JISCSURFCNImtgmay05

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From research data to new knowledge a lifecycle
approach. Dr Liz Lyon, Director UKOLN,
University of Bath, UK JISC/SURF/CNI Conference
May 2005, Amsterdam.
UKOLN is supported by
www.bath.ac.uk
www.ukoln.ac.uk
a centre of expertise in digital information
management
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Overview

• Scholarly communications in flux
• e-Research and the diversity of data
• Repositories meta-functionality
• Realising the link to learning eBank UK
• Providing value-added services
• Enabling knowledge extraction post-processing
• Look at (some of) the issues en route

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1. Scholarly communications in flux
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A medieval scriptorium..
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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
The scholarly knowledge cycle. Liz Lyon, Ariadne,
July 2003.
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
Publication
Peer-reviewed publications journals, conference
proceedings
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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
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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
Publication
Resource discovery, linking, embedding
Validation
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
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2. e-Research and the diversity of data
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Assuring permanent open access to the records of
science the humanities?

• Long term access to primary data
• Increasing data volumes from eScience and
  Grid-enabled / cyberinfrastructure applications
• Changing research paradigm data-driven science,
  big science
• Observational data, simulations, large-scale
  experimentation, computations
• Multi-media resources, statistical data,
  surveys, geo-spatial data

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Diversity of data collections

• Very large, relatively homogeneous
  Large-scale Hadron Collider (LHC)
  outputs from CERN
• Smaller, heterogeneous and richer collections
  World Data Centre for Solar-terrestrial
  Physics CCLRC
• Small-scale laboratory results
  jumping robots project at the
  University of Bath
• Population survey data UK Biobank
• Highly sensitive, personal data patient care
  records

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Taxonomy of data collections

• Research collections jumping robots
• Community collections Flybase at Indiana (with
  UC Berkeley )
• Reference collections Protein Data Bank
• Source NSF Long-Lived Digital Data Collections
• Draft report March 2005

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Taxonomy of data collections
Evolution

• Research collections jumping robots
• Community collections Flybase at Indiana (with
  UC Berkeley )
• Reference collections Protein Data Bank
• Source NSF Long-Lived Digital Data Collections
• Draft report March 2005

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Repository evolution 1971 Research collection
lt12 files 2005 Reference collection gt2700
structures deposited in 6 months
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1. Issues research data as content

• Sharing it!
• Data diversity
• Homo- or heterogeneous
• Raw and derived / processed
• Sensitivity
• Fast or slow growth in volume
• Repository evolution
• Likelihood to scale up (from bytes to petabytes)
• Quality assurance (from the start)
• Community-based standards development
  (folksonomies)
• Build robust services

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3. Repositories meta-functionality
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eBank UK linking research data to learning

• JISC-funded September 2003, Phase 2 February
  2005
• UKOLN at the University of Bath (lead),
  University of Southampton, University of
  Manchester
• Exemplar e-Science testbed Combechem
• Grid-enabled combinatorial chemistry
• Crystallography, laser and surface chemistry
  examples
• 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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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
Publication
Resource discovery, linking, embedding
Validation
Peer-reviewed publications journals, conference
proceedings
Quality assurance bodies
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Data Flow in eBank UK
Create
OAI-PMH
Index and Search
Institutional repository
eBank aggregator
Data files
Metadata
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Comb-e-Chem Project
Video
Simulation
Properties
Analysis
StructuresDatabase
Diffractometer
X-Raye-Lab
Propertiese-Lab
Grid Middleware
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The digital repository
ecrystals.chem.soton.ac.uk Acknowledgement Simon
Coles
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Access to the underlying data
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Harvesting OAIster
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Aggregating search discover
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Linking to publications
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eBank embedded in a science portal
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eBank Phase 2 linking to learning

• Embedding in e-Learning processes
• Evaluating the pedagogical benefits
• MChem course
• Chemical informatics course

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2. Issues generic data models, metadata schema
terminology

• Validation against other schema
• CCLRC Scientific Data Model Vs 2
  
• Complex digital objects and packaging options
• METS
• MPEG 21 DIDL
• Terminologies
• Domain crystallography
• Inter-disciplinary e.g. biomaterials
• Metadata enhancement subject keyword additions
  to datasets based on knowledge of keywords in
  related publications
• Meaningful resource discovery?

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3. Issues linking and identifiers

• Links to individual datasets within an experiment
• Links to all datasets associated with an
  experiment or a data collection
• Links to derived eprints and published literature
  
• Context sensitive linking find me
• Datasets by this author / creator
• Datasets related to this subject
• Learning objects by this author / creator
• Learning objects related to this subject
• Identifiers and persistence
• generic
• domain International Chemical Identifier (InChI
  code)
• Resource discovery Google Scholar?
• Provenance authenticity, authority, integrity?

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4. Issues embedding and workflow

• Into the crystallographic publishing community
  International Union of Crystallography
• Into the chemistry research workflow
• SMART TEA Digital Lab Book e-synthesis Lab
• Other analytical techniques and instrumentation
• Into the curriculum and e-Learning workflows
• MChem course
• Undergraduate Chemical Informatics courses

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Repositories and digital curation
For later use? In use now (and the future)?
Static
Dynamic
Data preservation
Data curation
maintaining and adding value to a trusted body
of digital information for current and future use
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Provide value-added services

• Annotation
• e-Lab books (Smart Tea Project in chemistry)
• Gene and protein sequences

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Enable post-processing and knowledge extraction

• The acquisition of newly-derived information and
  knowledge from repository content
• Run complex algorithms over primary datasets
• Mining (data, text, structures)
• Modelling (economic, climate, mathematical,
  biological)
• Analysis (statistical, lexical, pattern
  matching, gene)
• Presentation (visualisation, rendering)

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5. Issues knowledge services

• Layered over repositories
• Annotation
• Mining, modelling, analysis
• Visualisation
• Across multiple repositories
• Grid enabled applications
• Highly distributed, dynamic and collaborative
• Associated with curatorial responsibility
• UK Digital Curation Centre http//www.dcc.ac.uk

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Issues summary

• Research data is diverse, increasing rapidly in
  volume and complexity
• Repository collections are dynamic and evolve
• Technical challenges associated with
  interoperability, persistence, provenance,
  resource discovery and infrastructure provision
• Embedding in workflow is critical scholarly
  communications, research practice, learning
• Knowledge extraction tools will generate new
  discoveries based on repository content
• Repository solutions must scale M2M processing
  will become the norm