came from Identification locate things regardless where id
- 30. Systems Biology Commons • Link data, models and SOPs • Standards • Span resources • Snapshot + DOIs • Bundle and export • Logical bundles
- 31. Belhajjame et al (2015) Using a suite of ontologies for preserving workflow-centric research objects, JWeb Semantics doi:10.1016/j.websem.2015.01.003 application/vnd.wf4ever.robundle+zip Workflow Research Objects exchange, portability and maintenance *https://2016-oslo-repeatability.readthedocs.org/en/latest/overview-and-agenda.html
- 32. Asthma Research e-Lab Dataset building and releasing Standardised packing of Systems Biology models European Space Agency RO Library Large dataset management for life science workflows LHC ATLAS experiments Notre Dame U Rostock Encyclopedia of DNA Elements PeptideAtlas
- 33. Words matter. Reproducibility is not a end. Its a means to an end. Beware reproducibility zealots. 50 Shades of Reproducibility. form vs function A conundrum: big co-operative data-driven science makes reproducibility desirable but also means dependency and change are to be expected. Lab analogy for computational science
- 34. Bonus Slides