UKOLN is supported by: Realising the scholarly knowledge cycle: The experience of eBank UK Dr Liz Lyon, UKOLN, University of Bath, UK CNI Task Force Meeting

UKOLN is supported by:

Realising the scholarly knowledge cycle:

The experience of eBank UK

Dr Liz Lyon, UKOLN, University of Bath, UK

CNI Task Force Meeting Spring 2004

Alexandria, Virginia,

www.bath.ac.uk

a centre of expertise in digital information management

www.ukoln.ac.uk

CNI Spring 2004 2

Overview

• Setting the scene– e-Research trends– Towards a common infrastructure

• The scholarly knowledge cycle– Data, information and workflows– Provenance

• eBank UK Project– The experience so far– Issues arising

• Challenges for the future

Setting the scene

“The next generation of research breakthroughs will rely upon new ways of handling the immense amounts of data that are being produced by modern research methods and equipment, such as telescopes, particle accelerators, genome sequencers and biological imagers….Similar developments are having an impact in the arts and humanities, and in the social sciences.”

A Vision for Research,

Research Councils UK, December 2003.

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Report of the National Science Foundation

Blue-Ribbon Advisory Panel on Cyberinfrastructure

2003

http://www.cise.nsf.gov/sci/reports/toc.cfm

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Report of the National Science Foundation

Blue-Ribbon Advisory Panel on Cyberinfrastructure

2003

http://www.cise.nsf.gov/sci/reports/toc.cfm

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UK e-Science Programme

“e-Science is about global collaboration in key areas of science and the next generation of

infrastructure that will enable it.”

John Taylor, Director General, Research Councils, UK

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Powering the Virtual Universehttp://www.astrogrid.org(Edinburgh, Belfast, Cambridge, Leicester, London, Manchester, RAL)

AstroGrid will provide advanced, Grid based, federation and data mining tools to facilitate better and faster scientific output.

Picture credits: “NASA / Chandra X-ray Observatory / Herman Marshall (MIT)”, “NASA/HST/Eric Perlman (UMBC), “Gemini Observatory/OSCIR”, “VLA/NSF/Eric Perlman (UMBC)/Fang Zhou, Biretta (STScI)/F Owen (NRA)”

:

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e-Research: the trends?

• Increasingly data–intensive, quantitative• Open access to data and information

– OECD Declaration January 2004

• Implementing new science• Inter-disciplinary • New disciplines e.g. Astro-informatics• New skills requirements

– IT + statistics + domain

• Collaborative – virtual / transient – communities / organisations

• Highly distributed resources

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New resources…….used in new ways

• Primary / original data – Observational, experimental, numeric, genomic, 2/3D molecular

structures, satellite images, electron micrographs, wave spectra, CAD, musical compositions, VR, performances, animations

• Data and information– Creation, discovery, gathering, aggregation, dis-aggregation, replication,

federation, manipulation, transformation, linking, annotation, editing/versioning, validation, (self-)archiving, deposit, publication, curation

• Knowledge extraction and management– Analysis (textual, musical, statistical, mathematical, visual, chemical,

gene……)– Mining (text, data, structures……)– Modelling (economic, mathematical, biological..)– Simulation (molecular, physical, environmental, games…)– Presentation (visualisation, rendering….)

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Towards a common infrastructure

• UK e-Science Programme & JISC Development• e-Science Phase 2 2003 – 2006

– A National e-Science Centre linked to a network of Regional Grid Centres

– An Open Middleware Infrastructure Institute (OMII) based on common standards (Web Services)

• JISC Information Environment– Technical architecture based on open standards (Web Services,

OAI-PMH, Z39.50, RSS…..) http://www.ukoln.ac.uk/distributed-systems/jisc-ie/arch/

– A Digital Curation Centre (DCC) http://www.dcc.ac.uk/ – Virtual Research Environments?

• A changing landscape of scholarly communications

The scholarly knowledge cycle

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Research & e-Science workflows

Aggregator services: national, commercial

Repositories : institutional, e-prints, subject, data, learning objects

Data curation: databases & databanks

Validation

Harvestingmetadata

Data creation / capture / gathering: laboratory experiments, Grids, fieldwork, surveys, media

Deposit / self-archiving

Peer-reviewed publications: journals, conference proceedings

Publication

Validation

Data analysis, transformation, mining, modelling

Searching , harvesting, embedding

Presentation services: subject, media-specific, data, commercial portals

Resource discovery, linking, embedding

Linking

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Validation

Harvestingmetadata




Publication

Validation





Linking

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Validation

Harvestingmetadata




Publication

Validation





Linking

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Learning & Teaching workflows



Institutional presentation services: portals, Learning Management Systems, u/g, p/g courses, modules

Harvestingmetadata



Validation



Learning object creation, re-use


Quality assurance bodies

Validation


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Validation

Harvestingmetadata





Publication

Validation







Validation



Linking

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Aggregator services:

eBank UK




Validation

Harvestingmetadata





Publication

Validation







Validation



Linking

The eBank UK Project

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eBank UK project

• JISC-funded for 1 year from September 2003• UKOLN (lead), University of Southampton, University of

Manchester• “Building the links between research data, scholarly

communication and learning”• e-Science testbed Combechem

– Grid-enabled combinatorial chemistry– Crystallography, laser and surface chemistry– Development of an e-Lab using pervasive computing technology– National Crystallography Service

• Resource Discovery Network PSIgate physical sciences portal• http://www.ukoln.ac.uk/projects/ebank-uk/

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The project team

• UKOLN• Michael Day• Monica Duke• Rachel Heery• Liz Lyon• +• Andy Powell

• Southampton• Les Carr• Simon Coles• Jeremy Frey• Chris Gutteridge• Mike Hursthouse

• Manchester• John Blunden-Ellis

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Key Deliverables

1. Requirements specification

2. Pilot service

3. Two supporting studies:– Provenance: review of current research– Feasibility report on dataset description and

schema

4. Consultative evaluation workshop and report

5. Recommendations for future work

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Diagram by Andy Powell, UKOLN

Pilot service – technical architecture

Comb-e-Chem Project

X-Raye-Lab

Analysis

Properties

Propertiese-Lab

SimulationVideo

Diff

ract

omet

er

Grid Middleware

StructuresDatabase

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Crystallography workflow

• Initialisation: mount new sample on diffractometer & set up data collection

• Collection: collect data• Processing: process and correct images• Solution: solve structures• Refinement: refine structure• CIF: produce CIF• Report: generate Crystal Structure Report

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First steps: establishing common ground…

• Understand the data creation process • Terminology and definitions

– Data– Metadata– Datafile– Dataset– Data holding

• Different views– Digital library researchers, computer scientists, chemists– Generic vs specific– Modeller vs practitioner

• Aim for a common ontology• Modelling the domain• Creating a metadata schema

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ebank_dc record (XML)

Crystal structure (data holding)

Crystal structure report (HTML)

Dataset

Dataset

Institutional repository

eBank UK aggregator service

ePrint UK aggregator service

Subject service

DepositHarvesting OAI-PMH

ebank_dc

Harvesting OAI-PMH oai_dc

Harvesting OAI-PMH oai_dc

Searching, linking and embedding



Dataset

dc:identifier

dcterms:references

Linking

dc:type=“CrystalStructure” and/or “Collection”

Model input Andy Powell, UKOLN.

PSIgate portal

Eprint oai_dc record (XML)

dcterms:isReferencedBy

dc:type=“Eprint” and/or ”Text”

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Where are we now?

• Version 1.0 eBank metadata schema• Pilot eBank repository for harvesting• Exports records as ebank_dc and oai_dc• Validation of schema

– Against harvesting and searching– Against user requirements– Against other schema

• Concept of a collection and a Collection Level Description

• Implementing the pilot service

Challenges for the future

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What next?The metadata schema…some issues• Reduce to its simplest form or reflect the complexity?• ebank_dc versus oai_dc• Compatibility with other schema

– CLRC Scientific Metadata Model vs 1.0 2001 (under revision) http://www-dienst.rl.ac.uk/library/2002/tr/dltr-2002001.pdf

• Investigate packaging options – METS– MPEG 21 DIDL – ??

• Expand to include SMART e-Lab metadata e.g. sample preparation

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…and also….• Investigate identifiers e.g. International Chemical Identifier• Metadata enhancement - subject keyword additions to

datasets based on knowledge of keywords in related publications

• Develop search interface – embedding eBank UK• Testing with PSIgate physical sciences portal• Explore context sensitive linking: find me

– Datasets by this person– Journal articles by this person– Datasets related to this subject– Journal articles on this subject– Learning objects by this person– Learning objects on this subject

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Aggregator services: eBank UK




Validation

Harvestingmetadata





Publication

Validation







Validation



Linking

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Potential longer term impact

1. Track data, information and workflows in e-research and scholarly communications – knowledge audit??

2. Validate the accuracy and authenticity of derived works – ideas audit??

3. Facilitate explicit referencing and acknowledgment of original contributors – intellectual integrity??

4. Raise standards associated with publication of research outputs – academic publishing rigour??

5. Implement open access to and dissemination of data and information – enhance the research process??

6. Give students links to original data underpinning published works – enhance the learning process??

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Thank you.

Questions?…..

Documents

UKOLN is supported by: Realising the scholarly knowledge cycle: The experience of eBank UK Dr Liz Lyon, UKOLN, University of Bath, UK CNI Task Force Meeting