Innovations 2013 - e-Science, we-Science and the latest evolutions in e-publishing. STM International Association of Scientific, Technical & Medical Publishers. 4th December 2013, Congress Centre, Great Russell Street, London, UK.

1. e-Research and the Demise
of the Scholarly Article
David De Roure

2. A revolutionary idea…
Open Science!

3. This is a Fourth Quadrant Talk
More machines
e-infrastructure
Big Data
Big Compute
The Fourth
The Future!
Conventional
Computation
Social
Networking
Quadrant
More people
David De Roure
online
R&D

4. Christine Borgman

5. First

6. Research Councils UK

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

8. Image
Classification
Talk
Forum
Citizen Scientists
data reduction
Scientists

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

10. http://www.scilogs.com/eresearch/pages-of-history/
David De Roure

11. http://www.scilogs.com/eresearch/pages-of-history/
David De Roure

12. 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…”

13. 2.
It was no longer possible to reconstruct a
scientific experiment based on a paper alone

14. 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

15. 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.

16. 5.
Single authorship gave way to casts of
thousands

17. 6.
Quality control models scaled poorly with
the increasing volume
Filter, Publish, Filter, Publish, …
Like big data, publishing has
increasing volume, variety and
velocity
But what about veracity?

18. 7.
Alternative reporting necessary for
compliance with regulations
One piece of research
may have multiple
reports and multiple
narratives for multiple
readerships, in multiple
formats and languages
(Computer are readers
too!)

19. 8.
Research funders frustrated by inefficiencies
in scholarly communication
An investment is only worthwhile if
• Outputs are discoverable
• Outputs are reusable
…and preferably outputs accrue value through use
Using an obsolete scholarly communication system
impedes innovation and hence return on investment
What are we doing about it?
Trying to fix it using an obsolete scholarly
communication system!

20. Big data elephant versus sense-making network?
The challenge is to foster the co-constituted socio-technical
system on the right i.e. a computationally-enabled sensemaking network of humans and machines sharing social
objects… not just papers but
data, models, software, narratives – new digital artefacts we
call research objects.

21. method
script
protocol
program
workflow
model
data
…

22. Neil Chue Hong

23. http://www.myexperiment.org/

24. Evolving the myExperiment Social Machine
OAI
ORE
Workflows
Packs
Research Objects
W3C PROV
Computational
Research Objects

25. The R dimensions
Reusable. The key tenet of Research Replayable. Studies might involve
Objects is to support the sharing and single investigations that happen in
milliseconds or protracted processes
reuse of data, methods and
that take years.
processes.
Referenceable. If research objects
Repurposeable. Reuse may also
are to augment or replace traditional
involve the reuse of constituent
publication methods, then they must
parts of the Research Object.
be referenceable or citeable.
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
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
start with the same inputs and
methods and see if a prior result can of the provenance, lineage and flow
of intellectual property.
be confirmed.
http://www.scilogs.com/eresearch/replacing-the-paper-the-twelve-rs-of-the-e-research-record/

26. Jun Zhao
www.researchobject.org

27. The Order of 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.
Berners-Lee, Weaving the Web, 1999

28. Some Social Machines

29. Scholarly
Machines
Ecosystem

30. Discussion points
1. Citation in tomorrow’s sense-making
network of humans and machines:
– What are the artefacts / social objects?
– How and why are they cited?
2. Think about an ecosystem of interacting
Scholarly Social Machines
3. Science as a Social Machine?

31. Thanks to Christine Borgman, Iain Buchan, Neil Chue
Hong, Jun Zhao, Carole
Goble, FORCE11, myExperiment, Software Sustainability
Institute, wf4ever and SOCIAM
david.deroure@oerc.ox.ac.uk
www.oerc.ox.ac.uk/people/dder
http://www.scilogs.com/eresearch
@dder
www.oerc.ox.ac.uk
www.force11.org
www.researchobject.org
www.software.ac.uk
sociam.org