Opening keynote talk at 11th eResearch Australasia Conference, Brisbane Convention and Exhibition Centre, 16 – 20 October 2017. Based in part on public lecture "The Imagination of Ada Lovelace" on Ada Lovelace day at ANU, slides co-authored with Pip Willcox.

1. David De Roure
@dder
Creativity in
Digital Scholarship
UNIVERSITY OF OXFORD E-RESEARCH CENTRE

3. Grid compu+ng is promoted as the enabler for e-Science
but may actually be missing the point. This is the argument
that will be put forward by Professor David De Roure in his
keynote address ‘e-Science is about Scien+sts too’ to be
delivered tomorrow (Wednesday 27 June) at the eResearch
Australasia conference in Brisbane.
‘Although it has the best inten+ons, the Grid is some+mes
its own worst enemy’, said De Roure. ‘If we want to enable
new science then we need to empower the scien+st.
Delivering a new infrastructure is only part of the solu+on.’
De Roure will argue that the grid community can learn from the evolu+on of the Web - and
that e-Science developers should look to Web as well as Grid as their plaMorm. ‘It remains a
point of debate as to whether the func+onality of the Grid can be delivered through the far
simpler programming interfaces of the Web - I believe it can.’
In the abstract of his paper to be given at Brisbane, De Roure writes: ‘eScience presents a
vision of new scien+fic outcomes enabled by a new infrastructure. This cyberinfrastructure or
eInfrastructure perspec+ve brings with it a mindset of delivery of grid services to users. But is
this approach fundamentally wrong? If we look at what researchers actually do, perhaps we
will find that some new thinking is required. This talk promotes a people-centric perspec+ve
on eScience infrastructure and suggests that it is +me to re-evaluate the Grid ambi+on.’
http://www.ecs.soton.ac.uk/news/1351

5. Experimental methods have been
used in research since at least the
seventeenth century.
e-Research has given us new
computa+onal approaches and
enabled in silico experimenta+on,
and our community has been
responsible for innova+ng and
studying these new methods.
We look at the trajectory of the
experimental approach in the face of
both increasing ci+zen engagement
and increasing automa+on, and
demonstrate the emerging prac+ce
of “experimental humani+es”.
Ul+mately this is about the role of
the human in the future of research.
• Adventures with
Lovelace and
Babbage*
• Digital Scholarship
• Looking ahead
Sydney Padua
*Based on “The Imagination of Ada Lovelace”
public lecture at ANU on Ada Lovelace day
(10th October), coauthored with Pip Willcox.

6. A context
Jacquard
loom
London:"
the"
Waltz"
Lovelace"
born"
Darwin:"
Origin of !
Species!
Babbage"
dies"
Helmholtz:"
Sensations!
of Tone!
Dickens"
dies"
Markov"
born"
Lovelace"
dies"
Austen"
dies"
Lovelace"
presented"
Lovelace"
meets"
Babbage"
Wheatstone:
telegraph
Coronation:
Victoria
Lovelace:"
Translator’s!
Notes!

7. Images:ByCreator:CharlesHayter(NationalPortraitGallery)[PublicdomainorPublicdomain],
viaWikimediaCommons
ByRichardWestall(died1836)[Publicdomain],viaWikimediaCommons
ByUnknown(JohnMurrayPublishersLondon)(ScannedfromByronandhisWorldbyDerek
Parker.)[Publicdomain],viaWikimediaCommons
ByStippleengravingbyAndrewBirrell,afterSylvesterHarding[Publicdomain],viaWikimedia
Commons.
[Publicdomain]viahttp://www.thisbrighton.co.uk/Images/city-politics/dr-william-king.jpg.
ThomasPhillips[Publicdomain],viaWikimediaCommons.
BySophiaElizabethDeMorgan(MemoirofAugustusDeMorgan)[Publicdomain],via
WikimediaCommons
Anne Isabella
(Annabella)
Milbanke
1792 – 1860
George Gordon
Byron
1788 – 1824
Augusta Ada Byron
1815 – 1852
William Frend
1757 – 1841
William King
1786 – 1865
Mary Fairfax
Somerville
1780 – 1872
Augustus de
Morgan
1806 – 1871
Augusta Ada Noel Byron

8. #WorldMathsDay

9. The daughter
Images
ByCreator:CharlesHayter(NationalPortraitGallery)[PublicdomainorPublicdomain],via
WikimediaCommons.
ByRichardWestall(died1836)[Publicdomain],viaWikimediaCommons.
Scannedfrom TheCalculatingPassionofAdaByron byJoanBaum.Originallyfromthe
Lovelace-ByronCollection. [Publicdomain],viaWikimediaCommons.
Mad, bad, and
dangerous to
know
Caroline Lamb
Mathematical,
methodical, and
moral
Annabella Byron Science
vs.
Creativity?

10. Images
AntoineClaudet. Daguerreotype ofAdaLovelace,ownedbyGeoffreyBond.
AlfredEdwardChalon[Publicdomain],viaWikimediaCommons
AntoineClaudet[Publicdomain],viaWikimediaCommons.
A generative friendship, 1833 –1852

11. Lovelace translates and notates Menebrea
“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.”
Photograph:PipWillcox,fromTaylor’sScientific
MemoirsprovidedbyMagdalenCollegeLibrary
A.A. Lovelace (Trans.) Sketch of the analytical engine invented by
Charles Babbage, with notes by the translator. In Scientific Memoirs,
Selected from the Transactions of Foreign Academies of Science and
Learned Societies, Vol. 3, 1843, pp. 666-731, volume 3.

12. https://podcasts.ox.ac.uk/series/ada-lovelace-symposium-
celebrating-200-years-computer-visionary
Ada Lovelace Symposium
10–11 December 2015

13. DMRN+9

14. Copyright:SydneyPadua,
http://sydneypadua.com/2dgoggles/the-marvellous-
analytical-engine-how-it-works/
Sydney Paduahttp://sydneypadua.com/

15. Ada Lovelace: Notes and Numbers
“… the Mill has two 50 digit Ingress Axes to which arguments
are transferred, with the first Ingress Axis having an auxiliary
50 digit Primed Axis which receives the most significant 50
digits when a 100 digit number is divided by a number of up to
50 digits. Results from an arithmetic operation appear on the
Mill's 50 digit Egress Axis, which is also accompanied by a 50
digit Primed axis which, in the case of division, receives the
quotient while the remainder appears on the main Egress Axis.
In addition, the Mill has a Run-up Lever which is activated
when exceptional conditions arise during an arithmetic
operation.”
John Walker hYps://www.fourmilab.ch/babbage/

16. https://www.fourmilab.ch/babbage/
emulator.html
N0 35 . modulus
N1 82 . number of numbers
N2 1 . a
N3 1 . b
N4 0 . number counter
N5 2 . constant 2
(?
/
L2
L0
S2
P
L3
L0
S3
P
+
L2
L3
S2
L2
L3
S3
L4 . update count
L5
S4
-
L4
L1
)

17. Ada Lovelace: Notes and Numbers all F4-A5 D1-C4
Fib 35 Theme (F4-A5)
Fib 35 Puffle (D1-C4)

18. Piano
Harp
Piano
Xylophone
Tuba
3
4
4
4
4
4
4
&
Fibonacci mod 35 ('Puffle')
PipWillcox
David De Roure
for Analytical Engine, Piano, Harp, andTuba
? ∑
&
& ∑ ∑
? ∑
&
? ∑ ∑
œ œ œ ‰™
œ
r ≈ œ
œ
Œ Ó Ó
Œ
Ϫ
j ≈ œ
r ‰™ œ
J
‰
Ó Ó œ
œ œ œ
œ
œ
œ
œ
œ
œ
Œ œ
j ‰ œ
œ
≈ ‰ œ œ
≈ œ œ
œ ‰
œ
œ ‰™ œ
R
Œ œ
j ‰ Œ Ó
Ó Ó Œ
œ
œ
œ
œ
Ϫ
œ œ œ œ
œ œ
Ó
Ó ‰
œ œ œ œ
œ

20. Euler’s sequence: https://oeis.org/A275314
http://numbersintonotes.net
Mathematics in Lovelace’s day
20!
Fibonacci—Lagrange noted periodic functions in
Fibonacci numbers in 1774, the “Pisano period’
Pi—Pi was calculated by hand to 100 decimal places in
1706 using Machin's algorithm
Golden Ratio—The number that the ratio of successive
Fibonacci numbers converges to (1.618...)
Prime numbers—Calculated here using the Sieve of
Eratosthenes
Random—Musical dice games were popular in the 1700s
and generate random numbers
Factorials—The notation for factorial, e.g. 20!, was
introduced in 1808
Consonance— A mathematical function for calculating
consonance was defined by Leonhard Euler in1739

21. http://numbersintonotes.net

23. Numbers Into Notes: the première
Photograph:PipWillcox
https://soundcloud.com/alain_du_norde/the-
gift-the-algorithm-beyond-autonomy-and-
control

24. Computational creativity?
MargaretSarahCarpenter[Publicdomain],viaWikimedia
Commons
“The Analytical Engine has no
pretensions to originate anything.
It can do whatever we know how
to order it to perform. It can follow
analysis; but it has no power of
anticipating any analytical relations
or truths. Its province is to assist us
in making available what we are
already acquainted with.”
Boden’s Lovelace questions:
1. Helping human creativity
2. Appearing creative
3. Recognizing creativity
4. Creating

25. • Programmable circuit boards: Arduinos
• Replicating Numbers Into Notes Web app
• Controlled by co-creating participants
• Selecting and mapping a subset of notes
• Learning to suggest augmentations
Music engines
“The engine is capable, under certain
circumstances, of feeling about to
discover which of two or more possible
contingencies has occurred, and of then
shaping its future course accordingly.”
Photographs:DavidDeRoureandPipWillcox
Lovelace:

26. Material algorithms
USB
MIDI
• Hardware is a physical object
containing the algorithm
• Plugs in anywhere that
expects a MIDI keyboard
• Algorithm can be “gifted”
c = a + b;
if (c >= m) { c -= m; }
a = b;
b = c;

30. www.rncm.ac.uk/prism/

31. https://www.rncm.ac.uk/prism/

32. CharlesWheatstone
“A MURDER HAS GUST BEEN COMMITTED AT
SALT HILL AND THE SUSPECTED MURDERER
WAS SEEN TO TAKE A FIRST CLASS TICKET
TO LONDON BY THE TRAIN WHICH LEFT
SLOUGH AT 742 PM HE IS IN THE GARB OF A
KWAKER…”
https://en.wikipedia.org/wiki/Cooke_and_Wheatstone_telegraph
(John Tawell, 1845)

33. https://blog.sciencemuseum.org.uk/revealing-the-real-cooke-and-wheatstone-telegraph-dial/

34. n.(n – 1)

35. n.(n + 1)

36. Shaffner,TaliaferroPreston,TheTelegraphManual,Pudney&Russell,1859.

37. Part of Charles Babbage's Difference Engine #1
assembled by his son Henry Prevost Babbage.
Exhibit at Science Museum, London, photo DDeR.

38. Thinking through making
“It makes a difference whether we think in terms of
processes or of products.”
“a prototype is a theory”—Lev Manovich (2007)
“one of the goals of the designer has been deliberately to
carry out an interpretive act in the course of producing an
artifact"
Galey,A., Ruecker, S.‘How a prototype argues.’
Literary and Linguistic Computing 2010, (25)4:
405-24. doi:10.1093/llc/fqq021.

39. Experimental humanities:
methods and practices
1. Taking inspiration from ideas of the past
2. Generating and testing hypotheses
3. Designing digital artefacts
4. Informing intuitions
5. Explicating interpretations
6. Engaging with scholars and citizens
7. Evaluating and co-creating iterations
De Roure and Willcox Numbers into Notes: digital
prototyping as close reading of Ada Lovelace’s
‘Note A’, Digital Humanities 2017, Montreal, pp.215-6

40. And now
for something
completely different…
…or is it?

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

42. Digital
Scholarship
Social
Networks
Four quadrants
More people
More machines
Distributed
Computation
Conventional
Computation
Science 2.0
Big Data
Ci+zen
Science
Machine Learning
Scien+fic Compu+ng
Cybersecurity
Automa+on
Web 2.0
@dder

43. Nigel Shadbolt et al

44. Social Machines
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. The
ability to create new forms of social process would be
given to the world at large, and development would be
rapid.
Berners-Lee, Weaving the Web, 1999 (pp. 172–175)

45. hYps://twiYer.com/CR_UK/status/446223117841494016/
Some people's smartphones
autocorrected "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 adop+on."
hYp://www.bbc.com/news/technology-26723457

46. Social Media Triangle
social media
data and
analytics
social media
for engagement
with research
social media
as a subject
of research
Sam McGregor

47. The Macroscope

48. www.zooniverse.org

49. Scientists
Talk
Forum
Image
Classification
data reduction
Citizen Scientists

50. Community
Somware
Supercomputer
Digital Music
Collec+ons
Student-sourced
ground truth
Community
Somware
Linked Data
Repositories
Supercomputer
23,000 hours of
recorded music
Music Information
Retrieval Community
SALAMI

51. Carolin Rindfleisch

52. Human Digital
Physical
@dder
Human
Physical
DigitalSocial
Media
Internet of Things

55. • Research workflows
– To handle bigger and real+me data
– To share methods and reproduce research
• Scholarly communica+on workflows
– To find and to publish
– To implement policy
• Machine learning
– “Narrow AI” to perform human tasks
Automa+on today

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

57. 1. In the future “digital” will be pervasive and we won’t need
to use the word
§ e-Research == Research
§ Digital Scholarship == Scholarship
2. But we always need to innovate in new methods, to
experiment, to be crea+ve
§ e-Research isn’t just a service, it’s a crea+ve process
3. Insight into the future of scholarship informs conversa+ons
about the future of our knowledge infrastructure
§ Libraries, publishing, research data
Conclusions

58. 1. If Babbage had built the Analy+cal Engine and
Lovelace had lived, how would Data Science be
different today?
2. When we look back at Babbage & Lovelace today
we are used to thinking binary and electronics, not
decimal and mechanics. What will be people be
thinking when they look back at us in 20 or 200
years?
Two thought experiments

59. Ada Lovelace: the first e-Researcher?
“Those who have learned
to walk on the thresholds
of unknown worlds…may
then with the fair white
wings of Imagination
hope to sore further into
the unexplored amidst
which we live.”
daguerreotype1842-3,courtesyofGeoffreyBond

60. David De Roure
david.deroure@oerc.ox.ac.uk
@dder
Thanks to Pip Willcox and all our colleagues, collaborators, and contributors including Steve
Benford, Alan Chamberlain, Tim Crawford, J. Stephen Downie, Ian Emsley, Ichiro Fujinaga,
Chris Greenhalgh, Emily Howard, Adrian Johnstone, Graham Klyne, David Lewis, Chris
Lintott, Ursula Martin, Grant Miller, Terhi Nurmikko-Fuller, Sydney Padua, Kevin Page,
Michelle Phillips, Andrew Prescott, John Pybus, Alfie Abdul Rahman, Lasse Rempe-Gillen,
Carolin Rindfleisch, Mark Sandler, Marcus du Sautoy, Nigel Shadbolt, Ségolène Tarte,
Victoria Van Hyning, Max Van Kleek, Marc Weber, David Weigl, Mat Wilcoxson.
FAST EP/L019981/1 TMus AH/L006820/1 SOCIAM EP/J017728/1
Pip Willcox
pip.willcox@bodleian.ox.ac.uk
@pipwillcox