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The beauty of workflows and models
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Carole Goble at RDMFMeeting, Westminster, 2014-06-20. Workflows for research. Reproducible research.
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The beauty of workflows and models
- The beauty of workflows and models Workflows for research. Reproducible research. Professor Carole Goble The University of Manchester, UK The Software Sustainability Institute carole.goble@manchester.ac.uk @caroleannegoble RDMF Meeting, Westminster, 20 June 2014
- Scientific publications have at least two goals: (i) to announce a result and (ii) to convince readers that the result is correct ….. papers in experimental science should describe the results and provide a clear enough protocol to allow successful repetition and extension Jill Mesirov Accessible Reproducible Research Science 22 Jan 2010: 327(5964): 415-416 DOI: 10.1126/science.1179653 Virtual Witnessing* *Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life (1985) Shapin and Schaffer.
- Scientific publications have at least two goals: (i) to announce a result and (ii) to convince readers that the result is correct ….. papers in experimental science should describe the results and provide a clear enough protocol to allow successful repetition and extension Jill Mesirov Accessible Reproducible Research Science 22 Jan 2010: 327(5964): 415-416 DOI: 10.1126/science.1179653 Virtual Witnessing* *Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life (1985) Shapin and Schaffer.
- “An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment, [the complete data] and the complete set of instructions which generated the figures.” David Donoho, “Wavelab and Reproducible Research,” 1995 datasets data collections standard operating procedures software algorithms configurations tools and apps codes workflows scripts code libraries services, system software infrastructure, compilers hardware Morin et al Shining Light into Black Boxes Science 13 April 2012: 336(6078) 159-160 Ince et al The case for open computer programs Nature 482, 2012
- datasets data collections standard operating procedures software algorithms configurations tools and apps codes workflows scripts code libraries services, system software infrastructure, compilers hardware Morin et al Shining Light into Black Boxes Science 13 April 2012: 336(6078) 159-160 Ince et al The case for open computer programs Nature 482, 2012 “Executable Data”
- Biodiversity marine monitoring and health assessment ecological niche modelling Data Intensive Science Collaborative Science Pilumnus hirtellusEnclosed sea problem (Ready et al., 2010) Sarah Bourlat http://www.biovel.eu
- Data discoveryData discovery Data assembly, cleaning, and refinement Data assembly, cleaning, and refinement Ecological Niche Modeling Ecological Niche Modeling Statistical analysisStatistical analysis Analytical cycle Data collectionData collection InsightsInsights Scholarly Communication & Reporting Scholarly Communication & Reporting
- BioSTIF method instruments and laboratory Workflows: capture the steps assembly & interoperability shielding & optimising flexible variant reuse pipelines & exploration repetition & comparison record & set-up provenance collection report & embed multi-code and multi- resource experiments in-house and external workflow mgment systems materials http://www.taverna.org.uk
- Application GeneralistSpecialist Infrastructure Scientific Workflow Management Systems
- Systems Biology Modelling Cycle
- Virtual Physiological Human Morphology Microbiology Metabolic Pathways http://www.vph-share.eu/
- standards,standards,standards
- Data Models Articles External Databases http://www.seek4science.org Metadata http://www.isatools.org Aggregated Content Infrastructure share and interlinking multi-stewarded, mixed, methods, models, data, samples…
- Preservation Planning &Watch continuous preservation management Environment and users Repository access ingest harvest Monitored environment and usersWatch Planning Operations create/ reevaluate plans deploy plan monitored actions Monitored content and events execute action plan policies SCOUT c3po PLATO Taverna workflows RODA Long term preservation of digital data. Maintaining scans of newspapers, books, records of data; Metadata maintenance; large and automated. Preservation Policy: Collection level Control Policy: Low level actions & constraints http://www.scape-project.eu/
- Merge a Preservation Action Plan…. … with an Access Workflow Execution Workflow Preservation Planning & Watch Publish Use Components
- Workflows (and Scripts and Models) are…. …provenance of data …general technique for describing and enacting a process …precise, unambiguous, transparent protocols and records. …often complex, so they need explaining. …often challenging and expensive to develop. …know-how and best practice. …collaborations. …first class citizens of research …support the process of research
- Workflow publishing [Scott Edmunds] Publishing Journals Portals Integrative Frameworks galaxyproject.org/
- Reproducibility = Hard Work Code in sourceforge under GPLv3: http://soapdenovo2.sourceforge.net/>5000 downloads http://homolog.us/wiki/index.php?title=SOAPdenovo2 Data sets Analyses Linked to Linked to DOI DOI Open-Paper Open-Review DOI:10.1186/2047-217X-1-18 >11000 accesses Open-Code 8 reviewers tested data in ftp server & named reports published DOI:10.5524/100044 Open-Pipelines Open-Workflows DOI:10.5524/100038 Open-Data 78GB CC0 data Enabled code to being picked apart by bloggers in wiki [Scott Edmunds]
- http://ropensci.org Repositories Libraries Registries
- Archiving Publishing Component Libraries Preserving Recording Storing Exchanging Versioning SharingPACKS Repositories
- Data Operations in Workflows in the Wild Analysis of 260 publicly available workflows in Taverna, WINGS, Galaxy and Vistrails Garijo et al Common Motifs in Scientific Workflows: An Empirical Analysis, FGCS, 36, July 2014, 338–351
- Research Method Stewardship Management, Publishing, Preservation Workflows & Scripts Services & Codes Standard Operating Procedures Descriptions Standards Portal Different systems Formats Web Services Code Libraries Executables Models & Algorithms Mark-up Languages, Mathematical descriptions Standards BioModels
- Reuse Organised Groups Trust Reciprocity Visibility Roll your own from standard parts Complementarity Design and Instruction
- Curation Non-intrusive, Non-invasive, Not invisible Enclaves Specialist Flirts Blue collar Incremental JIJIT not JIC
- Victoria Stodden, AMP 2011 http://www.stodden.net/AMP2011/, Special Issue Reproducible Research Computing in Science and Engineering July/August 2012, 14(4) Howison and Herbsleb (2013) "Incentives and Integration In Scientific Software Production" CSCW 2013.
- Data Stewardship Making practices Sustainability Management planning Deposition Long term access Credit Journals Licensing Open source / access Best Practices for Scientific Computing http://arxiv.org/abs/1210.0530 1st Workshop on Maintainable Software Practices in e-Science – e-Science 2012 Stodden, Reproducible Research Standard, Intl J Comm Law & Policy, 13 2009 Software ModelsWorkflows Services
- Data Stewardship Best Practices for Scientific Computing http://arxiv.org/abs/1210.0530 1st Workshop on Maintainable Software Practices in e-Science – e-Science 2012 Stodden, Reproducible Research Standard, Intl J Comm Law & Policy, 13 2009 Software ModelsWorkflows Services Making practices Sustainability Management planning Deposition Long term access Credit Journals Licensing Open source / access
- http://sciencecodemanifesto.org/http://matt.might.net/articles/crapl/
- Jennifer Schopf, Treating Data Like Software: A Case for Production Quality Data, JCDL 2012 Software release paradigm Some of your data isn’t data Not a static document paradigm • Release research • Methods in motion. • Versioning • Forks & merges • F1000, PeerJ GitHub….
- Pivot around method / software / data rather than paper Citation semantics: software as was? software as is? The multi-dimensional paper
- methods, reproducibility what does it mean for content managers and the research workflow?
- Replication Gap 1. Ioannidis et al., 2009. Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14 2. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html 3. Bjorn Brembs: Open Access and the looming crisis in science https://theconversation.com/open-access-and-the-looming-crisis-in-science-14950 Out of 18 microarray papers, results from 10 could not be reproduced Out of 18 microarray papers, results from 10 could not be reproduced
- re-compute replicate rerun repeat re-examine repurpose recreate reuse restore reconstruct review regenerate revise recycle regenerate the figure redo “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.”* *Lewis Carroll, Through the Looking-Glass, and What Alice Found There (1871)
- reusereproduce repeat replicate Drummond C Replicability is not Reproducibility: Nor is it Good Science, online Peng RD, Reproducible Research in Computational Science Science 2 Dec 2011: 1226-1227. Methods (techniques, algorithms, spec. of the steps) Materials (datasets, parameters, algorithm seeds) Experiment Instruments (codes, services, scripts, underlying libraries) Laboratory (sw and hw infrastructure, systems software, integrative platforms) Setup
- same experiment same set up same lab same experiment same set up different lab same experiment different set up different experiment some of same validate Drummond C Replicability is not Reproducibility: Nor is it Good Science, online Peng RD, Reproducible Research in Computational Science Science 2 Dec 2011: 1226-1227. reusereproduce repeat replicate
- DesignDesign ExecutionExecution Result AnalysisResult Analysis CollectionCollection Publish / Report Publish / Report Peer Review Peer Review Peer Reuse Peer Reuse ModellingModelling Can I repeat & defend my method? Can I review / reproduce and compare my results / method with your results / method? Can I review / replicate and certify your method? Can I transfer your results into my research and reuse this method? * Adapted from Mesirov, J. Accessible Reproducible Research Science 327(5964), 415-416 (2010) Research Report PredictionPrediction MonitoringMonitoring CleaningCleaning
- Record Everything Automate Everything recomputation.org sciencecodemanifesto.org
- [Adapted Freire, 2013] Authoring Exec. Papers Link docs to experiment Sweave Provenance Tracking, Versioning Replay, Record, Repair Workflows, makefiles ProvStore open accessible available description intelligible machine- readable provenance gather dependencies capture steps track & keep results provenance gather dependencies capture steps track & keep results
- http://nbviewer.ipython.org/github/myGrid/DataHackL eiden/blob/alan/Player_example.ipynb https://www.youtube.com/watch?v=QVQwSOX5S08 ? notebooks Build into the workflows of research….
- RDataTracker and DDG Explorer Build into the workflows of research…. [Barbara S. Lerner and Emery R. Boose]
- Components Dependencies Change • 35 kinds of annotations • 5 Main Workflows • 14 Nested Workflows • 25 Scripts • 11 Configuration files • 10 Software dependencies • 1 Web Service • Dataset: 90 galaxies observed in 3 bands • Multiple platforms • Multiple systems José Enrique Ruiz (IAA-CSIC) Galaxy Luminosity Profiling
- specialist codes libraries, platforms, tools services (cloud) hosted services commodity platforms data collections catalogues software repositories my data my process my codes integrative frameworks gateways
- Document vs Instrument Reproducibility by Inspection Read It Reproducibility by Invocation Run It
- Instrument Entropy all experiments become less reproducible Zhao, Gomez-Perez, Belhajjame, Klyne, Garcia- Cuesta, Garrido, Hettne, Roos, De Roure and Goble. Why workflows break - Understanding and combating decay in Taverna workflows, 8th Intl Conf e-Science 2012 Mitigate Detect, Repair Preserve Partial replication Approx reproduce Verification Benchmarks
- Environmental Ecosystem Joppa et al SCIENCE 340 May 2013; Morin et al Science 336 2012 Black boxes Mixed systems, mixed stewardship Distributed, hosted systems
- Workflow Planning & Watch of Workflows Watch Operations Planning Env & Users Repository plan deploy monitor monitor monitor access ingest, harvest Decay, Service Deprecation, Data source monitoring, Checklists, Minimal Models Workflows, myExperiment Workflows for managing workflows
- portability variability tolerance [Adapted Freire, 2013] preservation packaging provenance gather dependencies capture steps track & keep results provenance gather dependencies capture steps track & keep results versioning host service Open Source/Store Sci as a Service Integrative fws Virtual Machines Recompute, limited installation, Black Box Byte execution, copies Descriptive read, White Box Archived record Read & Run, Co-location No installation Portable Package White Box, Installation Archived record
- portability [Adapted Freire, 2013] preservation packaging provenance gather dependencies capture steps track & keep results provenance gather dependencies capture steps track & keep results host service ReproZip variability tolerance versioning
- Levels of 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]
- indable ccessible nteroperable eusable http://datafairport.org/ Packs
- (Dynamic) Research Objects • Bundle and relate multi-hosted digital resources of a scientific experiment or investigation using standard mechanisms, Currency of exchange • Exchange, Releasing paradigm for publishing http://www.researchobject.org/
- • Bundle and relate multi-hosted digital resources of a scientific experiment or investigation using standard mechanisms, Currency of exchange • Exchange, Releasing paradigm for publishing http://www.researchobject.org/ (Dynamic) Research Objects
- • Bundle and relate multi-hosted digital resources of a scientific experiment or investigation using standard mechanisms, Currency of exchange • Exchange, Releasing paradigm for publishing http://www.researchobject.org/ (Dynamic) Research Objects OAI- ORE W3C OAM H. Van de Sompel et. al. Persistent Identifiers for Scholarly Assets and the Web: The Need for an Unambiguous Mapping 9th International Digital Curation Conference; Trusty URLs H. Van de Sompel et. al. Persistent Identifiers for Scholarly Assets and the Web: The Need for an Unambiguous Mapping 9th International Digital Curation Conference; Trusty URLs
- Machine readable metadata* Machine actionable systems** * Especially Linked Data and RDF ** Especially REST APIs
- Sys Bio Research Object Adobe UCF Research Object Bundle ORE PROVODF • Aggregation • Annotations/provenance • Ad-hoc domain-specific specification OMEX archive Systems Biology: Needed a common archive format for reuse across tools
- The research lifecycle IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Repositories Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training [Phil Bourne]
- productivity reproducibility personal side effect public side effect The Cameron Neylon Equation towards Steps born reproducible
- “may all your problems be technical” ...Jim Gray Social Matters Organisation MetricsCulture Process [Adapted, Daron Green]
- Summary • Workflow & modelling models in Science • Software-style Stewardship • Born reproducible • Collective cost & responsibility • Social factors dominate http://www.force11.org Force2015 12 - 13 January, 2015 Oxford University
- • myGrid – http://www.mygrid.org.uk • Taverna – http://www.taverna.org.uk • myExperiment – http://www.myexperiment.org • BioCatalogue – http://www.biocatalogue.org • Biodiversity Catalogue – http://www.biodiversitycatalogue.org • Seek – http://www.seek4science.org • Rightfield – http://www.rightfield.org.uk • VPH-Share – http://www.vph-share.eu/ • Wf4ever – http://www.wf4ever-project.org • Software Sustainability Institute – http://www.software.ac.uk • BioVeL – http://www.biovel.eu • Force11 – http://www.force11.org • SCAPE – http://www.scape-project.eu/
- Acknowledgements • David De Roure • Tim Clark • Sean Bechhofer • Robert Stevens • Christine Borgman • Victoria Stodden • Marco Roos • Jose Enrique Ruiz del Mazo • Oscar Corcho • Ian Cottam • Steve Pettifer • Magnus Rattray • Chris Evelo • Katy Wolstencroft • Robin Williams • Pinar Alper • C. Titus Brown • Greg Wilson • Kristian Garza • Donal Fellows • Wf4ever, SysMO, BioVel, UTOPIA and myGrid teams • Juliana Freire • Jill Mesirov • Simon Cockell • Paolo Missier • Paul Watson • Gerhard Klimeck • Matthias Obst • Jun Zhao • Pinar Alper • Daniel Garijo • Yolanda Gil • James Taylor • Alex Pico • Sean Eddy • Cameron Neylon • Barend Mons • Kristina Hettne • Stian Soiland-Reyes • Rebecca Lawrence • Alan Williams
Editor's Notes
- how, why and what matters benchmarks for codes plan to preserve repair on demand description persists use frameworks partial replication approximate reproduction verification
- / Minute Taking It examines the debate between Robert Boyle and Thomas Hobbes over Boyle's air-pump experiments in the 1660s. In 2005, Shapin and Schaffer were awarded the Erasmus Prize for this work.
- http://www.mysharedprotocols.com/ Openwetware.org Molmeth.org
- Ballast water in shipping lanes invasive species risk assessments When the tools are integrated on the Lifewatch portal, we will have access to the enviornmental data through the workflow. Global marine layers used in our study came from Bio-Oracle (http://www.biooracle. ugent.be/) with a resolution of 5 arc-minutes (9.2 km) (Tyberghein et al. 2012), and were used to study abiotic factors such as mean sea surface temperature (SST) (°C), mean surface salinity (SSS) (PSU) and mean photosynthetically available radiation (PAR) (Einstein/m2/day). Other marine layers used in our analyses came from AquaMaps (http://www.aquamaps.org/download/main.php) with a resolution of 30 arc-minutes, i.e. 1 km2 (Kaschner et al. 2010). If layers are needed from SMHI, they can be uploaded via the workflow. Not all things are batch VPH-Share opens a VNC connection spawned instance. Taverna Interaction Service Users interact with a workflow (wherever it is running) in a web browser. Interaction Service Workbench Plug-in
- Collecting, processing and management of big data Metagenomics, genotyping, genome sequencing, phylogenetics, gene expression analysis, proteomics, metabolomics, auto sampling Analytics and management of broad data from many different disciplines Coupling analytical metagenomics with meaningful ecological interpretations Continuous development of novel methods and technologies Functional trait-based ecology approach proposed by Barberán et. al 2012.
- Example of an extreme of the software issue Multi-code experiments platform libraries, plugins Infrastructure components, services infrastructure
- Variety: common metadata models rich metadata collection ecosystem Validity: auto record of experiment set-up, citable and shareable descriptions curation, publication, mixed stewardship third part availability model executability citability, QC/QA. trust. Social issues of understanding the culture of risk, reward, sharing and reporting.
- C3PO content profiling tool What kind of platform are my users running on Purpose: show how the system components work together and support the overall conceptual framework we are building on. The example story that we use is a real-world case from the British Library where a plan was created previously with Plato3 This kind of scenario is very common, of course - it could have been one of many other organisations that have similar, in particular digitized, collections. In parallel to the Plato case study we are relying on, we had conducted several others that led to different conclusions; and the same problem structure applies to the case study we just conducted in SCAPE with the SB-DK. Hence, the tools demonstrated here apply equally across many scenarios - the concepts used are completely scenario-agnostic, and decision support applies equally across scenarios, content sets, and organisations.
- Running migration tools
- Designing for reuse is hard No closing the feedback loop Off site collaboration (behind closed doors and invisible) Poor reciprocity – find, grab, piss off Not much reuse (too specific) Components Enclaves and groups are better File and forget Most workflows decay. Group enclaves Personal archives rather than sharing Credit….. Designing for reuse
- Curators Credit? Funded? Blue-Collar. Personal and institutional visibility Scholarly (citation metrics) Federate workloads Unpopular with the big data providers. Hell is other people’s metadata Jean-Paul Satre Invisible vs nonintrusive (discreet but controlled) credit Takes time, fears for description Non-intrusiveness vs control and invisible Off site collaboration Not much reuse (too specific) Components Enclaves and groups are better File and forget Most workflows decay.
- Designing for stewardship
- “As a general rule, researchers do not test or document their programs rigorously, and they rarely release their codes, making it almost impossible to reproduce and verify published results generated by scientific software” An aside
- TOLERANCE The explicit documentation of designed-in and anticipated variation methods matterresults may vary
- Added afterwards. 1. Required as condition of publication, certain exceptions permitted (e.g. preserving confidentiality of human subjects) 2. Required but may not affect editorial/publication decisions 3. Explicitly encouraged/addressed; may be reviewed and/or hosted 4. Implied 5. No mention 59% of papers in the 50 highest-IF journals comply with (often weak) data sharing rules. Alsheikh-Ali et al Public Availability of Published Research Data in High-Impact Journals. PLoS ONE 6(9) 2011
- conceptual replication “show A is true by doing B rather than doing A again” verify but not falsify [Yong, Nature 485, 2012] The letter or the spirit of the experiment indirect and direct reproducibility Reproduce the same affect? Or same result? Concept drift towards bottom. As an old ontologist I wanted an ontology or a framework or some sort of property based classification.
- Reproducible Research Environment Integrated infrastructure for producing and working with reproducible research. make reproducible, born reproducible Reproducible Research Publication Environment Distributing and reviewing; credit; licensing etc
- FAIRport* ReproducibilityFind, Access, Interoperate, Reuse, PortPreservation - Lots of copies keeps stuff safe Stability dimension Add two more dimensions to our classification of themes A virtual machine (VM) is a software implementation of a machine (i.e. a computer) that executes programs like a physical machine. Virtual machines are separated into two major classifications, based on their use and degree of correspondence to any real machine: System Overlap of course Static vs dynamic. GRANULARITY This model for audit and target of your systems overcoming data type silos public integrative data sets transparency matters cloud Recomputation.org Reproducibility by ExecutionRun It Reproducibility by InspectionRead It Availability – coverage Gathered: scattered across resources, across the paper and supplementary materials Availability of dependencies: Know and have all necessary elements Change management: Data? Services? Methods? Prevent, Detect, Repair. Execution and Making Environments: Skills/Infrastructure to run it: Portability and the Execution Platform (which can be people…), Skills/Infrastructure for authoring and reading Description: Explicit: How, Why, What, Where, Who, When, Comprehensive: Just Enough, Comprehensible: Independent understanding Documentation vs Bits (VMs) reproducibility Learn/understand (reproduce and validate, reproduce using different codes) vs Run (reuse, validate, repeat, reproduce under different configs/settings)
- Electronic lab notebooks Issues: non-secure html using http inside secure https iframe in ipython doesn’t work – need to update interaction service to deliver on https. I’ll come back to iPython notebook later Client package (currently under development, will be available via Python Package Index (PyPI) for installation for all major platforms (Linux, Mac, Windows) Allows for calling Taverna Workflows available via Taverna Player List of available workflows can be retrieved from the BioVel Portal (Taverna Player) Users can enter the input values using Ipython Notebook (these values can be then results of the code previously run in the Notebook The outputs from running the workflow (the results) are returned to the Notebook and processed further The full workflow run and the overall process (provenance) can be saved in the Ipython Notebook format For an example (static for now), see http://nbviewer.ipython.org/urls/raw.githubusercontent.com/myGrid/DataHackLeiden/alan/Player_example.ipynb?create=1
- the goldilocks paradox the description needed to make an experiment reproducible is too much for the author and too little for the reader” just enough just in time No just ONE system. Many systems. Bewildering range of standards for formats, terminologies and checklists The Economics of Curation Curate Incrementally Early and When Worthwhile Ramps: Automation & Integrated Tools Copy editing Methods
- the reproducibility ecosystem For peer and author complicated and scattered - super fragmentation – supplementary materials, multi-hosted, multi-stewarded. we must use the right platforms for the right tools The trials and tribulations of review Its Complicated www.biostars.org/ Apache Service based ScienceScience as a Service
- Shapin 1984 Boyle’s instruments
- Check numbers of workflows The how, why and what plan to preserve prepare to repair description persists common frameworks partial replication approximate reproduction verification benchmarks for codes
- “lets copy the box that the internet is in” black boxes closed codes & services, proprietary licences, magic cloud services, manual manipulations, poor provenance/version reporting, unknown peer review, mis-use, platform calculation dependencies Mixed systems, mixed stewardship, mixed provenance, different resources
- Decay monitoring, traffic lights contribute to Watch/Monitoring Customise to User characteristics, e.g. their version of workflow engine myExp and RODL provide both Operations and Repository Next steps: Planning component Explicit roles of archival and curation organisations Scientific users play different roles but it tends to be the same user Considering SCAPE brings up some questions about wf4ever What are the high level policies for workflow preservation? Who’s playing the role of, e.g. Curator or Archival Organisation? So far, use cases tend to have the same individual playing multiple roles rather than different individuals. Who’s our Designated Community? Meta-Question: How do we answer these questions? Opportunity to develop workflow preservation policies.
- FAIRport* ReproducibilityFind, Access, Interoperate, Reuse, PortPreservation - Lots of copies keeps stuff safe Stability dimension Add two more dimensions to our classification of themes A virtual machine (VM) is a software implementation of a machine (i.e. a computer) that executes programs like a physical machine. Virtual machines are separated into two major classifications, based on their use and degree of correspondence to any real machine: System Overlap of course Static vs dynamic. GRANULARITY This model for audit and target of your systems overcoming data type silos public integrative data sets transparency matters cloud Recomputation.org Reproducibility by ExecutionRun It Reproducibility by InspectionRead It Availability – coverage Gathered: scattered across resources, across the paper and supplementary materials Availability of dependencies: Know and have all necessary elements Change management: Data? Services? Methods? Prevent, Detect, Repair. Execution and Making Environments: Skills/Infrastructure to run it: Portability and the Execution Platform (which can be people…), Skills/Infrastructure for authoring and reading Description: Explicit: How, Why, What, Where, Who, When, Comprehensive: Just Enough, Comprehensible: Independent understanding Documentation vs Bits (VMs) reproducibility Learn/understand (reproduce and validate, reproduce using different codes) vs Run (reuse, validate, repeat, reproduce under different configs/settings)
- FAIRport* ReproducibilityFind, Access, Interoperate, Reuse, PortPreservation - Lots of copies keeps stuff safe Stability dimension Add two more dimensions to our classification of themes A virtual machine (VM) is a software implementation of a machine (i.e. a computer) that executes programs like a physical machine. Virtual machines are separated into two major classifications, based on their use and degree of correspondence to any real machine: System Overlap of course Static vs dynamic. GRANULARITY This model for audit and target of your systems overcoming data type silos public integrative data sets transparency matters cloud Recomputation.org Reproducibility by ExecutionRun It Reproducibility by InspectionRead It Availability – coverage Gathered: scattered across resources, across the paper and supplementary materials Availability of dependencies: Know and have all necessary elements Change management: Data? Services? Methods? Prevent, Detect, Repair. Execution and Making Environments: Skills/Infrastructure to run it: Portability and the Execution Platform (which can be people…), Skills/Infrastructure for authoring and reading Description: Explicit: How, Why, What, Where, Who, When, Comprehensive: Just Enough, Comprehensible: Independent understanding Documentation vs Bits (VMs) reproducibility Learn/understand (reproduce and validate, reproduce using different codes) vs Run (reuse, validate, repeat, reproduce under different configs/settings)
- ENCODE threads exchange between tools and researchers bundles and relates digital resources of a scientific experiment or investigation using standard mechanisms
- ENCODE threads exchange between tools and researchers bundles and relates digital resources of a scientific experiment or investigation using standard mechanisms Three principles underlie the approach: Identity Referring to resources (and the aggregation itself) Aggregation Describing the aggregation structureand its constituent parts Annotation Associating information with aggregated resources.
- ROs as Information Packages in OAIS DOIs – the resolution of a DOI URL is a landing page, not RDF metadata for a client. (designed for people) HTTP URIs provide both access and identification PIDs: Persistent Identifiers (e.g.DOIs) tend to resolve to human-readable landing pages With embedded links to further (possibly machine-readable) resources ROs seen as non-information resources with descriptive (RDF) metadata Redirection/negotiation Standard patterns for Linked Data resources Bidirectional mappings between URIs and PIDs Versioning through, e.g. Memento ENCODE threads exchange between tools and researchers bundles and relates digital resources of a scientific experiment or investigation using standard mechanisms Three principles underlie the approach: Identity Referring to resources (and the aggregation itself) Aggregation Describing the aggregation structureand its constituent parts Annotation Associating information with aggregated resources.
- http://www.youtube.com/watch?v=p-W4iLjLTrQ&list=PLC44A300051D052E5 Our collaboration with ISA/GigaScience/nanopublication is finally being written up and will be submitted to ECCB this Friday. We will upload a copy to Arxiv after the deadline. - We will continue our workshop at ISMB, with BioMED Central. And Kaitlin will also join us on the Panel. You can find more details about agenda and panel planning in other emails. Posted on December 11, 2013 by Kaitlin Thaney Part of the Science Lab’s mission is to work with other community members to build technical prototypes that move science on the web forward. In particular, we want to show that many problems can be solved by making existing tools and technology work together, rather than by starting from scratch.The reason behind that is two-fold: (1) most of the stuff needed to change behaviors already exists in some form and (2) the smartest minds are usually outside of your organization. Our newest project extends our existing work around “code as a research object”, exploring how we can better integrate code and scientific software into the scholarly workflow. The project will test building a bridge that will allow users to push code from their GitHub repository to figshare, providing a Digital Object Identifier for the code (a gold standard of sorts in science, allowing persistent reference linking). We will also be working on a “best practice” standard (think a MIAME standard for code), so that each research object has sufficient documentation to make it possible to meaningfully use. The project will be a collaboration of the Science Lab with Arfon Smith (Github; co-founder Zooniverse) and Mark Hahnel and his team at figshare. Why code? Scientific research is becoming increasingly reliant on software. But despite there being an ever-increasing amount of the academic process described in code, research communities do not yet treat these products as a fundamental component or “first-class research object” (see our background post here for more). Up until recent years, the sole “research object” in discussion was the published paper, the main means of packaging together the data, methods and research to communicate findings. The web is changing that, making it easier to unpack the components such as data and code for the community to remix, reuse, and build upon. A number of scientists are pushing the envelope, testing out new ways of bundling their code, data and methods together. But outside of copy and pasting lines of code into a paper or, if we’re lucky, having it included in a supplementary information file alongside a paper, the code is still often separated from the documentation needed for others to meaningfully use it to validate and reproduce experiments. And that’s if it’s shared openly at all. Code can go a long way in helping academia move toward the holy grail that is reproducibility. Unfortunately, academics whose main research output is the code they produce, often cannot get the recognition they deserve for creating it. There is also a problem with versioning: citing a paper written about software (as is common practice), gives no indication of which version, or release in GitHub terms, was used to generate the results. What we’re testing figshare and GitHub are two of the leading repositories for data and code (figshare for data; GitHub for code). Open data repositories like figshare have led the way in recent years in changing our practices in relation to data, championing the idea of data as a first-class research object. figshare and others such as Harvard’s Dataverse and Dryad have helped change how we think of data as part of the research process, providing citable endpoints for the data itself that the community trusts (DOIs), as well as clear licensing and making it easy to download, remix, and reuse information. One of the main objectives here is that the exact code used in particular investigations, can be accessed by anyone and persists in the form it was in when cited. This project will test whether having a means of linking code repositories to those commonly used for data will allow for software and code to be better incorporated into existing credit systems (by having persistent identifiers for code snapshots) and how seamless we can make these workflows for academics using tools they are already familiar with. We’ve seen this tested with data over recent years, with sharing of detailed research data associated with increased citation rates (Piwowar, 2007). This culture shift of publishing more of the product of research is an ongoing process and we’re keen to see software and code elevated to the same status as the academic manuscript. We believe that by having the code closely associated with the data it executes on (or generates) will help reduce the barriers when trying to reproduce and build upon the work of others. This is already being tested in the reverse, with computational scientists nesting their data with the code in GitHub (Carl and Ethan, for example). We want to find out if formally linking the two to help ease that pain will change behavior. We are also looking to foster a culture of reuse with academic code. While we know there are lots of variables in this space, we are actively soliciting feedback from the community to help determine best practices for licensing and workflows. How to get involved (UPDATE: Want to help us test? Instead of sending us an email, how about adding yourself to this issue in our GitHub repository? More about that here.) Mark and Arfon will be joining us for our next Mozilla Science Lab community call on December 12, 2013. Join us to hear more about the project. Have a question you’d like to ask? Add it to the etherpad! We’re also looking for computational researchers and publishers to help us test out the implementation. Shoot us an email if you’d like to participate. Posted in Uncategorized.
- APIs – DOIs on landing pages for people.
- Scientist works with local folder structure. Version management via github. Local tooling produces metadata description Metadata about the aggregation (and its resources) provided by “hidden folder” Zenodo/figshare pull snapshot from github Providing DOIs for the aggregrations Additional release cycles can prompt new DOIs
- Cameron Neylon, BOSC 2013, http://cameronneylon.net/
- SKIPPED
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