Enhancing sccess to resesrch dsts: the crystallogrsphy chsllenge

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Enhancing access to research data the e-Science
project eBank UK
UKOLN is supported by
2005-09-01 www.ukoln.ac.uk
www.bath.ac.uk
A centre of expertise in digital information
management
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Enhancing access to research data overview

• E-Science impact of digital technologies on
  research process
• Scholarly knowledge cycle and publication
  bottleneck
• eBank project applying digital library
  techniques to support data curation in
  crystallography
• Services, metadata, issues phase 3

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Changes in research process

• Increasing data volumes from eScience /
  Grid-enabled / cyber-infrastructure applications,
  big science, data-driven science
• Changing research methods high througput
  technologies, automation, smart labs
• Potential for re-use of data, new
  inter-disciplinary research
• Different types of data observational data,
  experimental data, computational data different
  stewardship and long-term access requirements

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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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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 or not Open Access to data?
• 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
• Relationship between institutional and subject
  rs
• Build robust services

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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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The data deluge crystallography
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Data overload the publication bottleneck
2,000,000
25,000,000
300,000
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Current Publishing Process

• Journal articles aims, ideas, context,
  conclusions only most significant data
• Raw underlying data required by peers not
  readily available

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Context existing data repositories

• National data archives
• UK Data Archive, Arts and Humanities Data
  Service, US National Archives and Records
  Administration (NARA), Atlas Datastore
• Discipline specific archives
• GenBank, Protein Data Bank
• Crystallography archives
• Cambridge Crystallographic Data Centre (Cambridge
  Structural Database) , Indiana University
  Molecular Structure Center (Crystal Data Server,
  Reciprocal Net), FIZ Karlsruhe (Inorganic
  crystals), Toth Information Systems (CHRYSTMET)
• Journals require deposit of data to support
  articles
• Typically deposit of summary data. partial
  coverage

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eBank UK project overview

• JISC funded in 2003, now in Phase 2 to 2006
• Joint effort between crystallographers, computer
  scientists, digital library researchers
• Investigating contribution of existing digital
  library technologies to enable publication at
  source
• Partners have interest in dissemination of
  chemistry research data, open access, OAI,
  institutional repositories http//www.ukoln.ac.uk/
  projects/ebank-uk/

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eBank project team

• University of Bath, UKOLN (lead)
• Monica Duke, Rachel Heery, Traugott Koch, Liz
  Lyon,
• University of Southampton, School of Chemistry
• Simon Coles, Jeremy Frey, Mike Hursthouse
• University of Southampton, School of Electronics
  and Computer Science
• Leslie Carr, Chris Gutteridge
• University of Manchester, PSIgate (physical
  sciences portal in RDN)
• John Blunden-Ellis

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eBank phase one achievements

• Gathered requirements from crystallographers
• Established pilot institutional repository for
  crystallography data at Southampton with web
  interface
• Developed a demonstrator aggregator service at
  UKOLN (CCDC exploring aggregation service)
• Developed appropriate schema
• Demonstrated a search interface as an embedded
  service at PSIgate portal
• Demonstrated an added value service linking
  research data to papers (one-off)

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Institutional repositoriespublication at source

• Institution establishes repository(s)
• Institution pro-actively supports deposit process
• OAI provides basis for interoperability
• Potential for added value services
• And/Or .international subject based archives?

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Crystallography good fit.

• Crystallography has well defined data creation
  workflow
• Tradition of sharing using standard file format
• Crystallography Information File (CIF)
• What about other chemistry sub-disciplines? other
  scientific disciplines?

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eBank UK 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 at Southampton

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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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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 (Crystallographic Information
  File)
• Validation chemical crystallographic checks

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Data Collection
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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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Southampton digital repository
http//ecrystals.chem.soton.ac.uk
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Access to ALL underlying data
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Harvesting OAIster
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OAI-PMH harvesting and aggregating
eBank aggregator at UKOLN http//eprints-uk.rdn.ac
.uk/ebank-demo/
Demonstrating potential for linking between data
and journal article
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Embedded search service at PSIgate
PSIgate subject gateway service provider
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Schema for records made available for harvesting

• Data holding (collection of files associated with
  experiment)
• Qualified Dublin Core data elements plus
  additional chemical properties
• Chemical formula
• International Chemical Identifier (InChI)
• Compound Class
• Individual data files
• Separate records for stage status of each file
• Description set wrapped into one XML record using
  METS
• Research metadata/data as a complex object

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Dataset
eBank data model
Dataset
Dataset
dctermsreferences
Harvesting OAI-PMH oai_dc
Crystal structure (data holding)
ePrint UK aggregator service
Linking
dctypeCrystalStructure
Harvesting OAI-PMH ebank_dc
ebank_dc record (XML)
Deposit
eBank UK aggregator service
dcidentifier
Institutional repositories
dctermsisReferencedBy
Crystal structure report (HTML)
Deposit
Harvesting OAI-PMH oai_dc,ebank_dc
Eprint jump-off page (HTML)
dcidentifier
Eprint manifestation (e.g. PDF)
Eprint oai_dc record (XML)
Other aggregators and services
dctypeEprint and/or Text
Linking
Model input Andy Powell, UKOLN.
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Creating the metadata

• Potential to embed deposit and disseminate into
  workflow of chemist in automated way

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eBank phase two work areas

• Sub-disciplines of chemistry, earth sciences,
  engineering
• Pursue generic data model
• Use of identifiers for citing datasets
• Subject approach to discovering research data
  (keywords, classification, ontology)
• Access to research data in teaching and learning
  context
• Liaise with other digital repository initiatives

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Related UK projects

• National e-Science Centre NESC
• NERC Data Grid (Athmospheric and Oceanographic
  Data Centres)
• JISC Digital Repositories Programme
• - Spectra (experim. chemistry, high volume
  ingestion)
• - R4L (lab equipment, metadata
  generation)
• - CLADDIER (citation, identifiers, linking)
• - StORe (data and publ. repository links)
• - GRADE (reuse of geospatial data)

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2. Issues generic data models, metadata schema
terminology

• Validation against generic 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 related publications
• Meaningful resource discovery?

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3. Issues linking

• 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 identifiers

• Identifiers and persistence
• generic DOI, PURL, Handle, ARK
• domain International Chemical Identifier (InChI)
• Resolution lookup
• Resource discovery Google Scholar?
• Granularity (metadata, linking)?
• Provenance authenticity, authority, integrity?

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5. 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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For the future

• Who provides added value services?
• Authority files, automated subject indexing,
  annotation, data mining, visualisation
• What are the preservation issues?
• UK Digital Curation Centre http//www.dcc.ac.uk
• National Science Board Draft report on long-lived
  data collections http//www.nsf.gov/nsb/meetings/2
  005/LLDDC_draftreport.pdf
• How to manage complex objects descriptions within
  OAI ?
• Digital curation of research data presents new
  roles for scientists, computer scientists, data
  managers.

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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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6. 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

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Project homepagehttp//www.ukoln.ac.uk/projects/
ebank-uk/Duke, M. et al Enhancing access to
research data the challenge of crystallography.
JCDL 2005.http//www.ukoln.ac.uk/projects/ebank-u
k/dissemination/jcdl2005/preprint.pdfAcknowledge
ment to all project partners for their
contributions to this presentation.