Glimpses of future research practice: a musical study

David De Roure

Overview

• Generation 1 – Early adopters• Generation 2 – Embedding• Generation 3 – Radical sharing• Music case study

e-Science

• e-Science was defined by John Taylor (Director General of the UK Research Councils) asglobal 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 extendse-Science andcyberinfrstructureto other disciplines, including the humanities andsocial sciences.”

e-Research

http://mitpress.mit.edu/catalog/item/default.asp?tid=12185&ttype=2

2000 – 2005

Generation 1

...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

26/2/2007 | myExperiment | Slide 8

Jeremy Frey

• 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

E. Science laboris

Kepler

Triana

BPEL

Taverna

Trident

Meandre

Galaxy

co-shapingco-design

co-creation

co-constitution

co-evolution

co-construction

co-

co-realisation

humilitythe quality of being modest,

reverential, even politely submissive, and never being

arrogant, contemptuous, rude

Box of Chemists

My Chemistry Experiment

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

Current practices of 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 emphasise in silico work.

1st Generation Summary

2005 – 2010

Generation 2

• 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.

Reuse, Recycling, Repurposing

“A biologist would rather share their toothbrush than their gene name”

Mike Ashburner and othersProfessor in Dept of Genetics,

University of Cambridge, UK

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

mySpace for scientists!Facebook for scientists!Not Facebook for scientists!

Web 2

Open Repositories

Researchers

Social Network

The experiment that is

Developers

Social Scientists

“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

A probe into researcher behaviour

Open source (BSD) Ruby on Rails app

REST and SPARQL interfaces, Linked Data compliant

Inspiration for: BioCatalogue, MethodBox and SysmoDB

myExperiment currently has 3849 members, 234 groups, 1315 workflows, 349 files and 133 packs

data

method

Results

Logs

Results

Metadata PaperSlides

Feeds into

produces

Included in

produces Published in

produces

Included in

Included in Included in

Published in

Workflow 16

Workflow 13Common pathways

QTLPaul’s PackPaul’s Research

Object

Research Objects enable data-intensive research to be:

1. Replayable – go back and see what happened2. Repeatable – run the experiment again3. Reproducible – independent expt to reproduce4. Reusable – use as part of new experiments5. Repurposeable – reuse the pieces in new expt6. Reliable – robust under automation7. Referenceable – citable and traceable

The Six Rs of Research Object Behaviours

http://blog.openwetware.org/deroure/?p=56

“Scientists and developers journeying together”

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.

2nd Generation Summary

2010 – 2015

Generation 3

4th Paradigm

The Fourth Paradigm: Data-Intensive Scientific DiscoveryPresenting the firstbroad look at the rapidly emerging field of data-intensive sciencehttp://research.microsoft.com/en-us/collaboration/fourthparadigm/

BioEssays, 26(1):99–105, January 2004

Francois Belleau

“…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”1) Taverna provenance exposed as RDF2) myExperiment RDF document for a protein discovery workflow3) Mocked-up BioCatalogue document using myExperiment RDF

data as example4) Provisional RDF documents obtained from the ConceptWiki

(conceptwiki.org) development server5) An RDF document for an example protein, obtained from the RDF

interface of the UniProt web site

A Bioinformatics Experiment Scott Marshall Marco Roos

LifeGuide http://www.lifeguideonline.org/

http://www.galaxyzoo.org/

MethodBox http://www.methodbox.org/

The solutions we'll be delivering in 5 yearsCharacterised 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. Increasing automation and decision-support for the researcher - the VRE becomes assistive. Curation is autonomic and social.

3rd Generation Summary

Easy and low risk to startProgress to advanced skillsFor researchersNo obligationGo as far as you want

Find a service & relax

Intellectual ramps

Malcolm Atkinson

42

NRAO/AUI/NSF

telescopes for the naked mindDatascopesMalcolm Atkinson

From Signal to Understanding

2010 – 2011and beyond

Music and Linked Data

http://www.openarchives.org/ore/terms/aggregates

http://eprints.ecs.soton.ac.uk/id/eprint/20817

EPrints

It’s about enabling the join

Ben Fields, 6th October 2010

SALAMI: Structural Analysis of Large Amounts of Music

Information

David De RoureJ. Stephen Downie

Ichiro Fujinaga

www.diggingintodata.org

Digital Music Collections

Crowdsourced ground truth

Community Software

Linked Data Repositories

Supercomputer

23,000 hours ofrecorded music

250,000 hours NCSASupercomputer time

Music InformationRetrieval Community

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

seasr.org/meandreMeandre

“Signal”Digital Audio

“Ground Truth”

Community

It’s web-like!

Q. If and when should community-generated content be assimilated into managed repositories?

StructuralAnalysis

How country is my country?

www.nema.ecs.soton.ac.uk/countrycountry

Stephen Downie

Music and computational thinking

• Co-*• Ramps• Datascopes• Linked data rocks• Computational thinking• It’s about enabling the join

Take homes

• Co-*• Ramps

• Datascopes• Linked data rocks

• Computational thinking• It’s about enabling the join

david.deroure@oerc.ox.ac.uk

Visit wiki.myexperiment.org

Thanks to: Jeremy Frey & CombeChem; Carole Goble & myGrid; Iain Buchan, Sean Bechhofer and the myExperiment team; Doug Kell; Marco Roos; Stephen Downie, Kevin Page, Ben Fields and the NEMA/SALAMI team; Malcolm Atkinson