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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. Enhancing access to research data: overview.

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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

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 / 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

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 carerecords

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

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

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

Aggregator services: national, commercial

Learning object creation, re-use


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



Resource discovery, linking, embedding


Peer-reviewed publications: journals, conference proceedings

Quality assurance bodies

Data Overload!

EPSRC National Crystallography Service

How do we disseminate?

The data deluge: crystallography

Data overload & the publication bottleneck




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 sub-disciplines? 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









Grid Middleware




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

Setup via GUI

BruNo Unmount

Sample Tray

BruNo Mount





Unit Cell





Data Collection

Data Process

System Y

Data Collection





Harvest (XML)



Data Flow in eBank UK



Index and Search

Institutional repository

eBank aggregator

Data files


Southampton digital repository


Access to ALL underlying data

Harvesting: OAIster

OAI-PMH: harvesting and aggregating

eBank aggregator at UKOLN


Demonstrating potential for linking between data and journal article

Embedded search service at PSIgate

PSIgate subject


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


eBank data model




Harvesting OAI-PMH oai_dc

Crystal structure (data holding)

ePrint UK aggregator service



Harvesting OAI-PMH


ebank_dc record (XML)


eBank UK aggregator service


Institutional repositories


Crystal structure report (HTML)


Harvesting OAI-PMH oai_dc,ebank_dc

Eprint “jump-off” page (HTML)


Eprint manifestation (e.g. PDF)

Eprint oai_dc record (XML)

Other aggregators and services

dc:type=“Eprint” and/or ”Text”


Model input Andy Powell, UKOLN.

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”

    • domain: International Chemical Identifier (InChI code)

  • Resource discovery : Google Scholar?

  • Provenance: authenticity, authority, integrity?

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? In use now (and the future)?



Data preservation

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

  • 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

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/ebank-uk/dissemination/jcdl2005/preprint.pdfAcknowledgementto all project partners for their contributions to this presentation.

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