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Creating impact with Accessible Data in
Agriculture and Nutrition
Sharing data pre-competitive in Public Private Partnersh...
Resources For Breeding
2
FAIR data Principles
The FAIR Data initiative aims to support existing communities
in their attempts to enable valuable sc...
Sharing Pre-Competitive data
Sharing Pre Competitive data and
Analytics, for example:
 Developed controlled vocabularies can be easily
shared, however...
Questions?
Acknowledgements:
Theo van Hinthum & Frank
Menting (Centre Genetic
Resources, NL)
Denis Guryunov & Martijn
van ...
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Creating impact with accessible data in agriculture and nutrition: sharing data pre-competitive in public private partnerships

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Richard Finkers (Wageningen UR) presented at the 2nd International Workshop: Creating Impact with Open Data in Agriculture and Nutrition in The Hague, 11 September 2015.

Published in: Government & Nonprofit
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Creating impact with accessible data in agriculture and nutrition: sharing data pre-competitive in public private partnerships

  1. 1. Creating impact with Accessible Data in Agriculture and Nutrition Sharing data pre-competitive in Public Private Partnerships 11 November 2015, Richard Finkers (@rfinkers) Wageningen UR Plant Breeding
  2. 2. Resources For Breeding 2
  3. 3. FAIR data Principles The FAIR Data initiative aims to support existing communities in their attempts to enable valuable scientific data and knowledge to be published and utilized in a ‘FAIR’ manner. • Findable - (meta)data is uniquely and persistently identifiable. Should have basic machine readable descriptive metadata. • Accessible - data is reachable and accessible by humans and machines using standard formats and protocols. • Interoperable - (meta)data is machine readable and annotated with resolvable vocabularies/ontologies. • Reusable - (meta)data is sufficiently well-described to allow (semi)automated integration with other compatible data sources.
  4. 4. Sharing Pre-Competitive data
  5. 5. Sharing Pre Competitive data and Analytics, for example:  Developed controlled vocabularies can be easily shared, however not the ontology.  Required analytics can be developed pre-competitive and shared easily, strength is in the germplasm and innovative approaches for interpretation of the outcome of these algorithms.  Discovery for Knowledge is pre-competitive, the discovered knowledge is competitive
  6. 6. Questions? Acknowledgements: Theo van Hinthum & Frank Menting (Centre Genetic Resources, NL) Denis Guryunov & Martijn van Kaauwen et. all.

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