social machines of science and scholarship
DESCRIPTION
Keynote talk at Joint Conference on Digital Libraries (JCDL) 2013, Indianapolis, 25 July 2013TRANSCRIPT
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David De Roure
Social Machines ofScience and Scholarship
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1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines
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http://www.scilogs.com/eresearch/pages-of-history/
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A revolutionary idea…Open Science!
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http://ww
w.scilogs.com
/eresearch/pages-of-history/D
avid
De
Ro
ure
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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…”
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2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
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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
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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.
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5. Single authorship gave way to casts of thousands Presented by
David De RoureTechnical Adviser
Kevin PageSoftware designerDon CruickshankMusical DirectorIchiro Fujinaga
Philosophy Consultantand Catering
J. Stephen Downie
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6. Quality control models scaled poorly with the increasing volume
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7. Alternative reporting necessary for compliance with regulations
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8. Research funders frustrated by inefficiencies in scholarly communication
An investment is only worthwhile if• Outputs are discoverable• Outputs are reusable• Outputs accrue value
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1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines
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Chr
istin
e B
orgm
an
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More people
Mor
e m
achi
nes
This is a Fourth Quadrant Talk
Big DataBig Compute
Conventional Computation
The Future!
SocialNetworking
e-infrastructure
onlineR&D
The Fourth Quadrant
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F i r s t
Bio
Ess
ays,
, 26
(1):
99–
105,
Jan
uary
200
4
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
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The Problem
signal
understanding
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http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score
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Digital Music Collections
Student-sourced ground truth
Community Software
Linked Data Repositories
Supercomputer
23,000 hours ofrecorded music
Music InformationRetrieval Community
SALAMI
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Ashley Burgoyne
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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
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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
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MIREX TASKSAudio 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 ww
w.m
usic
-ir.o
rg/m
irex
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
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seasr.org/meandreMeandre
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Stephen Downie
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Web as lens
Web as artefact
Web as
infrastructure
Web Observatorieshttp://www.w3.org/community/webobservatory/
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Scientists
TalkForum
ImageClassification
data reduction
Citizen Scientists
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Information Circuits
Community Software
Execution
Digital MusicCommunity annotation
Linked Data Repositories
Workflows
Generate Paper
Conference
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Notifications and automatic re-runs
Machines are users too
Autonomic
Curation
Self-repair
New research?
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1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines
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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
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data
method
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KnowledgeInfrastructureKnowledge
Objects
Descriptivelayer
Observatories
An
no
tati
on
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http://www.myexperiment.org/
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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/
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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
Resultshttps://w3id.org/bundleStian Soiland-Reyes
Research Object Bundle
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Research Objects
ComputationalResearch Objects
WorkflowsPacks O
AIO
RE
W3C PRO
V
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Join the W3C Community Group www.w3.org/community/rosc
www.researchobject.org
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1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines
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Nigel Shadbolt et al
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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
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Some Social Machines
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myExperiment is a Social Machineprotected 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…”
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Whither the Social Machine?
Kevin Page
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Whither the Social Machine?
Kevin Page
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Whither the Social Machine?
Kevin Page
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What to observe? Logs Analytics Data findings
e.g. Success rate of transcription
Social sciences Qualitative study Motivation
Individual andgroup
Mixed methods Differences in
technique and scale Unlikely to be an simple
transferable metric
Kevin Page
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Trajectories... distinguished by purpose
Trajectories through Social Machines https://sites.google.com/site/bwebobs13/
Kevin Page
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Cat De Rourehttp://botornot.net
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https://support.twitter.com/entries/18311-the-twitter-rules
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Scholarly Machines Ecosystem
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Scholarly Machines Ecosystem
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Physical World(people and devices)
Building a Social Machine
Design andComposition
Participation andData supply
Model of social interaction
Virtual World(Network of social interactions)
Dave Robertson
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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
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Bush, Vannevar (July 1945). "As We May Think". The Atlantic.
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Correspondence Chess
http://commons.wikimedia.org/wiki/File:Postcard-for-correspondence-chess.jpg
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1. The End of the ArticleStill necessary but no longer sufficient
2. How digital research is done todayNew methods, automation and more to come
3. Social ObjectsWhy papers work so well, and new artefacts are emerging
4. Social MachinesThis community knows how to help design Social Machines… and a Social Machines ecosystem
In Conclusion
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• 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
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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 RobertsonThanks 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
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Research Objects http://www.researchobject.org/Social Machines http://sociam.org/ myExperimenthttp://www.myexperiment.orgWf4ever http://www.wf4ever-project.org Web Science Trust http://webscience.org/ FORCE11 http://www.force11.orgSALAMI 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