eLabs and Research Objects

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e-Labs and Research Objects
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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

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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

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Research Objects

• The common currency for e-Labs
• A story about an investigation
• An aggregation of resources
• With a particular purpose, reason or rationale
  for the aggregation
• Capturing the investigation process from soup to
  nuts
• Intended to be
• Reusable
• Repeatable
• Replayable

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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
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Research Methods Experts
Development e-Lab
Research Objects
Scripts
Data sets
Services
Publications
Workflows
Application e-Lab
Delivery Experts
Policy makers
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Knowledge Burying (Mons)

• Publishing/mining cycle results in loss of
  knowledge
• 40 of information lost
• RIP Rest in Paper
• ROs as a mechanism for publication of knowledge,
  preserving information about the process.

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(Current) RO Principles

• Common Schema for internal strcture
• References metadata rather than Data
• Graceful degradation of understanding
• Not all services understand everything
• cf RDF/OWL
• Reflective
• Clickable
• Displayable
• Mailable

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Anatomy of an RO
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Flavours of RO

• RO as encapsulation of a process
• Up to date references to appropriate resources
• RO as a record of what happened
• Curated, fossilised, immutable aggregation
• RO as collection
• E.g Tutorial materials
• RO as protocol
• General templates that may be specialised for
  specific domains/tasks

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Whats inside?

• A research problem
• A hypothesis
• Experimental design
• Data sets
• Measurements
• Workflows used to analyse data
• Results of data analysis
• Information about ethical approval
• Governance policies

• Publications, e.g. papers, reports, slide-decks
• The investigators involved in the experiment
• References to other SROs that the work depends on
  or cites
• Descriptions of relationships between resources.
• Lilly experiment ontology,
• SWAN/SIOC
• Scholarly discourse
• OBO relations ontology

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RO Lifecycle

• ROs have a lifecycle they may be created,
  manipulated, edited, interrogated and published.
• Appropriate servicessupport this lifecycle

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e-Labs services

• Registry
• Repository
• Workflow Monitoring
• Event Logging
• News feeds, activities
• Social Metadata
• Tagging, groups, users, Sharing
• Annotation
• Search
• Visualisation
• Notification

• Authentication, Authorisation and Role based
  Access
• Job Execution. Workflow engine, HPC scripts etc.
• Naming and Identity Centralised vs. distributed.
• Synchronisation
• To support on-line and off-line working
• Anonymisation
• e.g. for health records
• Text Mining

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e-Labs activity
e-Labs TAG

• Obesity e-Lab (details next)
• myExperiment
• Packs as a precursor to ROs
• Sharing/Social networking services
• Biocatalogue
• Curated collection of bio web services
• LifeGuide
• myExperiment for storing/sharing Internet
  interventions

• NW eHealth
• e-Labs as a sense-making layer on top of NHS
  Information Systems
• ONDEX
• Linking bio data sets
• Sysmo-DB
• Web-based exchange of data
• Shared Genomics
• HPC Infrastructure for analysis of large-scale
  genetic data

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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

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ROs and e-Labs

• Research Objects
• Aggregations of resources (people data
  methods)
• Rationale, purpose, story
• Lifecycle
• Share and Exchange Reuse, Replay, Repeat
• E-Labs
• Collection of services consuming and producing
  Research Objects

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A dream
Problem
E-Lab