1. myExperiment: Towards Research Objects David De Roure
Building Linked Web Communities
in Biomedicine to Accelerate Research

2. What is it?
How it’s being used
How we built it
Towards the e-Laboratory

3. Virtual Learning Environment Peer-Reviewed Journal & Conference Papers
The social process of Science
Virtual Learning Environment
2.0
Undergraduate Students
Digital Libraries
scientists
Graduate Students
Technical Reports
Reprints
Peer-Reviewed Journal & Conference Papers
Preprints & Metadata
experimentation
Local Web
Repositories
Data, Metadata Provenance Workflows Ontologies
Certified Experimental Results & Analyses

4. Sharing pieces of process
Not just collaboration in workflows, but collaborating with sharing workflows
Over 400 taverna workflows publicly available.
Combine different formalisms in one system?
E.g. a dataflow Kahn network and a central- clock based calculus
Kepler logo 4

5. 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

6. Triana Trident Kepler Taverna Ptolemy II BioExtract BPEL
Not just collaboration in workflows, but collaborating with sharing workflows
Over 400 taverna workflows publicly available.
Combine different formalisms in one system?
E.g. a dataflow Kahn network and a central- clock based calculus
Kepler logo
BioExtract 6

7. 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.

8. “Facebook for Scientists” ...but different to Facebook!
A repository of research methods
A community social network
A Virtual Research Environment
Open source (BSD) Ruby on Rails application with HTML, REST and SPARQL interfaces
Project started March 2007
Closed beta since July 2007
Open beta November 2007
myExperiment currently has 1712 registered users, 141 groups, 584 Taverna workflows plus 81 others, and 51 packs
Go to to access publicly available content or create an account

10. myExperiment Features
User Profiles
Groups
Friends
Sharing
Tags
Workflows
Developer interface
Credits and Attributions
Fine control over privacy
Packs
Federation
Enactment
Distinctives

12. Control over sharing The most important aspect of myExperiment
Designed by scientists

13. A Pack Workflow 16 Logs Results Metadata Slides Paper Results
QTL
Logs
Results
A Pack
Metadata
Slides
Paper
Common pathways
Results
Workflow 13

14. For Developers
All the myExperiment services are accessible through simple RESTful programming interfaces
use your existing environment and augment it with myExperiment functionality
build entirely new interfaces and functionality mashups
The Ruby on Rails codebase is open source (BSD) so you can run your own myExperiment – perhaps for your own lab or to develop new funcionality
Go to wiki.myexperiment.org for information about our Developer Community

15. What is it?
How it’s being used
How we built it
Towards the e-Laboratory

18. Adam Belloum

19. SigWin-detector: is a grid-enabled workflow application that takes a sequence of numbers and a series of window sizes as input and detects all significant windows for each window size using a moving median false discovery rate (mmFDR) procedure.
WS-VLAM composer
Human transcriptome map
discovered RIDGE
Human transcriptome map
DNA curvature of the Escherichia Coli chromosome
More details:

20. Carol Lushbough

21. Google Gadgets
Bringing myExperiment to the iGoogle user

22. Taverna Plugin
Bringing myExperiment to the Taverna user

23. Facebook

25. Scientists do share!  Consumers > Curators > Producers
Of the 661 workflows, 531 are publicly visible whereas 502 are publicly downloadable.
3% of the workflows with restricted access are entirely private to the contributor and for the remaining they elected to share with individual users and groups.
69 workflows (over 10%) have been shared, with the owner granting edit permissions to specific users and groups.
In addition there are 52 instances where users have noted that a workflow is based on another workflow on the site.
The most viewed workflow has 1566 views.
There are 50 packs, ranging from tutorial examples to bundles of materials relating to specific experiments.
Scientists do share! 
Consumers > Curators > Producers

26. Analysis Two distinct myExperiment communities:
Considerations in Collaborative Curation:
Supermarket shoppers Workflow consumers prefer larger workflows ready to be downloaded and enacted
Tool builders Workflow authors prefer smaller, modularized workflows which can be assembled & customized
Quality and sufficiency of good documentation
Content decay surveillance
Consumers > curators > producers
Contributor, expert and community curation
Incentives for curation

27. What is it?
How it’s being used
How we built it
Towards the e-Laboratory

29. EPrints DSpace Fedora S3 SRB For Developers ORE FOAF SIOC tags ratings
Managed REST API
facebook
iGoogle
android
XML
ORE
FOAF
SIOC
API config
HTML
RDF Store
SPARQL endpoint
Search Engine
Search API
tags
ratings
reviews
profiles
groups
workflows
credits `
EPrints
DSpace
Fedora S3
SRB
friendships
packs
files
Enactor
Enactor API
mySQL

30. Semantically-Interlinked Online Communities
SPARQL endpoint
PREFIX rdf: <
PREFIX myexp: <
PREFIX sioc: <
select ?friend1 ?friend2 ?acceptedat where {?z rdf:type
< . ?z myexp:has-requester
?x .
?x sioc:name ?friend1 . ?z myexp:has-accepter ?y . ?y sioc:name ?friend2 .
?z myexp:accepted-at ?acceptedat }
All accepted Friendships including accepted-at time
Semantically-Interlinked Online Communities

32. Exporting packs

33. Scientific Discourse Relationships Ontology Specification
Open Provenance Model
Communications of the ACM 51, 4 (Apr. 2008), 52-58

34. Phase 2
Phase 2
Repository integration (institutional: EPrints, Fedora)
Controlled vocabularies
Relationships between items (in and between packs)
Recommendations
Improved search ranking and faceted browsing
Indexing of packs
New contribution types (Meandre, Kepler, e-books)
Further blog / wiki integration
Biocatalogue integration

35. Content Capture and Curation
Reuse and Symbiosis
Content Capture and Curation
Self by Service Providers
Experts
refine
validate
refine
validate
seed
seed
Workflows and Services
refine
validate
refine
validate
seed
seed
In particular a platform for research into curation practices
As in the panel today
Expert – Is library like
Suppliers and crowd are the web side
Automated is
Expert curators: bioinformaticians who understand the services and workflows whose job it is to annotate and set up the curation pipelines, for services and workflows that are not of their own making.
Self-curation: Some registries are closed – the myGrid registry is only curated by experts from the myGrid project itself. Others encourage service developers to self-curate, emphasising the use of plug-ins to service development environments such as Eclipse; examples include BioMoby’s jMoby plugin and SAWSDL4J, Lumina and Radiant toolkits for SAWSDL and WSMO Studio (21). Workflow repositories such as myExperiment rely on self-curation by the workflow developers and community curation by their users. Challenges include (a) the enforcement of controlled vocabularies by self-curators, particularly if the vocabularies are also managed by the developers as they can quickly become unruly and (b) incentivising people to contribute their services and workflows for the good of the community.
Community Curators: The trend is to follow in the footsteps of popular Web 2.0 social computing sites and encourage community curation through user feedback, blogging, e-tracking, recommendations and folksonomy based tagging. Community approach to services development and use being tried by Seekda and BioMoby and for workflows by myExperiment. Community and self-curation requires built-in incentive models for people to contribute such as credit and attribution, but can be made to work for example iCapture successfully pioneered community curation of ontologies (Wilkinson PSB).
Automated Curators: Automated scavengers and crawlers identify candidates for submission and extract as much metadata as possible. Functional metadata is hard to auto-curate, requiring: specialist metadata extraction tools [54]; software plug-ins that incidentally gather metadata from services as they are used in applications; or smart reasoning over seeded service descriptions and workflows [54]. Operational and usage metadata is ripe for automation, generated from monitoring services, application diagnostics, customer reports and Social Network Analysis. Workflow analytics is the term used for processing workflow collections to identify, for example, service co-use patterns and service popularity. Automated curation needs excellent infrastructure.
Social by User Community
Automated

37. Six Principles of Software Design to Empower Scientists
Fit in, Don’t Force Change
Jam today and more jam tomorrow
Just in Time and Just Enough
Act Local, think Global
Enable Users to Add Value
Design for Network Effects
Keep your Friends Close
Embed
Keep Sight of the Bigger Picture
Favours will be in your Favour
Know your users
Expect and Anticipate Change
De Roure, D. and Goble, C. "Software Design for Empowering Scientists," IEEE Software, vol. 26, no. 1, pp , January/February 2009

38. What is it?
How it’s being used
How we built it
Towards the e-Laboratory

39. e-Laboratory Lifecycle
Local projects using Taverna and/or myExperiment
SysMO
Ondex
NEMA
Obesity eLab
Shared Genomics
CombeChem
LifeGuide
IBBRE

40. What is an e-Laboratory?
A laboratory is a facility that provides controlled conditions in which scientific research, experiments and measurements may be performed, offering a work space for researchers.
An e-Laboratory is a set of integrated components that, used together, form a distributed and collaborative space for e-Science, enabling the planning and execution of in silico experiments -- processes that combine data with computational activities to yield experimental results

41. e-Labs An e-Lab consists of:
a community
work objects
generic resources for building and transforming work objects
Sharing infrastructure and content across projects
People
Data
Methods

42. e-Labs + Research Objects
An e-Lab is built from a collection of services, consuming and producing Research Objects
Visualisation
Notification
Annotation etc.
Workbench/
RO driven UI
Service
RO Bus
RO aware
services
Service
Service
Service

43. e-Laboratory Evolution
1st Generation
Current practice of early adoptors of e-Labs tools such as Taverna
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.
Provenance is recorded but not shared and re-used.
Science is accelerated and practice beginning to shift to emphasise in silico work
2nd Generation
Designing and delivering now, e.g. Obesity e-Lab
Experience with Taverna and myExperiment and on our research results arising from these activities
Key characteristic is re-use - of the increasing pool of tools, data and methods across areas/disciplines.
Contain some freestanding, recombinant, reproducible research objects. Provenance analytics plays a role.
New scientific practices are established and opportunities arise for completely new scientific investigations.
3rd Generation
The vision - the e-Labs we'll be delivering in 5 years - illustrated by open science.
Characterised by global reuse of tools, data and methods across any discipline, and surfacing the right levels of complexity for the researcher.
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 e-Laboratory becomes assistive.
Provenance assists design
Curation is autonomic and social

44. Assembling e-Laboratories
Example Core Services
Workflow Monitoring
Event Logging
Social Metadata
Annotation Service
Search, ranking
User Registration
Distributed Data Query
Job Execution Naming and Identity
Anonimisation
Text Mining
Research Object Management
Probity
Coreference Resolution
An e-Lab is a set of components and resources
An open system, not a software monolith
Utility of components transcends their immediate application
We envisage an ecosystem of cooperating e-Laboratories
What are the e-Lab components and services?
What are the Research Objects?

45. Workflow 16 Results Logs Metadata Slides Paper Results Workflow 13
Paul Fisher
Workflow 16
QTL
Results
Logs
produces
Included in
Published in
Included in
Feeds into
produces
Included in
Included in
Metadata
Slides
Paper
produces
Published in
Common pathways
Results
Workflow 13

46. David Shotton

47. Anatomy of a Research Object

48. SWAN-SIOC
Experiments
myExperiment
Tim Clark

49. Characteristics of a Research Object
Composite. Contain typed interrelationships and dependencies between resources but are in turn labelled and identifiable as an individual resource.
Distributed. Structured collections of references to locally managed and externally located resources. Implications for reliability, consistency, mixed stewardship, versioning and identity resolution.
Annotated. Carry metadata concerning provenance profile, lifecycle profile, sharing profile (permissions, licensing, downloads, views), curation profile (tags, comments, ratings) and usage profile.
Repeatable. Capture information about the lifecycle of the investigation facilitating experiments to be repeatable (without change), reusable (with reconfiguration), replayable and/or repurposable (as new components or templates).
Interoperable. Publishable and exchangeable units that facilitate interoperability; OAI-ORE standards increase interoperability and facilitate the consumption of Research Objects in between applications.

50. Thoughts
myExperiment provides social infrastructure – it facilitates sharing and enables scientists to “collaborate in order to compete”
myExperiment has growing community and growing content
New content types: meandre, kepler, R, matlab, ..., spreadsheets? SPARQL queries?
We are targetting how we believe research will be conducted in the future, through the assembly of e-Laboratories which share Research Objects
SPARQL endpoint is an effective alternative to the API – provides any service you want!
Workflows for Semantic Web scripting?

51. Simon Coles, Paul Fisher, Adam Belloum, Sean Bechhofer, David Shotton
Contact
David De Roure
Carole Goble
Slide Credits
Simon Coles, Paul Fisher, Adam Belloum, Sean Bechhofer, David Shotton