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

1. David De Roure
Social Machines of
Science and Scholarship

2. 1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines

3. http://www.scilogs.com/eresearch/pages-of-history/

4. A revolutionary idea…
Open Science!

5. http://www.scilogs.com/eresearch/pages-of-history/DavidDeRoure

6. 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…”

7. 2. It was no longer possible to reconstruct a
scientific experiment based on a paper alone

8. 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

9. 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.

10. 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

11. 6. Quality control models scaled poorly with
the increasing volume

12. 7. Alternative reporting necessary for
compliance with regulations

13. 8. Research funders frustrated by inefficiencies
in scholarly communication
An investment is only worthwhile if
• Outputs are discoverable
• Outputs are reusable
• Outputs accrue value

14. 1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines

15. ChristineBorgman

16. 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

17. F i r s t
BioEssays,,26(1):99–105,January2004
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

18. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.

The Problem
signal
understanding

19. http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score

20. Digital Music
Collections
Student-sourced
ground truth
Community
Software
Linked Data
Repositories
Supercomputer
23,000 hours of
recorded music
Music Information
Retrieval Community
SALAMI

21. Ashley Burgoyne

22. 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

23. 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

24. 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

25. seasr.org/meandreMeandre

27. Stephen Downie

28. Web as lens
Web as artefact
Web Observatories
http://www.w3.org/community/webobservatory/

29. Scientists
Talk
Forum
Image
Classification
data reduction
Citizen Scientists

30. Information Circuits
Community
Software
Execution
Digital Music
Community
annotation
Linked Data
Repositories
Workflows
Generate
Paper
Conference

31. Notifications and automatic re-runs
Machines are users too
Autonomic
Curation
Self-repair
New research?

32. 1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines

33. 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

34. data
method

35. Knowledge
InfrastructureKnowledge
Objects
Descriptive
layer
Observatories
Annotation

36. http://www.myexperiment.org/

37. 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/

38. 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

39. Research Objects
Computational
Research Objects
Workflows
Packs
OAI
ORE
W3CPROV

41. Join the W3C Community Group www.w3.org/community/rosc
www.researchobject.org

42. 1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines

43. Nigel Shadbolt et al

44. 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

45. Some Social Machines

47. 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…”

48. Whither the Social Machine?
Kevin Page

49. Whither the Social Machine?
Kevin Page

50. Whither the Social Machine?
Kevin Page

51. 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

52. Trajectories... distinguished by purpose
Trajectories through Social Machines https://sites.google.com/site/bwebobs13/
Kevin Page

53. Cat De Rourehttp://botornot.net

54. https://support.twitter.com/entries/18311-the-twitter-rules

55. Scholarly Machines Ecosystem

56. Scholarly Machines Ecosystem

57. 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

58. 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

59. Bush, Vannevar (July 1945). "As We May Think". The Atlantic.

60. Correspondence Chess
http://commons.wikimedia.org/wiki/File:Postcard-for-correspondence-chess.jpg

61. 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

62. • 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

63. 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

64. 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