The Evolution of e-Research: Machines, Methods and Music
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The document summarizes the evolution of e-research over three generations from 1981 to the present. The first generation saw early adopters using tools within their disciplines with some reuse. The second generation was characterized by increased reuse of tools, data and methods across areas. The third generation is defined by radical sharing of resources globally across any discipline through social networks and reusable research objects. The document also discusses several specific projects and tools that exemplify each generation of e-research including myExperiment, Galaxy, and SALAMI.
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The Evolution of e-Research: Machines, Methods and Music
- 1. The Evolution of e-Research Machines, Methods and Music David De Roure
- 2. MathsPhysics Medical electronics PhD in distributed declarative programming language design Hypermedia Large scale Distributed Systems Semantic Sensor Networks Web Science Devices Amorphous Computing Digital Social Research Equator e-Science MusicElectronics Programming Transputers Temporal Media Computational Musicology Advanced Knowledge Technologies Semantic Web Process Networks myExperiment Web 2 Statistics Grid Linked Data 1981 2010 Environmental sensing Networks VREs MITAJGH PH WH PEOPLEOPLE Agents Semantic Grid e-Laboratories Workflows QBH
- 3. Overview Generation 1: Early adopters Generation 2: Embedding Generation 3: Radical sharing SALAMI A case study in 3rd generation e-Research
- 4. e-Science • e-Science was defined by John Taylor (Director General of the UK Research Councils) as global collaboration in key areas of science and the next generation of infrastructure that will enable it • e-Science was the name of the destination • It became the name of the journey • When we arrive, the destination is just called science
- 5. “e-research extends e-Science and cyberinfrastructure to other disciplines, including the humanities and social sciences.” e-Research http://mitpress.mit.edu/catalog/item/default.asp?tid=12185&ttype=2
- 7. ...the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites Tony Hey and Anne Trefethen Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203
- 8. 26/2/2007 | myExperiment | Slide 8 Jeremy Frey
- 9. • Workflows are the new rock and roll • Machinery for coordinating the execution of (scientific) services and linking together (scientific) resources • The era of Service Oriented Applications • Repetitive and mundane boring stuff made easier Carole Goble E. Science laboris
- 11. co-shaping co-design co-creation co-constitution co-evolution co-construction co- co-realisation
- 13. Box of Chemists My Chemistry Experiment CombeChem
- 14. CombeChem
- 15. empower to equip or supply with an ability; enable service the performance of duties or the duties performed as or by a waiter or servant
- 16. Early adoptors of tools. Characterised by researchers using tools within their particular problem area, with some re-use of tools, data and methods within the discipline. Traditional publishing is supplemented by publication of some digital artefacts like workflows and links to data. Science is accelerated and practice beginning to shift to emphasise in silico work. 1st Generation Summary Thanks to Iain Buchan and the chipmunks
- 17. 2005 – 2010 Generation 2
- 18. • Paul writes workflows for identifying biological pathways implicated in resistance to Trypanosomiasis in cattle • Paul meets Jo. Jo is investigating Whipworm in mouse. • Jo reuses one of Paul’s workflow without change. • Jo identifies the biological pathways involved in sex dependence in the mouse model, believed to be involved in the ability of mice to expel the parasite. • Previously a manual two year study by Jo had failed to do this. Reuse, Recycling, Repurposing Carole Goble
- 19. Carole Goble “e-Science is me-Science: What do Scientists want?”, EGEE 2006 “There are these great collaboration tools that 12-year-olds are using. It’s all back to front.” Robert Stevens
- 20. “A biologist would rather share their toothbrush than their gene name” Mike Ashburner and others Professor in Dept of Genetics, University of Cambridge, UK
- 21. Data mining: my data’s mine and your data’s mine
- 23. mySpace for scientists!FacebookNot too open! too passé!
- 25. “Facebook for Scientists” ...but different to Facebook! A repository of research methods A community social network of people and things A Social Virtual Research Environment A probe into researcher behaviour Open source (BSD) Ruby on Rails app REST and SPARQL interfaces, supports Linked Data Inspiration for: BioCatalogue, MethodBox and SysMO-SEEK myExperiment currently has 4400 members, 236 groups, 1336 workflows, 351 files and 141 packs
- 27. Visits to www.myexperiment.org (Oct 2010) Global collaboration in key areas of science and the next generation of infrastructure that will enable it http://wiki.myexperiment.org
- 28. data method
- 29. Methods should be first class citizens Celebrate the flux! Let the data flow through the pipelines. Nail down the methods not the data! Towards “Linked Open Methods” Though this be madness, yet there is method in it * Polonius in Hamlet ** Sean Bechhofer in Manchester *** Not the e-Science Envoy * *** ** Data bonanza => Methods bonanza!
- 30. It’s not just the data And what other people do with it ...that you never thought of It’s what you do with it that counts
- 31. Results Logs Results Metadata PaperSlides Feeds into produces Included in produces Published in produces Included in Included in Included in Published in Workflow 16 Workflow 13 Common pathways QTL Paul’s PackPaul’s Research Object
- 32. Research Objects enable data-intensive research to be: 1. Replayable – go back and see what happened 2. Repeatable – run the experiment again 3. Reproducible – independent expt to reproduce 4. Reusable – use as part of new experiments 5. Repurposeable – reuse the pieces in new expt 6. Reliable – robust under automation 7. Referenceable – citable and traceable The Six Rs of Research Object Behaviours http://blog.openwetware.org/deroure/?p=56
- 33. Semantically enhanced publication versus Shared digital Research Objects Challenging the mindset of paper-sized chunks
- 36. Projects delivering now. Some institutional embedding. Key characteristic is re-use – of the increasing pool of tools, data and methods across areas/disciplines. Contain some freestanding, recombinant, reproducible research objects. New scientific practices are established and opportunities arise for completely new scientific investigations. Some expert curation. 2nd Generation Summary
- 37. 2010 – 2015 Generation 3
- 38. 4th Paradigm The Fourth Paradigm: Data-Intensive Scientific Discovery Presenting the first broad look at the rapidly emerging field of data- intensive science http://research.microsoft.com/en-us/collaboration/fourthparadigm/
- 41. BioEssays, 26(1):99–105, January 2004 Doug Kell
- 42. Francois Belleau
- 43. “…to discover proteins that interact with transmembrane proteins, particularly those that can be related to neuro- degenerative diseases in which amyloids play a significant role” 1) Taverna provenance exposed as RDF 2) myExperiment RDF document for a protein discovery workflow 3) Mocked-up BioCatalogue document using myExperiment RDF data as example 4) Provisional RDF documents obtained from the ConceptWiki (conceptwiki.org) development server 5) An RDF document for an example protein, obtained from the RDF interface of the UniProt web site A Bioinformatics Experiment Scott Marshall Marco Roos
- 45. MethodBox http://www.methodbox.org/ Enable cross disciplinary research into Major Public Health problems Ease handling data and sharing results and insights
- 48. The solutions we'll be delivering in 5 years Characterised by global reuse of tools, data and methods across any discipline, and surfacing the right levels of complexity for the researcher. Routine use. Key characteristic is radical sharing. Research is significantly data driven – plundering the backlog of data, results and methods. Publishing by the social network Increasing automation and decision-support for the researcher – the VRE becomes assistive. Curation is autonomic and social. 3rd Generation Summary
- 50. Easy and low risk to start Progress to advanced skills For researchers No obligation Go as far as you want Find a service & relax Intellectual ramps Malcolm Atkinson
- 51. NRAO/AUI/NSF telescopes for the naked mindDatascopes Malcolm Atkinson From Signal to Understanding
- 52. Jeannette M. Wing COMMUNICATIONS OF THE ACM March 2006/Vol. 49, No. 3 Pages 33-35
- 53. 2010 – 2011 and beyond Music and Linked Data
- 56. It’s about enabling the join Ben Fields, 6th October 2010
- 57. SALAMI: Structural Analysis of Large Amounts of Music Information David De Roure J. Stephen Downie Ichiro Fujinaga
- 59. The SALAMI collaboration • DDeR (e-Research South), J. Stephen Downie (Illinois) and Ichiro Fujinaga (McGill) • NCSA donating 250,000 supercomputer hours • 350,000 pieces of music (23,000 hours) – Internet Archive, DRAM, IMIRSEL, McGill • Feature analysis and structural analysis • Music Ontology by Yves Raimond (BBC) • Musicologists from McGill and Southampton • Sharing of analyses http://salami.music.mcgill.ca
- 60. Digital Music Collections Crowdsourced ground truth Community Software Linked Data Repositories Supercomputer 23,000 hours of recorded music 250,000 hours NCSA Supercomputer time Music Information Retrieval Community
- 62. MIREX Overview • Began in 2005 • Tasks defined by community debate • Data sets collected and/or donated • Participants submit code to IMIRSEL • Code rarely works first try • Huge labour consumption getting programs to work • Meet at ISMIR to discuss results Stephen Downie http://www.music-ir.org/mirex
- 63. MIREX TASKS Audio Artist Identification Audio Onset Detection Audio Beat Tracking Audio Tag Classification Audio Chord Detection Audio Tempo Extraction Audio Classical Composer ID Multiple F0 Estimation Audio Cover Song Identification Multiple F0 Note Detection Audio Drum Detection Query-by-Singing/Humming Audio Genre Classification Query-by-Tapping Audio Key Finding Score Following Audio Melody Extraction Symbolic Genre Classification Audio Mood Classification Symbolic Key Finding Audio Music Similarity Symbolic Melodic Similarity
- 65. “Signal” Digital Audio “Ground Truth” Community It’s web-like! Q. If and when should community-generated content be assimilated into managed repositories? Structural Analysis
- 66. How country is my country? Kevin Page and Ben Fieldshttp://www.nema.ecs.soton.ac.uk/countrycountry/
- 67. Stephen Downie Music and computational thinking
- 68. “Again, it [the Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine...”
- 69. “Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent.” Ada, The Enchantress of Numbers: Poetical Science by Betty Alexandra Toole http://www.well.com/user/adatoole/ Betty Alexandra Toole
- 70. I can write a workflow that creates workflows based on those of others, and automatically modify it – think genetic mutation and crossovers. Who owns it? I can register a query over an increasing number and diversity of “linked data” sources to ask new research questions. http://eresearch-ethics.org/ The computer can learn from the activities of 1,000,000 scientists – and be indistinguishable from them? What about the ethics of Citizen Social Science? Of citizens designing experiments?
- 71. Co-* Methods Access ramps Research Objects Computational thinking Ethics of e-Research at scale
- 72. david.deroure@oerc.ox.ac.uk Thanks to: Jeremy Frey & CombeChem; Carole Goble, myGrid and myExperiment; Iain Buchan & Obesity e-Lab; Sean Bechhofer; Doug Kell; Marco Roos; Lucy Yardley; Arfon Smith; Malcolm Atkinson; Stephen Downie, Kevin Page, Ben Fields, Ashley Burgoyne and NEMA/SALAMI; Betty Toole. http://www.myexperiment.org/packs/153
Editor's Notes
- Today I’m going to talk about the trajectory of e-Science – from its conception through examples of 3 generations, and I’ll reflect on how we are moving from generation 2 to generation 3. Different disciplines and especially communities may be in different stages of evolution.
- First something about words. This definition of e-Science is important – it reminds us that it isn’t just about technology but about people working together and being empowered by technology – and the emphasis on “science” reminds us that ultimately success is measured by new scientific outcome.At the turn of the decade this was a vision of the future. A programme was created called e-Science. The projects doing the innovation were labelled as “e-Science”. By the time we arrive, it’s just “science”. So “e-Science” has become the name of the journey rather than the destination. Note that the innovation that takes us to the destination isn’t solely in the custody of e-Science projects – there’s a lot of relevant work going on that doesn’t carry that label.Note also that when we say “e-Science” we actually mean “e-Research”! We sometimes forget to say that.
- First something about words. This definition of e-Science is important – it reminds us that it isn’t just about technology but about people working together and being empowered by technology – and the emphasis on “science” reminds us that ultimately success is measured by new scientific outcome.At the turn of the decade this was a vision of the future. A programme was created called e-Science. The projects doing the innovation were labelled as “e-Science”. By the time we arrive, it’s just “science”. So “e-Science” has become the name of the journey rather than the destination. Note that the innovation that takes us to the destination isn’t solely in the custody of e-Science projects – there’s a lot of relevant work going on that doesn’t carry that label.Note also that when we say “e-Science” we actually mean “e-Research”! We sometimes forget to say that.
- Today I’m going to talk about the trajectory of e-Science – from its conception through examples of 3 generations, and I’ll reflect on how we are moving from generation 2 to generation 3. Different disciplines and especially communities may be in different stages of evolution.
- CERN teams up with Leaders in Information Technology to build giant Data GridData accumulation rate: 10 Petabytes per year (equivalent to about 20 million CD-ROMs).http://public.web.cern.ch/press/pressreleases/Releases2001/PR11.01ECERNopenlab.html
- Scientific workflow systems are a key automation technique for systematically handling the data deluge and giving us the “workflow” as a new sharable artefact of digital science – to record, repeat, reproduce and repurpose an experiment.This is an iconic slide by Carole Goble which is much repeated, reproduced and repurposed!
- Today I’m going to talk about the trajectory of e-Science – from its conception through examples of 3 generations, and I’ll reflect on how we are moving from generation 2 to generation 3. Different disciplines and especially communities may be in different stages of evolution.
- What we didn’t see much in phase 1 was sharing and reuse, but this is essential to harnessing of the new technology.The story on this slide involves sharing in a corridor and we will go on to see how we do it digitally! But it’s an important motivation. It led to new science.
- myExperiment in one slide! It’s a “boutique” Web site with the largest public collection of scientific workflows. For lots more information see the myExperiment wiki http://wiki.myexperiment.org/BioCatalogue is a registry of Web Service in the life sciences and is directly based on the myExperiment experience. Sysmo and Methodbox grew from the myExperiment codebase – methodbox is an e-Social Science e-Laboratory for sharing and analysing data, and sysmo is customised to the systems biology domain. Seehttp://www.biocatalogue.org/http://www.methodbox.org/http://www.sysmo-db.org/
- This is reflected in a third distinctive – the pack. This is Paul Fishers pack from the Tryps example.Some packs contain example input and output data so workflows can be checked for “decay” (they don’t actually rot, but the world changes round them).While others are looking at semantically enhanced publication, we are asking “what is the shared artefact of future research?” We come at the same problem from the other side. We have it surrounded! Our approach relieves us of the paper mindest – so, for example, a Research Object could contain information for many audiences and purposes, with a commonly interpreted core (social scientists will recognise the idea of a “boundary object”).
- Today I’m going to talk about the trajectory of e-Science – from its conception through examples of 3 generations, and I’ll reflect on how we are moving from generation 2 to generation 3. Different disciplines and especially communities may be in different stages of evolution.
- First something about words. This definition of e-Science is important – it reminds us that it isn’t just about technology but about people working together and being empowered by technology – and the emphasis on “science” reminds us that ultimately success is measured by new scientific outcome.At the turn of the decade this was a vision of the future. A programme was created called e-Science. The projects doing the innovation were labelled as “e-Science”. By the time we arrive, it’s just “science”. So “e-Science” has become the name of the journey rather than the destination. Note that the innovation that takes us to the destination isn’t solely in the custody of e-Science projects – there’s a lot of relevant work going on that doesn’t carry that label.Note also that when we say “e-Science” we actually mean “e-Research”! We sometimes forget to say that.
- Now we look at myExperiment as a probe into the future behaviour of researchers. For example, these workflows by Francois Belleau show what could be described as another level of working – building on the new tooling.
- Here we see bioinformaticians assembling the resources they need to answer a research question – and also demonstrating what the methods section of the future paper needs to look like.They are using Linked Data. We see the power – ease of assembly. This could be where the new computer science challenges lie in e-Research.
- From The Galileo Project web site: http://galileo.rice.edu/sci/instruments/telescope.html- The earliest known illustration of a telescope. Giovanpattista della Porta included this sketch in a letter written in August 1609 - porta-sketchJohannes Hevelius (Poland, 1611-1687) observing with one of his telescopes (Source: Selenographia, 1647)Hubble_earth_horz and hubble - from http://hubble.nasa.gov/. Very Large Array from http://images.nrao.edu/Telescopes. Copyright requirement - include "NRAO/AUI/NSF" on slide.
- Today I’m going to talk about the trajectory of e-Science – from its conception through examples of 3 generations, and I’ll reflect on how we are moving from generation 2 to generation 3. Different disciplines and especially communities may be in different stages of evolution.
- That example comes from a Digging into Data project with the best project acronym ever. The projects is conducting a massive structural analysis of music in the internet archibe, to support musicologists. It illustrates many of the things we are now seeing in e-Research – crowdsourcing, annotation, community software development, high performance computation, data publication. This project involves UIUC, McGill and Oxford – and the supercomputer time is donated by NCSA.
- That example comes from a Digging into Data project with the best project acronym ever. The projects is conducting a massive structural analysis of music in the internet archibe, to support musicologists. It illustrates many of the things we are now seeing in e-Research – crowdsourcing, annotation, community software development, high performance computation, data publication. This project involves UIUC, McGill and Oxford – and the supercomputer time is donated by NCSA.