e-Labs and Research Objects

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

• An e-Lab consists of:

1. a community;

2. work objects;

3. generic resources for building and transforming work objects.

• Sharing infrastructure and content across projects

e-Labs

PeoplePeople DataData MethodsMethods

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

e-Labs + Research Objects

• An e-Lab is built from a collection of services, consuming and producing Research Objects

RO Bus

Service Service Service

Workbench/RO driven UI

RO awareservices

Service

VisualisationNotification

Annotation etc.

Research ObjectsResearch Objects

Workflows

Data sets

Services

Scripts

Publications

Development e-Lab

Application e-Lab

ResearchMethodsExperts

Policy makers

Delivery Experts

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.

Experiment

Paper

Knowledge

Publication Text Mining

(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

Anatomy of an RO

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

What’s 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

RO Lifecycle

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

• Appropriate servicessupport this lifecycle

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

e-Labs activity

• 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

e-Labs TAG

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

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

A dream…

http://www.flickr.com/photos/fatdeeman/2879894

ProblemProblem

E-LabE-Lab