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Data management: international challenges, national infrastructure, and institutional responses

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Presentation delivered to UKOLN on April 1, 2011.

Presentation delivered to UKOLN on April 1, 2011.

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  • So, let’s look at the state of data in scholarly communication. Unfortunately, it’s inconvenient, imprisoned, invisible, inaccessible, and incomprehensible
  • Need to retype
  • Near impossible to liberate. Talk about ChemXSeer example and DataThief Java application
  • Too transformed
  • Discipline scientist may know how to get these data but I don’t
  • NOTE: Some of these arguments are at individual, national, global levelEfficiency for researcher – don’t reinvent wheelValidation – repeatability of researchIntegrity – of scholarly recordValue for Money for funder – public money funded it, it should be available to public (ClimateGate!)Self-interest – sharing with a future self, greater visibility, more citationsSo, what are some good stories around data sharing?
  • Number of initiatives around the world working to do a better job on data: NSF DataNet (Sayeed/Bill later in conference), JISC Managing Research Data, NL SURF/DANS
  • I’m going to take a programmatic view (because that explains how we are funding stuff), while recognising that the issues don’t necessarily fit neatly inside those boundaries
  • And thank you for the opportunity to speak to you this afternoon.
  • Transcript

    • 1. Data Management: International challenges, National Infrastructure, and Institutional Responses - an Australian Perspective
      Dr Andrew Treloar
      Director of Technology
      Australian National Data Service
    • 2. International Challenges
    • 3. Inconvenient data
      DOI: 10.1098/rsta.2005.1569
    • 4. Imprisoneddata
      DOI 10.1098/rsta.2006.1793
    • 5. Invisible data
      DOI 10.1098/rsta.2006.1793
    • 6. Inaccessible data
    • 7. Incomprehensible data
      ands.org.au
      7
    • 8. 8
      Summary
      Not a first class object
      Unmanaged
      Disconnected
      Unfindable
      Unreusable
    • 9. Why re-use data?
      Efficiency
      Validation
      Integrity
      Value for money
      Self-interest
    • 10. Astronomy case study
      Hubble Space Telescope (HST) operating since 1990
      Observations are proposed, and if accepted, data is collected and made available to the proposers – who then write a research paper
      Each year around 1,000 proposals are reviewed and approximately 200 are selected, for a total of 20,000 individual observations
      Data is stored at the Space Telescope Science Institute and made available after embargo period
      There are now more research papers written by “second use” of the research data, than by the use initially proposed
      10
    • 11. 11
      Source: http://archive.stsci.edu/hst/bibliography/pubstat.html
    • 12. Cancer micro-array trial case study
      Piwowar, et. al., “Sharing Detailed Research Data Is Associated with Increased Citation Rate”
      http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000308
      Looked at the citation history of cancer microarray clinical trial publications
      Found that publicly available data was associated with a 69% increase in citations, independent of journal impact factor, date of publication, and author country of origin
      12
    • 13. Alzheimer’s Disease NeuroImaging Initiative
      Collaborative effort to find brain biomarkers for Alzheimer’s disease
      Key: All brain scans and other data freely available to scientific community without embargo.
      Over 3K full downloads and 1M scan downloads by over 400 investigators world-wide
      Over 100 publications
      13
      Institut Douglas CC BY-NC-ND
      http://www.fnih.org/work/areas/chronic-disease/adni
    • 14. National Infrastructure
      14
    • 15. National approaches
      Number of different countries: UK, US, DE, NL
      Different environments => different ecosystems
      and so some local tradeoffs
      But some common themes emerging:
      Do the things that only you can do
      Be the ‘voice for data’
      Prime the pump
    • 16. Australian National Data Service
      • An initiative of the Australian Government being conducted as part of the National Collaborative Research Infrastructure Strategy ($A24M) and the Super Science Initiative ($A48M)
      • 17. A collaboration between Monash University, the Australian National University and CSIRO
      • 18. Nearly 50staff, funded to mid 2013
      • 19. More researchers re-using more data more often
      • 20. Data as a first-class object
      ands.org.au
      16
    • 21. ANDS is enabling the transformation of:
      Data that are:
      Unmanaged
      Disconnected
      Invisible
      Single use
      17
      Collections that are:
      Managed
      Connected
      Findable
      Reusable
      so that Australian researchers can easily discover, access and re-use data
    • 22. 18
      Defining characteristics of ANDS
      Building national services
      Engaging with institutions not researchers (mostly)
      Working within funding constraints
      use, not amount!
      Building the Australian Research Data Commons
    • 23.
    • 24. 20
      ANDS Programs
      Frameworks and Capability
      Seeding the Commons
      Data Capture
      Metadata Stores
      ARDC Core
      Public Sector Data
      Applications
    • 25. 21
      Spending profile
    • 26. RDA Demo
      http://www.google.com/
      22
    • 27. Institutional Responses
    • 28. 24
      Driven by Australian Code for Responsible Conduct of Research
      Equivalent of UKRIO’s Code of Practice for Research: Promoting good practice and preventing misconduct
      Takes significant time to get accepted
      ANDS providing models of good practice
      Seeding the Commons
      U->M
      Data management policy and planning
    • 29. 25
      Retrospective data description
      Different selection mechanisms
      Seeding the Commons
      U->M
      Fixing the past
    • 30. 26
      Improving internal CRIS systems
      Better integration
      Moving beyond publications
      Better links to data collection descriptions
      Seeding the Commons, Metadata Stores
      D->C
    • 31. 27
      Facilitating easier/better capture of data and metadata from selected ‘instruments’
      Making the right thing easier
      Improving quality of metadata
      Data Capture
      U->M
      S->R
      Fixing the future
    • 32. 28
      Describing institutions research data assets
      Series of metadata stores rollouts plus some ancillary activity
      Metadata Stores, Seeding the Commons, Data Capture
      D->C
      I->F
    • 33. 29
    • 34. Ongoing Issues
      30
    • 35. Country-Institution-Discipline
      Who wins?
      Who should win?
      31
    • 36. Sustainability, sustainability, sustainability…
      Institutional activity
      National services/resources
      Developed software
      32
    • 37. 33
      Priming the pump, or continuing to pump?
      If institutions/researchers/disciplines don’t care, why should the funders?
      Role of Government
    • 38. Questions/Links
      ands.org.au
      services.ands.org.au
      andrew.treloar@ands.org.au
      @atreloar
      andrew.treloar.net