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

2. 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

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

4. 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

5. 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

6. Repository evolution: 1971 Research collection <12 files 2005 Reference collection >2700 structures deposited in 6 months

7. 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

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

9. Data Overload! How do we disseminate? EPSRC National Crystallography Service The data deluge: crystallography

10. Data overload & the publication bottleneck 25,000,000 2,000,000 300,000

11. Current Publishing Process Journal articles: aims, ideas, context, conclusions – only most significant data Raw & underlying data required by peers not readily available

12. 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

13. 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/

14. 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

15. 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)

16. 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?

17. 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?

18. 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

19. Comb-e-Chem Project X-Ray e-Lab Analysis Properties Properties e-Lab Simulation Video Diffractometer Grid Middleware Structures Database

20. Crystallography workflow RAW DATADERIVED DATARESULTS 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

21. Data Collection Diffraction Unit Cell Success Strategy Data Collection Data Process System Y PreScans Yes BruNo Mount BruNo Unmount Setup via GUI Sample Tray No

22. Data Flow in eBank UK OAI-PMH Submit Store/link Harvest (XML) Index and Search Data files Metadata present HTML present HTML Institutional repository eBank aggregator Create

23. Southampton digital repository http://ecrystals.chem.soton.ac.uk

24. Access to ALL underlying data

25. Harvesting: OAIster

26. 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

27. Embedded search service at PSIgate PSIgate subject gateway: service provider

28. 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

29. ebank_dc record (XML) Crystal structure (data holding) Crystal structure report (HTML) Dataset Institutional repositories eBank UK aggregator service ePrint UK aggregator service Other aggregators and services Deposit Harvesting OAI-PMH ebank_dc Harvesting OAI- PMH oai_dc,ebank_dc Harvesting OAI-PMH oai_dc Dataset dc:identifier dcterms:references Linking dc:type=“CrystalStructure” Model input Andy Powell, UKOLN. Eprint oai_dc record (XML) dcterms:isReferencedBy dc:type=“Eprint” and/or ”Text” eBank data model Eprint “jump-off” page (HTML) dc:identifier Eprint manifestation (e.g. PDF) Linking Deposit

30. Creating the metadata Potential to embed ‘deposit and disseminate’ into workflow of chemist in automated way

31. 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

32. 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)

33. 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?

34. 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?

35. 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?

36. 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

37. 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 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 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….

38. For later use? In use now (and the future)? Repositories and digital curation Data preservationData curation StaticDynamic “maintaining and adding value to a trusted body of digital information for current and future use”

39. Provide value-added services Annotation e-Lab books (Smart Tea Project in chemistry) Gene and protein sequences

40. 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)

41. 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

42. 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

43. 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.pdf Acknowledgement to all project partners for their contributions to this presentation.