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Social Machines of Science and Scholarship
 

Social Machines of Science and Scholarship

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Keynote talk at Joint Conference on Digital Libraries (JCDL) 2013, Indianapolis, 25 July 2013

Keynote talk at Joint Conference on Digital Libraries (JCDL) 2013, Indianapolis, 25 July 2013

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    Social Machines of Science and Scholarship Social Machines of Science and Scholarship Presentation Transcript

    • David De Roure Social Machines of Science and Scholarship
    • 1. The End of the Article 2. How digital research is done today 3. Social Objects 4. Social Machines
    • http://www.scilogs.com/eresearch/pages-of-history/
    • A revolutionary idea… Open Science!
    • http://www.scilogs.com/eresearch/pages-of-history/DavidDeRoure
    • 1. It was no longer possible to include the evidence in the paper – container failure! “A PDF exploded today when a scientist tried to paste in the twitter firehose…”
    • 2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
    • 3. Writing for increasingly specialist audiences restricted essential multidisciplinary re-use Grand Challenge Areas: • Energy • Living with Environmental Change • Global Uncertainties • Lifelong Health and Wellbeing • Digital Economy • Nanoscience • Food Security • Connected Communities • Resilient Economy Today’s research challenges do not respect traditional disciplinary boundaries
    • 4. Research records needed to be readable by computer to support automation and curation A computationally-enabled sense-making network of expertise, data, models and narratives.
    • 5. Single authorship gave way to casts of thousands Presented by David De Roure Technical Adviser Kevin Page Software designer Don Cruickshank Musical Director Ichiro Fujinaga Philosophy Consultant and Catering J. Stephen Downie
    • 6. Quality control models scaled poorly with the increasing volume
    • 7. Alternative reporting necessary for compliance with regulations
    • 8. Research funders frustrated by inefficiencies in scholarly communication An investment is only worthwhile if • Outputs are discoverable • Outputs are reusable • Outputs accrue value
    • 1. The End of the Article 2. How digital research is done today 3. Social Objects 4. Social Machines
    • ChristineBorgman
    • More people Moremachines This is a Fourth Quadrant Talk Big Data Big Compute Conventional Computation The Future! Social Networking e-infrastructure online R&D The Fourth Quadrant
    • F i r s t BioEssays,,26(1):99–105,January2004 http://research.microsoft.com/en-us/collaboration/fourthparadigm/
    • INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.  The Problem signal understanding
    • http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score
    • Digital Music Collections Student-sourced ground truth Community Software Linked Data Repositories Supercomputer 23,000 hours of recorded music Music Information Retrieval Community SALAMI
    • Ashley Burgoyne
    • salami.music.mcgill.ca Jordan B. L. Smith, J. Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen Downie. 2011. Design and creation of a large-scale database of structural annotations. In Proceedings of the International Society for Music Information Retrieval Conference, Miami, FL, 555–60
    • class structure Ontology models properties from musicological domain • Independent of Music Information Retrieval research and signal processing foundations • Maintains an accurate and complete description of relationships that link them Segment Ontology Ben Fields, Kevin Page, David De Roure and Tim Crawford (2011) "The Segment Ontology: Bridging Music-Generic and Domain- Specific" in 3rd International Workshop on Advances in Music Information Research (AdMIRe 2011) held in conjunction with IEEE International Conference on Multimedia and Expo (ICME), Barcelona, July 2011
    • 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 www.music-ir.org/mirex Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010). The Music Information Retrieval Evaluation eXchange: Some Observations and Insights. Advances in Music Information Retrieval Vol. 274, pp. 93-115 Music Information Retrieval Evaluation eXchange
    • seasr.org/meandreMeandre
    • Stephen Downie
    • Web as lens Web as artefact Web Observatories http://www.w3.org/community/webobservatory/
    • Scientists Talk Forum Image Classification data reduction Citizen Scientists
    • Information Circuits Community Software Execution Digital Music Community annotation Linked Data Repositories Workflows Generate Paper Conference
    • Notifications and automatic re-runs Machines are users too Autonomic Curation Self-repair New research?
    • 1. The End of the Article 2. How digital research is done today 3. Social Objects 4. Social Machines
    • The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense- making network of expertise, data, models and narratives. Big data elephant versus sense-making network? Iain Buchan
    • data method
    • Knowledge InfrastructureKnowledge Objects Descriptive layer Observatories Annotation
    • http://www.myexperiment.org/
    • Reusable. The key tenet of Research Objects is to support the sharing and reuse of data, methods and processes. Repurposeable. Reuse may also involve the reuse of constituent parts of the Research Object. Repeatable. There should be sufficient information in a Research Object to be able to repeat the study, perhaps years later. Reproducible. A third party can start with (some of) the same inputs and methods and see if a prior result can be confirmed. Replayable. Studies might involve single investigations that happen in milliseconds or protracted processes that take years. Referenceable. If research objects are to augment or replace traditional publication methods, then they must be referenceable or citeable. Revealable. Third parties must be able to audit the steps performed in the research in order to be convinced of the validity of results. Respectful. Explicit representations of the provenance, lineage and flow of intellectual property. The R dimensions Replacing the Paper: The Twelve Rs of the e-Research Record” on http://blogs.nature.com/eresearch/
    • used wasGeneratedBy wasStartedAt "2012-06-21" Metagenome Sample wasAssociatedWith Workflow server wasInformedBy wasStartedBy Workflow run wasGeneratedBy Results Sequencing wasAssociatedWith Alice hadPlan Workflow definition hadRole Lab technician Results https://w3id.org/bundleStian Soiland-Reyes Research Object Bundle
    • Research Objects Computational Research Objects Workflows Packs OAI ORE W3CPROV
    • Join the W3C Community Group www.w3.org/community/rosc www.researchobject.org
    • 1. The End of the Article 2. How digital research is done today 3. Social Objects 4. Social Machines
    • Nigel Shadbolt et al
    • Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration… The stage is set for an evolutionary growth of new social engines. Berners-Lee, Weaving the Web, 1999 The Order of Social Machines
    • Some Social Machines
    • myExperiment is a Social Machine protected by the reCAPTCHA Social Machine “The myExperiment social machine protected by the reCAPTCHA social machine was attacked by the spam social machine so we built a temporary social machine to delete accounts using people, scripts and a blacklisting social machine then evolved the myExp social machine into a new social machine…”
    • Whither the Social Machine? Kevin Page
    • Whither the Social Machine? Kevin Page
    • Whither the Social Machine? Kevin Page
    • What to observe?  Logs  Analytics  Data findings e.g. Success rate of transcription  Social sciences  Qualitative study  Motivation Individual and group  Mixed methods  Differences in technique and scale  Unlikely to be an simple transferable metric Kevin Page
    • Trajectories... distinguished by purpose Trajectories through Social Machines https://sites.google.com/site/bwebobs13/ Kevin Page
    • Cat De Rourehttp://botornot.net
    • https://support.twitter.com/entries/18311-the-twitter-rules
    • Scholarly Machines Ecosystem
    • Scholarly Machines Ecosystem
    • Physical World (people and devices) Building a Social Machine Design and Composition Participation and Data supply Model of social interaction Virtual World (Network of social interactions) Dave Robertson
    • An informal definition of a digital library is a managed collection of information, with associated services, where the information is stored in digital formats and accessible over a network
    • Bush, Vannevar (July 1945). "As We May Think". The Atlantic.
    • Correspondence Chess http://commons.wikimedia.org/wiki/File:Postcard-for-correspondence-chess.jpg
    • 1. The End of the Article Still necessary but no longer sufficient 2. How digital research is done today New methods, automation and more to come 3. Social Objects Why papers work so well, and new artefacts are emerging 4. Social Machines This community knows how to help design Social Machines… and a Social Machines ecosystem In Conclusion
    • • Where’s the critical reflection in the new paradigm? • Am I guilty of data fundamentalism? • User-centric, but not discussed User Experience • Object conflation – maybe computers need different social objects? • Is this Taylorization of research? • Are we burning a paradigm into the infrastructure? Critical thinking
    • david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder www.scilogs.com/eresearch @dder Slide credits: Christine Borgman, Iain Buchan, Ichiro Fujinaga, Kevin Page, Stephen Downie, Jun Zhao, Stian Soiland-Reyes, Nigel Shadbolt, Dave Robertson Thanks to the SOCIAM and SALAMI teams, to Carole Goble and colleagues in myExperiment, Wf4Ever, myGrid and FORCE11, to friends and colleagues in GSLIS and to students and colleagues at the DH@Ox Summer School 2013 SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org. Research also supported in part by Wf4Ever (FP7-ICT ICT-2009.4 project 270192), e-Research South (EPSRC EP/F05811X/1), Digital Social Research (ESRC RES-149-34-0001- A), Smart Society (FP7-ICT ICT-2011.9.10 project 600854). http://www.slideshare.net/davidderoure/social-machines-of-science-and-scholarship
    • Research Objects http://www.researchobject.org/ Social Machines http://sociam.org/ myExperiment http://www.myexperiment.org Wf4ever http://www.wf4ever-project.org Web Science Trust http://webscience.org/ FORCE11 http://www.force11.org SALAMI http://salami.music.mcgill.ca/ MIREX http://www.music-ir.org/mirex/ Zooniverse https://www.zooniverse.org/ DPRMA http://dprma.oerc.ox.ac.uk/ W3C Community Groups: ROSC http://www.w3.org/community/rosc/ Web Observatory http://www.w3.org/community/webobservatory