The Evolution of e-Research: Machines, Methods and Music

The Evolutionof e-Research
Machines, Methods and Music
David De Roure

Programming
1981
Maths
Electronics
Physics
Music
Medical electronics
Networks
PhD in distributed declarative
programming language design
Temporal Media
MIT
AJGH
PH
WH
Hypermedia
QBH
Large scale
Distributed
Systems
Transputers
ProcessNetworks
PEOPLE
EOPLE
Agents
AdvancedKnowledge
Technologies
Devices
Semantic Web
Workflows
Grid
Statistics
AmorphousComputing
Equator
Web 2
Web
Science
Linked
Data
VREs
Semantic
Grid
Environmental
sensing
e-Science
Digital SocialResearch
myExperiment
Semantic Sensor Networks
e-Laboratories
Computational Musicology
2010

Overview
Generation 1: Early adopters
Generation 2: Embedding
Generation 3: Radical sharing
SALAMI
A case study in 3rd generation e-Research

e-Science
e-Science was defined by John Taylor (Director General of the UK Research Councils) as
global collaboration in key areas of science and the next generation of infrastructure that will enable it
e-Science was the name of the destination
It became the name of the journey
When we arrive, the destination is just called science

e-Research
“e-research extendse-Science andcyberinfrastructureto other disciplines, including the humanities andsocial sciences.”
http://mitpress.mit.edu/catalog/item/default.asp?tid=12185&ttype=2

Generation 1
2000 – 2005

...the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites
Tony Hey and Anne Trefethen
Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203

Jeremy Frey
26/2/2007 | myExperiment | Slide 8

E. Science laboris
Workflows are the new rock and roll
Machinery for coordinating the execution of (scientific) services and linking together (scientific) resources
The era of Service Oriented Applications
Repetitive and mundane boring stuff made easier
Carole Goble

Kepler
Triana
BPEL
Trident
Meandre
Taverna
Galaxy

co-shaping
co-design
co-evolution
co-
co-creation
co-construction
co-constitution
co-realisation

http://webscience.org

My Chemistry Experiment
Box of Chemists
CombeChem

CombeChem

empower to equip or supply with an ability; enable
servicethe performance of duties or the duties performed as or by a waiter or servant

Thanks to Iain Buchan
and the chipmunks
1st Generation Summary
Early adoptors of tools.
Characterised by researchers using tools within their particular problem area, with some re-use of tools, data and methods within the discipline.
Traditional publishing is supplemented by publication of some digital artefacts like workflows and links to data.
Science is accelerated and practice beginning to shift to emphasisein silicowork.

Generation 2
2005 – 2010

Reuse, Recycling, Repurposing
Paul writes workflows for identifying biological pathways implicated in resistance to Trypanosomiasis in cattle
Paul meets Jo. Jo is investigating Whipworm in mouse.
Jo reuses one of Paul’s workflow without change.
Jo identifies the biological pathways involved in sex dependence in the mouse model, believed to be involved in the ability of mice to expel the parasite.
Previously a manual two year study by Jo had failed to do this.
Carole Goble

Carole Goble “e-Science is me-Science: What do Scientists want?”, EGEE 2006
“There are these great collaboration tools that 12-year-olds are using. It’s all back to front.”
Robert Stevens

“A biologist would rather share their toothbrush than their gene name”
Mike Ashburner and others
Professor in Dept of Genetics,
University of Cambridge, UK

Data mining: my data’s mine and your data’s mine

photos
movies
slides
workflows

too passé!
Not
mySpace for scientists!
Facebook
too open!

Researchers
Social Scientists
Open Repositories
Social Networkers
Developers

A probe into researcher behaviour
Open source (BSD) Ruby on Rails app
REST and SPARQL interfaces, supports Linked Data
Inspiration for: BioCatalogue, MethodBox and SysMO-SEEK
“Facebook for Scientists” ...but different to Facebook!
A repository of research methods
A community social network of people and things
A Social Virtual Research Environment
myExperiment currently has 4400 members, 236 groups, 1336 workflows, 351 files and 141 packs

http://www.myexperiment.org/

Global collaboration in key areas of science and the next generation of infrastructure that will enable it
Visits to www.myexperiment.org (Oct 2010)
http://wiki.myexperiment.org

method
data

Though this be madness, yet there is method in it
*
Data bonanza => Methods bonanza!
***
Methods should be first class citizens
Celebrate the flux! Let the data flow through the pipelines. Nail down the methods not the data!
Towards “Linked Open Methods”
**
* Polonius in Hamlet ** Sean Bechhofer in Manchester *** Not the e-Science Envoy

It’s not just the data
It’s what you do with it that counts
And what other people do with it
...that you never thought of

Paul’s Pack
Paul’s Research Object
Workflow 16
QTL
Results
produces
Included in
Published in
Included in
Feeds into
Logs
produces
Included in
Included in
Metadata
Slides
Paper
produces
Published in
Common pathways
Workflow 13
Results

The Six Rs of Research Object Behaviours
Research Objects enable data-intensive research to be:
Replayable – go back and see what happened
Repeatable – run the experiment again
Reproducible – independent expt to reproduce
Reusable – use as part of new experiments
Repurposeable – reuse the pieces in new expt
Reliable – robust under automation
Referenceable– citable and traceable
http://blog.openwetware.org/deroure/?p=56

Semantically enhanced publication versus
Shared digital Research Objects
Challenging the mindset of paper-sized chunks

Documentsunder glass

2nd Generation Summary
Projects delivering now.
Some institutional embedding.
Key characteristic is re-use – of the increasing pool of tools, data and methods across areas/disciplines.
Contain some freestanding, recombinant, reproducible research objects.
New scientific practices are established and opportunities arise for completely new scientific investigations.
Some expert curation.

Generation 3
2010 – 2015

4th Paradigm
The Fourth Paradigm: Data-Intensive Scientific Discovery
Presenting the firstbroad look at the rapidly emerging field of data-intensive science
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

http://blogs.nature.com/fourthparadigm/

Doug Kell
BioEssays, 26(1):99–105, January 2004

Francois Belleau

Taverna
A Bioinformatics Experiment
Scott Marshall Marco Roos
“…to discover proteins that interact with transmembrane proteins, particularly those that can be related to neuro-degenerative diseases in which amyloids play a significant role”
Taverna provenance exposed as RDF
myExperiment RDF document for a protein discovery workflow
Mocked-up BioCatalogue document using myExperiment RDF data as example
Provisional RDF documents obtained from the ConceptWiki (conceptwiki.org) development server
An RDF document for an example protein, obtained from the RDF interface of the UniProt web site

LifeGuide
http://www.lifeguideonline.org/
Lucy Yardley

http://www.methodbox.org/
MethodBox
Enable cross disciplinary research into Major Public Health problems
Ease handling data and sharing results and insights

http://www.galaxyzoo.org/

Arfon Smith
http://www.zooniverse.org/

3rd Generation Summary
The solutions we'll be delivering in 5 years
Characterised by global reuse of tools, data and methods across any discipline, and surfacing the right levels of complexity for the researcher.
Routine use.
Key characteristic is radical sharing.
Research is significantly data driven – plundering the backlog of data, results and methods.
Publishing by the social network
Increasing automation and decision-support for the researcher – the VRE becomes assistive.
Curation is autonomic and social.

Find a service & relax
Intellectual ramps
Easy and low risk to start
Progress to advanced skills
For researchers
No obligation
Go as far as you want
Malcolm Atkinson

telescopes for the naked mind
Datascopes
Malcolm Atkinson
NRAO/AUI/NSF
From Signal to Understanding

Jeannette M. Wing COMMUNICATIONS OF THE ACM March 2006/Vol. 49, No. 3 Pages 33-35

Music and Linked Data
2010 – 2011and beyond

http://www.openarchives.org/ore/terms/aggregates
http://eprints.ecs.soton.ac.uk/id/eprint/20817

It’s about enabling the join
Ben Fields, 6th October 2010

SALAMI: Structural Analysis of Large Amounts of Music Information
David De Roure
J. Stephen Downie
Ichiro Fujinaga

www.diggingintodata.org

The SALAMI collaboration
DDeR (e-Research South), J. Stephen Downie (Illinois) and Ichiro Fujinaga (McGill)
NCSA donating 250,000 supercomputer hours
350,000 pieces of music (23,000 hours)
Internet Archive, DRAM, IMIRSEL, McGill
Feature analysis and structural analysis
Music Ontology by Yves Raimond (BBC)
Musicologists from McGill and Southampton
Sharing of analyses
http://salami.music.mcgill.ca

Digital Music Collections
23,000 hours ofrecorded music
Music InformationRetrieval Community
Community Software
Crowdsourced ground truth
Supercomputer
250,000 hours NCSASupercomputer time
Linked Data Repositories

Ashley Burgoyne
http://www.sonicvisualiser.org/

MIREX Overview
Began in 2005
Tasks defined by community debate
Data sets collected and/or donated
Participants submit code to IMIRSEL
Code rarely works first try 
Huge labour consumption getting programs to work
Meet at ISMIR to discuss results
Stephen Downie
http://www.music-ir.org/mirex

Meandre
seasr.org/meandre

It’s web-like!
“Ground Truth”
Community
Digital Audio
“Signal”
StructuralAnalysis
Q. If and when should community-generated content be assimilated into managed repositories?

How country is my country?
Kevin Page and Ben Fields
http://www.nema.ecs.soton.ac.uk/countrycountry/

Music and computational thinking
Stephen Downie

“Again, it [the Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should bealso susceptible of adaptations to the action of the operating notation and mechanism of the engine...”

“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.”
Ada, The Enchantress of Numbers: Poetical Science
by Betty Alexandra Toole
http://www.well.com/user/adatoole/
Betty Alexandra Toole

I can write a workflow that creates workflows based on those of others, and automatically modify it – think genetic mutation and crossovers. Who owns it?
I can register a query over an increasing number and diversity of “linked data” sources to ask new research questions.
The computer can learn from the activities of 1,000,000 scientists – and be indistinguishable from them?
What about the ethics of Citizen Social Science? Of citizens designing experiments?
http://eresearch-ethics.org/

Co-*
Methods
Access ramps
Research Objects
Computational thinking
Ethics of e-Research at scale
Enjoy the Open Day!

david.deroure@oerc.ox.ac.uk
Thanks to: Jeremy Frey & CombeChem; Carole Goble, myGrid and myExperiment; Iain Buchan & Obesity e-Lab; Sean Bechhofer; Doug Kell; Marco Roos; Lucy Yardley; Arfon Smith; Malcolm Atkinson; Stephen Downie, Kevin Page, Ben Fields, Ashley Burgoyneand NEMA/SALAMI; Betty Toole.
http://www.myexperiment.org/packs/153

The Evolution of e-Research: Machines, Methods and Music

by David De Roure

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