3. Overview
1. Shifts in scholarship
2. End of the article
3. Research Objects
4. Social Machines
4. The Big Picture
More people
Moremachines
Big Data
Big Compute
Conventional
Computation
“Big Social”
Social Networks
e-infrastructure
Online R&D
(Science
2.0)
Information
Society
@dder
(Social Machines)
5. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and
Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
17. 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…”
18. 2. It was no longer possible to reconstruct a
scientific experiment based on a paper alone
19. 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
20. 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.
22. 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?
23. 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!)
24. 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!
29. The R Dimensions
Research Objects facilitate research that is
reproducible, repeatable, replicable, reusable,
referenceable, retrievable, reviewable, replayable,
re-interpretable, reprocessable, recomposable,
reconstructable, repurposable, reliable,
respectful, reputable, revealable, recoverable,
restorable, reparable, refreshable?”
@dder 14 April 2014
sci method
access
understand
new use
social
curation
Research
Object
Principles
31. 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)
Social Machines
32. 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
34. 1. Shifts in scholarship
– A “turn” or ongoing transformation?
2. End of the article
– Don’t retrofit digital, think post-digital
3. Research Objects
– Inevitable with automation
– How do we cite them, how are they curated?
4. Social Machines
– Humans in the loop, empowered
– Can you view your projects as social machines?
35. 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
@dder
www.oerc.ox.ac.uk
www.force11.org
www.researchobject.org
www.software.ac.uk
sociam.org
ESRC was allocated 64m and much of this is being used to set up the ESRC Big Data Network. The ESRC’s Big Data Network will support the development of a network of innovative investments which will strengthen the UK’s competitive advantage in Big Data for the social sciences. The core aim of this network is to facilitate access to different types of data and thereby stimulate innovative research and develop new methods to undertake that research. Although you should note that diagram it is only illustrative in terms of how the UKDS and ADS will work across – that is still under discussion; and only illustrative in the number of Business and Local Government Data Research.This network has been divided into three phases. In Phase 1 of the Big Data Network the ESRC has invested in the development of the Administrative Data Research Network (ADRN) which will provide access to de-identified administrative data collected by government departments for research use – focus of this meeting and all your grants.A few words about Phase 2 and 3 before we pass to Vanessa to talk about the ADRN some more. Phase 2is currently bring commissioned and will deal primarily with business data and/ or local government data. Phase 3, further details of which will be released in the last autumn / winter and will focus primarily on third sector data and social media data. It is expected that there will be opportunities for interaction across all elements of the ESRC Big Data Network and that they will all work together around the wider objectives of facilitating access to different forms of data and of ensuring maximum impact is generated from the use of that data for the mutual benefit of data owners and researchers, and through the research facilitated by the Network, benefit society and the economy more generally.
ESRC Cities Expert Group
Thanks to Simon Hettrick for additional input to this slide.