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Evolution or revolution? The changing data landscape

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Presentation given by Liz Lyon (UKOLN) at the 1st DCC Regional Roadshow, Bath, UK in November 2010.

Presentation given by Liz Lyon (UKOLN) at the 1st DCC Regional Roadshow, Bath, UK in November 2010.

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  • CATH database ( http://www.cathdb.info/ ). Its about protein structure classification, so it is quite niche. SCOP ( http://scop.mrc-lmb.cam.ac.uk/scop ), the other protein structure classification database. Its information is manually curated by Alexey Murzin and his group at the MRC Laboratory of Molecular Biology in Cambridge. Its web page still looks like it belongs in the late 90s. ChemSpider was originally a homemade project but was bought out by the Royal Society of Chemistry when they realised what a valuable resource it was to chemists. Massive centralisation – clouds and curated core facilities Massive decentralisation – sticks, spreadsheets, wikis


  • 1. UKOLN is supported by: Evolution or revolution? The changing data landscape Dr Liz Lyon, Director, UKOLN, University of Bath, UK Associate Director, UK Digital Curation Centre 1 st DCC Regional Roadshow, Bath, November 2010 . This work is licensed under a Creative Commons Licence Attribution-ShareAlike 2.0
  • 2. “ Data sets are becoming the new instruments of science” Dan Atkins, Univ Michigan
  • 3. Digital data as the new special collections? Sayeed Choudhury, Johns Hopkins
  • 4. Research data : institutional crown jewels? http://www.flickr.com/photos/lifes__too_short__to__drink__cheap__wine/4754234186 /
  • 5. Perspectives
    • Environmental scan
      • Scale and complexity
      • Infrastructure
      • Open science
    • Policy
      • Funders
      • Institutions
      • Ethics & IP
    • Practice Challenges
      • Storage
      • Incentives
      • Costs & Sustainability
  • 6. Big science
  • 7. Data collections PDB GenBank UniProt Pfam Spreadsheets, Notebooks Local, Lost High throughput experimental methods Industrial scale Commons based production Publicly data sets Cherry picked results Preserved CATH, SCOP (Protein Structure Classification) ChemSpider Slide: Carole Goble
  • 8. Structural Sciences Infrastructure
  • 9. Infrastructure Roadmap Cross Organisations
  • 10. Infrastructure Roadmap Cross Disciplines
  • 11. Infrastructure Roadmap Open Science
  • 12. Open Laboratories
  • 13.
    • Faculty work with public
    • Smartphone apps facilitation
    • Societal benefits
    Citizen as scientist
  • 14. Validate results data
  • 15. Policy
  • 16. INCREMENTAL Project
    • Institutional perspective
    • Creating & organising data
    • Storage and access
    • Back-up
    • Preservation
    • Sharing and re-use
    The majority of people felt that some form of policy or guidance was needed....
  • 17. Jeff Haywood, RDMF V October 2010 http://www.dcc.ac.uk/sites/default/files/documents/RDMF/RDMF5/Haywood.pdf
  • 18. “ While many researchers are positive about sharing data in principle, they are almost universally reluctant in practice. ..... using these data to publish results before anyone else is the primary way of gaining prestige in nearly all disciplines.” INCREMENTAL Project “ Data sharing was more readily discussed by early career researchers.”
  • 19. “ In our view, CRU should have been more open with its raw data…” Data is headline news JISC FoI FAQ
  • 20. P4 medicine: Predictive, Personalised, Preventive, Participatory. Leroy Hood – Institute for Systems Biology Your genome is basis for your medical record
  • 21. Open data and ethics
    • Direct-to-Consumer kits
    • Informed consent?
    • Privacy?
    • UC Berkeley initiative
    • Implications for HE students & staff?
  • 22. Policy Gaps...
    • Is Policy disconnected from Practice?
      • Data Sharing
      • Data Licensing
      • Ethics and Privacy
      • Citizen Science & Public Engagement
      • Data Storage, Selection & Appraisal
      • Data Citation and Attribution
  • 23. “ Departments don’t have guidelines or norms for personal back-up and researcher procedure, knowledge and diligence varies tremendously. Many have experienced moderate to catastrophic data loss” Incremental Project Report, June 2010 http://www.flickr.com/photos/mattimattila/3003324844/
  • 24. Data storage... The case for cloud computing in genome informatics. Lincoln D Stein, May 2010
      • Scaleable
      • Cost-effective (rent on-demand)
      • Secure (privacy and IPR)
      • Robust and resilient
      • Low entry barrier / ease-of-use
      • Has data-handling / transfer / analysis capability
    • Cloud services?
  • 25. Your data in the cloud
  • 26. Incentivising data management
  • 27.  
  • 28. Sustainability: Who owns? Who benefits? Who selects? Who preserves? Who pays?
  • 29. KRDS
  • 30. Chicago Mart Plaza, 6-8 December 2010 Thank you…