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Realising the scholarly knowledge cycle:

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Presentation on theme: "Realising the scholarly knowledge cycle:"— Presentation transcript:

1 Realising the scholarly knowledge cycle:
The experience of eBank UK Dr Liz Lyon, UKOLN, University of Bath, UK CNI Task Force Meeting Spring 2004 Alexandria, Virginia, UKOLN is supported by: a centre of expertise in digital information management

2 Overview Setting the scene The scholarly knowledge cycle
e-Research trends Towards a common infrastructure The scholarly knowledge cycle Data, information and workflows Provenance eBank UK Project The experience so far Issues arising Challenges for the future CNI Spring 2004

3 Setting the scene

4 A Vision for Research, Research Councils UK, December 2003.
“The next generation of research breakthroughs will rely upon new ways of handling the immense amounts of data that are being produced by modern research methods and equipment, such as telescopes, particle accelerators, genome sequencers and biological imagers….Similar developments are having an impact in the arts and humanities, and in the social sciences.” A Vision for Research, Research Councils UK, December 2003.

5 Report of the National Science Foundation
Blue-Ribbon Advisory Panel on Cyberinfrastructure 2003 CNI Spring 2004

6 Report of the National Science Foundation
Blue-Ribbon Advisory Panel on Cyberinfrastructure 2003 CNI Spring 2004

7 UK e-Science Programme
“e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it.” John Taylor, Director General, Research Councils, UK CNI Spring 2004

8 CNI Spring 2004

9 Powering the Virtual Universe http://www. astrogrid
Powering the Virtual Universe (Edinburgh, Belfast, Cambridge, Leicester, London, Manchester, RAL) AstroGrid will provide advanced, Grid based, federation and data mining tools to facilitate better and faster scientific output. Picture credits: “NASA / Chandra X-ray Observatory / Herman Marshall (MIT)”, “NASA/HST/Eric Perlman (UMBC), “Gemini Observatory/OSCIR”, “VLA/NSF/Eric Perlman (UMBC)/Fang Zhou, Biretta (STScI)/F Owen (NRA)” : CNI Spring 2004

10 e-Research: the trends?
Increasingly data–intensive, quantitative Open access to data and information OECD Declaration January 2004 Implementing new science Inter-disciplinary New disciplines e.g. Astro-informatics New skills requirements IT + statistics + domain Collaborative virtual / transient communities / organisations Highly distributed resources CNI Spring 2004

11 New resources…….used in new ways
Primary / original data Observational, experimental, numeric, genomic, 2/3D molecular structures, satellite images, electron micrographs, wave spectra, CAD, musical compositions, VR, performances, animations Data and information Creation, discovery, gathering, aggregation, dis-aggregation, replication, federation, manipulation, transformation, linking, annotation, editing/versioning, validation, (self-)archiving, deposit, publication, curation Knowledge extraction and management Analysis (textual, musical, statistical, mathematical, visual, chemical, gene……) Mining (text, data, structures……) Modelling (economic, mathematical, biological..) Simulation (molecular, physical, environmental, games…) Presentation (visualisation, rendering….) CNI Spring 2004

12 Towards a common infrastructure
UK e-Science Programme & JISC Development e-Science Phase – 2006 A National e-Science Centre linked to a network of Regional Grid Centres An Open Middleware Infrastructure Institute (OMII) based on common standards (Web Services) JISC Information Environment Technical architecture based on open standards (Web Services, OAI-PMH, Z39.50, RSS…..) A Digital Curation Centre (DCC) Virtual Research Environments? A changing landscape of scholarly communications CNI Spring 2004

13 The scholarly knowledge cycle

14 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 Data analysis, transformation, mining, modelling Aggregator services: national, commercial Harvestingmetadata Research & e-Science workflows Repositories : institutional, e-prints, subject, data, learning objects Deposit / self-archiving Validation Validation Publication Linking Peer-reviewed publications: journals, conference proceedings Data curation: databases & databanks CNI Spring 2004

15 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 Data analysis, transformation, mining, modelling Aggregator services: national, commercial Harvestingmetadata Research & e-Science workflows Repositories : institutional, e-prints, subject, data, learning objects Deposit / self-archiving Validation Validation Publication Linking Peer-reviewed publications: journals, conference proceedings Data curation: databases & databanks CNI Spring 2004

16 CNI Spring 2004

17 CNI Spring 2004

18 CNI Spring 2004

19 CNI Spring 2004

20 CNI Spring 2004

21 CNI Spring 2004

22 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 Data analysis, transformation, mining, modelling Aggregator services: national, commercial Harvestingmetadata Research & e-Science workflows Repositories : institutional, e-prints, subject, data, learning objects Deposit / self-archiving Validation Validation Publication Linking Peer-reviewed publications: journals, conference proceedings Data curation: databases & databanks CNI Spring 2004

23 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 CNI Spring 2004

24 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 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 Validation Publication Resource discovery, linking, embedding Validation Linking Peer-reviewed publications: journals, conference proceedings Quality assurance bodies Data curation: databases & databanks CNI Spring 2004

25 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 Validation Publication Resource discovery, linking, embedding Validation Linking Peer-reviewed publications: journals, conference proceedings Quality assurance bodies Data curation: databases & databanks CNI Spring 2004

26 The eBank UK Project

27 eBank UK project JISC-funded for 1 year from September 2003
UKOLN (lead), University of Southampton, University of Manchester “Building the links between research data, scholarly communication and learning” e-Science testbed Combechem Grid-enabled combinatorial chemistry Crystallography, laser and surface chemistry Development of an e-Lab using pervasive computing technology National Crystallography Service Resource Discovery Network PSIgate physical sciences portal CNI Spring 2004

28 The project team UKOLN Michael Day Monica Duke Rachel Heery Liz Lyon +
Andy Powell Southampton Les Carr Simon Coles Jeremy Frey Chris Gutteridge Mike Hursthouse Manchester John Blunden-Ellis CNI Spring 2004

29 Key Deliverables Requirements specification Pilot service
Two supporting studies: Provenance: review of current research Feasibility report on dataset description and schema Consultative evaluation workshop and report Recommendations for future work CNI Spring 2004

30 Pilot service – technical architecture
Diagram by Andy Powell, UKOLN CNI Spring 2004

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

32 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 Report: generate Crystal Structure Report CNI Spring 2004

33 CNI Spring 2004

34 First steps: establishing common ground…
Understand the data creation process Terminology and definitions Data Metadata Datafile Dataset Data holding Different views Digital library researchers, computer scientists, chemists Generic vs specific Modeller vs practitioner Aim for a common ontology Modelling the domain Creating a metadata schema CNI Spring 2004

35 PSIgate portal Dataset Searching, linking and embedding Dataset
dcterms:references Crystal structure (data holding) ePrint UK aggregator service Harvesting OAI-PMH oai_dc Linking Searching, linking and embedding Harvesting OAI-PMH ebank_dc ebank_dc record (XML) Deposit PSIgate portal dc:type=“CrystalStructure” and/or “Collection” eBank UK aggregator service Institutional repository dc:identifier Crystal structure report (HTML) dcterms:isReferencedBy Harvesting OAI-PMH oai_dc dc:type=“Eprint” and/or ”Text” Eprint oai_dc record (XML) Subject service Searching, linking and embedding Model input Andy Powell, UKOLN. CNI Spring 2004

36 Where are we now? Version 1.0 eBank metadata schema
Pilot eBank repository for harvesting Exports records as ebank_dc and oai_dc Validation of schema Against harvesting and searching Against user requirements Against other schema Concept of a collection and a Collection Level Description Implementing the pilot service CNI Spring 2004

37 Challenges for the future

38 What next? The metadata schema…some issues
Reduce to its simplest form or reflect the complexity? ebank_dc versus oai_dc Compatibility with other schema CLRC Scientific Metadata Model vs (under revision) Investigate packaging options METS MPEG 21 DIDL ?? Expand to include SMART e-Lab metadata e.g. sample preparation CNI Spring 2004

39 …and also…. Investigate identifiers e.g. International Chemical Identifier Metadata enhancement - subject keyword additions to datasets based on knowledge of keywords in related publications Develop search interface – embedding eBank UK Testing with PSIgate physical sciences portal Explore context sensitive linking: find me Datasets by this person Journal articles by this person Datasets related to this subject Journal articles on this subject Learning objects by this person Learning objects on this subject CNI Spring 2004

40 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 Validation Publication Resource discovery, linking, embedding Validation Linking Peer-reviewed publications: journals, conference proceedings Quality assurance bodies Data curation: databases & databanks CNI Spring 2004

41 Potential longer term impact
Track data, information and workflows in e-research and scholarly communications – knowledge audit?? Validate the accuracy and authenticity of derived works – ideas audit?? Facilitate explicit referencing and acknowledgment of original contributors – intellectual integrity?? Raise standards associated with publication of research outputs – academic publishing rigour?? Implement open access to and dissemination of data and information – enhance the research process?? Give students links to original data underpinning published works – enhance the learning process?? CNI Spring 2004

42 CNI Spring 2004

43 Thank you. Questions?…..

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