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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
A centre of expertise in digital information management
www.bath.ac.uk
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
Changes in research process
• Increasing data volumes from eScience / Gridenabled / 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
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
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
Repository
evolution:
1971 Research
collection
<12 files
2005 Reference
collection
>2700 structures
deposited in 6
months
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 r’s
Build robust services
Presentation services: subject, media-specific, data, commercial portals
Data creation /
capture /
gathering:
laboratory
experiments,
Grids,
fieldwork,
surveys, media
Resource
discovery, linking,
embedding
Data analysis,
transformation,
mining, modelling
Searching ,
harvesting,
embedding
Aggregator
services: national,
commercial
Resource
discovery,
linking,
embedding
Learning object
creation, re-use
Harvesting
metadata
Research &
e-Science
workflows
Deposit / selfarchiving
Learning &
Teaching
workflows
Repositories :
institutional,
e-prints, subject,
data, learning objects
Validation
Publication
Deposit / selfarchiving
Institutional
presentation
services: portals,
Learning
Management
Systems, u/g, p/g
courses, modules
Resource
discovery, linking,
embedding
Peer-reviewed
publications: journals,
conference proceedings
Validation
Quality
assurance
bodies
The data deluge: crystallography
Data
Overload!
EPSRC National
Crystallography
Service
How do we
disseminate?
Data overload & the publication bottleneck
2,000,000
Cl
Cl
N
O
O
Cl
Cl
+
Cl
N O
OCl
O
Cl
Cl
Cl
Cl
N
Cl
O
O
+
N O
Cl
O
N
Cl
O
N
25,000,000
300,000
Current Publishing Process
• Journal articles: aims,
ideas, context, conclusions –
only most significant data
• Raw & underlying data
required by peers not readily
available
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
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/
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
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)
Institutional repositories…publication 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?
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 subdisciplines? other scientific disciplines?
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
Comb-e-Chem Project
Video
Simulation
Diffractometer
Properties
Analysis
Structures
Database
X-Ray
e-Lab
Properties
e-Lab
Grid Middleware
Crystallography workflow
RAW DATA
DERIVED DATA
RESULTS DATA
• 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
Data Collection
Setup via GUI
Sample Tray
BruNo Unmount
BruNo Mount
PreScans
Diffraction
Yes
Unit Cell
Success
Yes
Strategy
Data Collection
Data Process
System Y
No
No
Data Flow in eBank UK
Create
HTML
Submit
OAI-PMH
present
Store/link
Index
and
Search
Harvest
(XML)
Institutional
repository
eBank
aggregator
HTML
present
Data files
Metadata
Southampton digital repository
http://ecrystals.chem.soton.ac.uk
Access to ALL underlying data
Harvesting: OAIster
OAI-PMH: harvesting and aggregating
eBank aggregator at
UKOLN
http://eprints-uk.rdn.ac.uk/ebankdemo/
Demonstrating
potential for linking
between data and
journal article
Embedded search service at PSIgate
PSIgate subject
gateway:
service provider
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
Dataset
eBank data model
Dataset
Dataset
dcterms:references
Harvesting
OAI-PMH
oai_dc
Crystal structure
(data holding)
Linking
ePrint UK
aggregator
service
dc:type=“CrystalStructure”
ebank_dc
record (XML)
Harvesting
OAI-PMH
Deposit
ebank_dc
dc:identifier
dcterms:isReferencedBy
Institutional
repositories
eBank UK
aggregator
service
Crystal structure
report (HTML)
Eprint
“jump-off”
page
(HTML)
Eprint
manifestation
(e.g. PDF)
dc:identifier
Linking
Model input Andy Powell, UKOLN.
Eprint oai_dc
record (XML)
dc:type=“Eprint”
and/or ”Text”
Harvesting OAIPMH
oai_dc,ebank_dc
Other
aggregators and
services
Creating the metadata
• Potential to embed ‘deposit and
disseminate’ into workflow of chemist
in automated way
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
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)
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?
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”
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?
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
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/2005/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….
Repositories and digital curation
For later use?
Static
Data preservation
In use now (and the future)?
Dynamic
Data curation
“maintaining and adding value to a trusted body
of digital information for current and future use”
Provide value-added services
Annotation
• e-Lab books (Smart Tea Project in chemistry)
• Gene and protein sequences
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)
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
Issues summary
1. Research data is diverse, increasing rapidly
in volume and complexity
2. Repository collections are dynamic and
evolve
3. Technical challenges associated with
interoperability, persistence, provenance,
resource discovery and infrastructure
provision
4. Embedding in workflow is critical: scholarly
communications, research practice, learning
5. Knowledge extraction tools will generate new
discoveries based on repository content
6. Repository solutions must scale: M2M
processing will become the norm
Project homepage:
http://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/ebankuk/dissemination/jcdl2005/preprint.pdf
Acknowledgement to all project partners for their
contributions to this presentation.