e-Labs and Research Objects

1. e-Labs and Research Objects

2. 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

3. People Data Methods 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

4. 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

5. 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

6. Research Methods Experts Development e-Lab Research Objects Scripts Data sets Services Publications Workflows Application e-Lab Delivery Experts Policy makers

7. Knowledge Experiment Publication Text Mining Paper 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.

8. (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

9. Anatomy of an RO

10. 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

11. 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 What’s inside?

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

13. 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 services

14. 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 activity e-Labs TAG

15. 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

16. 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

17. http://www.flickr.com/photos/fatdeeman/2879894 A dream… Problem E-Lab