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Transcript of Scholarly Social Machines
David De Roure
Scholarly Social Machines
1. The End of the Article
2. How digital research is done today
3. Social Objects
4. Social Machines
http://www.scilogs.com/eresearch/pages-of-history/
A revolutionary idea…Open Science!
http://ww
w.scilogs.com
/eresearch/pages-of-history/D
avid
De
Ro
ure
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…”
2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
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
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.
5. Single authorship gave way to casts of thousands
6. Quality control models scaled poorly with the increasing volume
7. Alternative reporting necessary for compliance with regulations
8. Research funders frustrated by inefficiencies in scholarly communication
An investment is only worthwhile if• Outputs are discoverable• Outputs are reusable• Outputs accrue value
More people
Mor
e m
achi
nes
This is a Fourth Quadrant Talk
Big DataBig Compute
Conventional Computation
The Future!
SocialNetworking
e-infrastructure
onlineR&D
The Fourth Quadrant
F i r s t
Bio
Ess
ays,
, 26
(1):
99–
105,
Jan
uary
200
4
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
The Problem
signal
understanding
http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score
Digital Music Collections
Student-sourced ground truth
Community Software
Linked Data Repositories
Supercomputer
23,000 hours ofrecorded music
Music InformationRetrieval Community
SALAMI
Ashley Burgoyne
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
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
MIREX TASKSAudio 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 ww
w.m
usic
-ir.o
rg/m
irex
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
seasr.org/meandreMeandre
Stephen Downie
Information Circuits
Community Software
Execution
Digital MusicCommunity annotation
Linked Data Repositories
Workflows
Generate Paper
Conference
Notifications and automatic re-runs
Machines are users too
Autonomic
Curation
Self-repair
New research?
Scientists
TalkForum
ImageClassification
data reduction
Citizen Scientists
Web as lens
Web as artefact
Web as
infrastructure
Web Observatorieshttp://www.w3.org/community/webobservatory/
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.
This requires a “social machines” perspective from the outset as well as humanistic input. The Web, and with it Web Science, are an important exemplar.
Big data elephant versus sense-making network?
data
method
KnowledgeInfrastructureKnowledge
Objects
Descriptivelayer
Observatories
An
no
tati
on
http://www.myexperiment.org/
Research Objects
ComputationalResearch Objects
Evolving the myExperiment Social Machine
WorkflowsPacks O
AIO
RE
W3C PRO
V
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 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/
Join the W3C Community Group www.w3.org/community/rosc
www.researchobject.org
Nigel Shadbolt et al
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
Some Social Machines
myExperiment is a Social Machineprotected by the reCAPTCHA Social Machine
Social M
achines of Spam
Whither the Social Machine?
Whither the Social Machine?
Whither the Social Machine?
What to observe? Logs Analytics Data findings
e.g. Success rate of transcription
Social sciences Qualitative study Motivation
Individual andgroup
Mixed methods Differences in
technique and scale Unlikely to be an simple
transferable metric
Trajectories... distinguished by purpose
Trajectories through Social Machines https://sites.google.com/site/bwebobs13/
Cat De Rourehttp://botornot.net
https://support.twitter.com/entries/18311-the-twitter-rules
ScholarlyMachinesEcosystem
The Social Machines of MIR
SoundCloudMirex
Machine
eTree Internet Archive
Annotation machine
ISMIRMachinePeer review
MusicBrainz
recommenders
Physical World(people and devices)
Building a Social Machine
Design andComposition
Participation andData supply
Model of social interaction
Virtual World(Network of social interactions)
Dave Robertson
Bush, Vannevar (July 1945). "As We May Think". The Atlantic.
Correspondence Chess
http://commons.wikimedia.org/wiki/File:Postcard-for-correspondence-chess.jpg
1. The End of the Article– Necessary but not sufficient
2. How digital research is done today– Systems which don’t fit in papers
3. Social Objects– Why papers work, new artefacts emerging
4. Social Machines– Design one tonight!
www.oerc.ox.ac.uk/people/dderwww.scilogs.com/eresearch
@dder
Slide credits: Christine Borgman, Iain Buchan, Ichiro Fujinaga, , Kevin Page, Stephen Downie, Nigel Shadbolt, Dave RobertsonSOCIAM: 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).
• Theory and Practice of Social Machines (SOCIAM)http://sociam.org/
• Future of Research Communication (FORCE11)http://force11.org/
• myExperiment project wikihttp://wiki.myexperiment.org/
• Workflow Forever project (Wf4Ever)http://www.wf4ever-project.org/
Links