berlin5 : NIH and RCUK

NIH has an open policy since 1994. Barbar Seto presented an example, GWAS which has to deal with human subjects. How to make data Open, while protecting identity?
NIH serves as central data repository, including: Genome-wide acssociation study (GWAS), Genbank, Protein cluster, Pubchem, http://www.nlm.nioh.gov/databases/
GWAS – identifies common genetic factors influencing health and disease. Genetic variations associated with observable traits. It combines genomic data with clinical and phenotypic data to understand disease mechanism and prediction of disease.
Because some diseases are rare it is sometimes possible to work out indentities from anonymized data.
Cold room for use at grantee institution = data is open within a specified location and can’t be taken away
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Mark Thorley NERC and RCUK. Reacting to issues brought up in the morning. 4.1 billion EUR:

  • Data as byproduct of research data.
  • Data as part of the scientific record; support publications
  • Data as published output in own right

Drivers:

  • scientific need (e.g. atmospheric physics requires data sharing)
  • increased value – as part of larger collection e.g. oceans
  • value for money. Ship costs 10,000 GBP/day
  • public funds, so public access

One-size-fits all is NOT appropriate.

  • RC’s recognise data as valuable long-term public-good resource
  • Data sharing improves opportunities for exploitation (e.g. mashups). “Power of information” (UK, Cabinet Office) Stimulate knowledge economy
  • Investigator has a right of first use and right to be acknowledged. But there must be a limit, but early release can be a problem.
  • Effective exploitation requires effective data management.
  • Must be legal. (e.g. directive on public access to environmental information)

Any differences between RC’s is not policy, but how to support data sharing.
National facilities (NERC, ESRC) or local delegation (AHRC, BBSRC, MRC)?

  • National – longer term, single point, centres of excellence. expensive, less agile
  • Delegates, more responsive, closer to science, cheaper, lack of long term.
  • Long term commitment. Needs long-term vision, long term support. Are PIs the right people to do this?
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