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Realising%20the%20scholarly%20knowledge%20cycle%20-%20eBank%20UK

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Dr Liz Lyon, UKOLN, University of Bath, UK. CNI Task Force Meeting Spring 2004 ... OSCIR', 'VLA/NSF/Eric Perlman (UMBC)/Fang Zhou, Biretta (STScI)/F Owen (NRA) ... – PowerPoint PPT presentation

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Title: Realising%20the%20scholarly%20knowledge%20cycle%20-%20eBank%20UK


1
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,
UKOLN is supported by
www.bath.ac.uk
www.ukoln.ac.uk
a centre of expertise in digital information
management
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

3
Setting the scene
4
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.

5
Report of the National Science Foundation
Blue-Ribbon Advisory Panel on Cyberinfrastructure
2003 http//www.cise.nsf.gov/sci/reports/toc.c
fm
6
Report of the National Science Foundation
Blue-Ribbon Advisory Panel on Cyberinfrastructure
2003 http//www.cise.nsf.gov/sci/reports/toc.cfm

7
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 Universe http//www.astrog
rid.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)



10
e-Research the trends?
  • Increasingly dataintensive, 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

11
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.)

12
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

13
The scholarly knowledge cycle
14
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Aggregator services national, commercial
Data analysis, transformation, mining, modelling
Harvestingmetadata
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Deposit / self-archiving
Validation
Validation
Publication
Linking
Peer-reviewed publications journals, conference
proceedings
Data curation databases databanks
15
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Aggregator services national, commercial
Data analysis, transformation, mining, modelling
Harvestingmetadata
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Deposit / self-archiving
Validation
Validation
Publication
Linking
Peer-reviewed publications journals, conference
proceedings
Data curation databases databanks
16
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Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Aggregator services national, commercial
Data analysis, transformation, mining, modelling
Harvestingmetadata
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Deposit / self-archiving
Validation
Validation
Publication
Linking
Peer-reviewed publications journals, conference
proceedings
Data curation databases databanks
23
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Aggregator services national, commercial
Learning object creation, re-use
Harvestingmetadata
Learning Teaching workflows
Repositories institutional,
e-prints, subject, data, learning objects
Institutional presentation services portals,
Learning Management Systems, u/g, p/g courses,
modules
Deposit / self-archiving
Validation
Resource discovery, linking, embedding
Validation
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
24
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Aggregator services national, commercial
Data analysis, transformation, mining, modelling
Learning object creation, re-use
Harvestingmetadata
Learning Teaching workflows
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Institutional presentation services portals,
Learning Management Systems, u/g, p/g courses,
modules
Deposit / self-archiving
Deposit / self-archiving
Validation
Validation
Publication
Resource discovery, linking, embedding
Validation
Linking
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
Data curation databases databanks
25
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Data analysis, transformation, mining, modelling
Learning object creation, re-use
Aggregator services eBank UK
Harvestingmetadata
Learning Teaching workflows
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Institutional presentation services portals,
Learning Management Systems, u/g, p/g courses,
modules
Deposit / self-archiving
Deposit / self-archiving
Validation
Validation
Publication
Resource discovery, linking, embedding
Validation
Linking
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
Data curation databases databanks
26
The eBank UK Project
27
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/

28
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

29
Key Deliverables
  • Requirements specification
  • Pilot service
  • Two supporting studies
  • Provenance review of current research
  • Feasibility report on dataset description and
    schema
  • Consultative evaluation workshop and report
  • Recommendations for future work

30
Pilot service technical architecture
Diagram by Andy Powell, UKOLN
31
Comb-e-Chem Project
Video
Simulation
Properties
Analysis
Structures Database
Diffractometer
X-Ray e-Lab
Properties e-Lab
Grid Middleware
32
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

35
Searching, linking and embedding
Dataset
Dataset
Dataset
dctermsreferences
Crystal structure (data holding)
ePrint UK aggregator service
Harvesting OAI-PMH oai_dc
Linking
Searching, linking and embedding
Harvesting OAI-PMH ebank_dc
ebank_dc record (XML)
Deposit
PSIgate portal
dctypeCrystalStructure and/or Collection
eBank UK aggregator service
Institutional repository
dcidentifier
Crystal structure report (HTML)
dctermsisReferencedBy
Harvesting OAI-PMH oai_dc
Eprint oai_dc record (XML)
dctypeEprint and/or Text
Subject service
Searching, linking and embedding
Model input Andy Powell, UKOLN.
36
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

37
Challenges for the future
38
What next? The metadata schemasome 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-20
    02001.pdf
  • Investigate packaging options
  • METS
  • MPEG 21 DIDL
  • ??
  • Expand to include SMART e-Lab metadata
    e.g. sample preparation

39
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

40
Presentation services subject, media-specific,
data, commercial portals
Searching , harvesting, embedding
Resource discovery, linking, embedding
Resource discovery, linking, embedding
Data creation / capture / gathering laboratory
experiments, Grids, fieldwork, surveys, media
Data analysis, transformation, mining, modelling
Learning object creation, re-use
Aggregator services eBank UK
Harvestingmetadata
Learning Teaching workflows
Research e-Science workflows
Repositories institutional,
e-prints, subject, data, learning objects
Institutional presentation services portals,
Learning Management Systems, u/g, p/g courses,
modules
Deposit / self-archiving
Deposit / self-archiving
Validation
Validation
Publication
Resource discovery, linking, embedding
Validation
Linking
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
Data curation databases databanks
41
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?..
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