The document discusses the intersection of humanities and digital technology, emphasizing the role of data-intensive research and social machines in scholarship. It highlights challenges in collaborative projects like Wikipedia due to declining participation and the need for new approaches to scholarly communication. Supported by funding bodies, the text covers various aspects including social networks, citizen engagement, and advancements in data science applications.

1. David De Roure
@dder
Intersection, Scale, and Social Machines:
The Humanities in the Digital World
DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE

2. Data-intensive research
Human-intensive research
Music
Scholarly Communication

3. The Big Picture(s)
Challenging Assumptions

4. ChristineBorgman

5. 13,785,659
total
volumes
6,871,154
book
6tles
364,473
serial
6tles
4,824,980,650
pages
618
terabytes
163
miles
11,201
tons
5,372,477
public
domain
volumes
10,000,000,000,000,000 bytes archived!

6. New Forms of Data
▶ Internet data, derived from social
media and other online interactions
(including data gathered by
connected people and devices, eg
mobile devices, wearable
technology, Internet of Things)
▶ Tracking data, monitoring the
movement of people and objects
(including GPS/geolocation data,
traffic and other transport sensor
data, CCTV images etc)
▶ Satellite and aerial imagery (eg
Google Earth, Landsat, infrared,
radar mapping etc) http://www.oecd.org/sti/sci-tech/new-data-for-
understanding-the-human-condition.htm

7. The
Big
Picture
More people
Moremachines
Big Data
Big Compute
Conventional
Computation
“Big Social”
Social Networks
e-infrastructure
Online R&D
(Science 2.0)
Digital
Scholarship
@dder

8. theODI.org

9. Data Detect Store AnalyticsFilter Analysts
@dder

10. There is no such thing as the Internet of Things
There is no such thing as a closed system
Humans are creative and subversive
The Rise of the Bots A Swarm of Drones
Accidents happen (in the lab, bin)
Holding machines to account Software vulnerability
Where are the throttle points?
@dder

11. F i r s t

12. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and
Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552

13. Social Machines
Empowered Citizens

14. Social
Machines
Defini6on
TBL
Pip Willcox

15. https://twitter.com/CR_UK/status/446223117841494016/
Some people's smartphones
had autocorrected the word
"BEAT" to instead read
"BEAR".
"Thank you for choosing an
adorable polar bear," the
reply from the WWF said.
"We will call you today to set
up your adoption."
http://www.bbc.com/news/technology-26723457

17. 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

18. “Yet
Wikipedia
and
its
stated
ambi6on
to
“compile
the
sum
of
all
human
knowledge”
are
in
trouble.
The
volunteer
workforce
that
built
the
project’s
flagship,
the
English-­‐language
Wikipedia—and
must
defend
it
against
vandalism,
hoaxes,
and
manipula6on—
has
shrunk
by
more
than
a
third
since
2007
and
is
s6ll
shrinking…
The
main
source
of
those
problems
is
not
mysterious.
The
loose
collec6ve
running
the
site
today,
es6mated
to
be
90
percent
male,
operates
a
crushing
bureaucracy
with
an
oYen
abrasive
atmosphere
that
deters
newcomers
who
might
increase
par6cipa6on
in
Wikipedia
and
broaden
its
coverage…”
http://www.technologyreview.com/featuredstory/520446/the-decline-of-wikipedia/

20. “Panoptes has been designed so
that it’s easier for us to update
and maintain, and to allow
more powerful tools for project
builders. It’s also open source
from the start, and if you find
bugs or have suggestions about
the new site you can note them
on Github (or, if you’re so
inclined, contribute to the
codebase yourself).”
"
http://blog.zooniverse.org/2015/06/29/a-whole-new-zooniverse/
http://monsterspedia.wikia.com/wiki/File:Argus-Panoptes.jpg
Panoptes

21. Musical Social Machines
Social Machines of Scholarship

22. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.
ê
The
Problem
signal
understanding
Ichiro Fujinaga

23. 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

24. Sequence alignment
http://en.wikipedia.org/wiki/Sequence_alignment#/media/File:Histone_Alignment.png

25. Dan Edelstein, Robert Morrissey, and Glenn Roe, To Quote or not to Quote: Citation Strategies in the Encyclopédie.
Journal of the History of Ideas , Volume 74, Number 2, April 2013 . pp. 213-236. 10.1353/jhi.2013.0012
Glenn Roe

26. Digital
Music
Collec6ons
Grad-­‐sourced
ground
truth
Community
SoYware
Linked
Data
Repositories
Supercomputer
23,000 hours of
recorded music
Music Information
Retrieval Community
SALAMI

27. Ashley Burgoyne

28. www.music-ir.org/mirex
Music Information Retrieval Evaluation eXchange
Audio Onset Detection
Audio Beat Tracking
Audio Key Detection
Audio Downbeat Detection
Real-time Audio to Score Alignment(a.k.a
Score Following)
Audio Cover Song Identification
Discovery of Repeated Themes & Sections
Audio Melody Extraction
Query by Singing/Humming
Audio Chord Estimation
Singing Voice Separation
Audio Fingerprinting
Music/Speech Classification/Detection
Audio Offset Detection
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

29. Stephen
Downie

30. http://chordify.net/

31. Digital
Material
Pip Willcox

32. Kevin Page
David Weigl
Interfaces, for
computer and
human
!

34. Sonifying
the
Variants
• From
Play
to
Sonifica6on
• Using
First
Folio
and
Quartos
data
• Parsing
the
TEI
XML,
conver6ng
it
with
rule
set
into
numbers,
sonifying
the
data
to
produce
sounds
34
Sonification
Iain Emsley

35. Studying Social Machines
Scholarship of Social Machines

36. Ecosystem
Perspective
• We see a community of
living, hybrid organisms,
rather than a set of
machines which happen to
have humans amongst
their components
• Their successes and
failures inform the design
and construction of their
offspring and successors

37. time
Social Machine instances
@dder

38. Observer of
one social
machine
Observers using third
party observatory
Observer of
multiple social
machines
Human
participants in
Social
Machine
Human participants in
multiple Social Machines
Observer of Social
Machine infrastructure
1
4
2
3
5
6
SM
SM
SM
Social Machine
Observing Social
Machines
7
@dder
De Roure, D.,
Hooper, C., Page,
K., Tarte, S., and
Willcox, P. 2015.
Observing Social
Machines Part 2:
How to Observe?
ACM Web Science

39. The Web
Observatory
Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D.,
Contractor, N. and Hendler, J. 2013. The Web Science
Observatory, IEEE Intelligent Systems 28(2) pp 100–104.
ThanassisTiropanis

40. Simpson, R., Page, K.R. and
De Roure, D. 2014.
Zooniverse: observing the
world's largest citizen science
platform. In Proceedings of
the companion publication of
the 23rd international
conference on World Wide
Web, 1049-1054.
Kevin Page

41. STORYTELLING AS A STETHOSCOPE
FOR SOCIAL MACHINES
1. Sociality through storytelling potential
and realization
2. Sustainability through reactivity and
interactivity
3. Emergence through collaborative
authorship and mixed authority
Zooniverse
is
a
highly
storified
Social
Machine
Facebook
doesn’t
allow
for
improvisa6on
Wikipedia
assigns
authority
rights
rigidly
http://ora.ox.ac.uk/objects/ora:8033
Tarte, S.M., De Roure, D. and Willcox, P. 2014. Working out the Plot: the Role of
Stories in Social Machines. SOCM2014: The Theory and Practice of Social
Machines, Seoul, Korea, International World Wide Web Conferences pp. 909–914

42. Pip Willcox

43. Tarte, S. Willcox, P., Glaser, H. and De Roure, D. 2015. Archetypal Narratives in Social
Machines: Approaching Sociality through Prosopography. ACM Web Science 2015.
SégolèneTarte

44. Scholarly Communication
Preface

46. Elizabeth Williamson

47. Richard O’Bierne

48. A computationally-enabled
sense-making network of
expertise, data, software,
models and narratives
Big Data, in a
Big Data Centre

49. Pip Willcox and Kevin Page

50.
consume
produce
compose
perform
capture
distribute
Mark
Sandler
Curate
Preserve
!

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

52. The
R
Dimensions
Research
Objects
facilitate
research
that
is
reproducible,
repeatable,
replicable,
reusable,
referenceable,
retrievable,
reviewable,
replayable,
re-­‐interpretable,
reprocessable,
recomposable,
reconstructable,
repurposable,
reliable,
respecful,
reputable,
revealable,
recoverable,
restorable,
reparable,
refreshable?”
@dder 14 April 2014
sci
method
access
understand
new
use
social
cura6on
Research
Object
Principles
De Roure, D. 2014. The future
of scholarly communications.
Insights: the UKSG journal,
27, (3), 233-238.
DOI 10.1629/2048-7754.171

53. https://www.gartner.com/technology/research/digital-marketing/transit-map.jsp

54. Intersection, Scale, and
Social Machines:
The Humanities in the
Digital World

55. First
Folio
Social
Machines
Metadata
Story of the
First Folio
Social
Machines Annotation
David De Roure and Pip Willcox
‘“Coniunction, with the participation of Society”: Citizens, Scale, and
Scholarly Social Machines’
Beyond the PDF: Born-Digital Humanities, Boston, 27–28 April 2015
Pip Willcox

56. PipWillcox

57. david.deroure@oerc.ox.ac.uk @dder
Thanks to Tim Crawford, Mark d’Inverno, Stephen Downie,
Iain Emsley, Ichiro Fujinaga, Chris Lintott, Grant Miller,
Terhi Nurmikko-Fuller, Kevin Page, Carolin Rindfleisch,
Glenn Roe, Mark Sandler, Ségolène Tarte, David Weigl, and
Pip Willcox.
http://www.slideshare.net/davidderoure/humanities-in-the-digital-world
Supported by SOCIAM: The Theory and Practice of Social Machines, funded by the UK Engineering and Physical
Sciences Research Council (EPSRC) under grant number EP/J017728/1; Fusing Semantic and Audio Technologies for
Intelligent Music Production and Consumption (FAST) funded by EPSRC under grant number EP/L019981/1; and
Transforming Musicology, funded by the UK Arts and Humanities Research Council under the Digital Transformations
programme. Thanks also to the Andrew W. Mellon Foundation.

58. www.oerc.ox.ac.uk
david.deroure@oerc.ox.ac.uk
@dder