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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 e-Labs • An e-Lab consists of: 1. a community; 2. work objects; 3. generic resources for building and transforming work objects. People • Data Methods Sharing infrastructure and content across projects 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 Visualisation Notification Annotation etc. Workbench/ RO driven UI Service RO Bus Service Service Service RO aware services Research Methods Experts Development e-Lab Scripts Data sets Research Objects Services Publications Workflows Application e-Lab Delivery Experts Policy makers Knowledge Burying (Mons) Knowledge Experiment Publication Text Mining Paper • 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. (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 services support 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 offline working • Anonymisation – e.g. for health records • Text Mining 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 Evolution 1st Generation •Current practice of early adoptors of e-Labs tools 2nd Generation such as Taverna •Designing and delivering now, e.g. Obesity e-Lab 3rd Generation •Characterised by researchers using tools within with -Taverna andsome myExperiment •The problem vision the e-Labs we'll be delivering inon 5 years their •Experience particular area, with re-use of and research results arising from these activities - and illustrated by open science. tools,our data methods within the discipline. •Key •Characterised characteristic re-use - of theby increasing pooland by global reuse of tools, data •Traditional publishing isissupplemented methods across any discipline, and surfacing the of tools, and methods across publication ofdata some digital artefacts likeareas/disciplines. workflows rightsome levelsfreestanding, of complexityrecombinant, for the researcher. •Contain and links to data. •Key characteristic is radical sharing analytics reproducible research objects. Provenance •Provenance is recorded but not shared and re-used. is significantly data driven - plundering the plays•Research a role. •Science is accelerated and practice backlog of data, results andbeginning methods.to scientificinpractices are established and shift •New to emphasise silico work •Increasing automation and decision-support opportunities arise for completely new scientific for the researcher - the e-Laboratory becomes assistive. investigations. •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… Problem http://www.flickr.com/photos/fatdeeman/2879894 E-Lab