Bih session7 panel2_summary

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Panel Discussion: TOWARD HORIZON 2020 CONSORTIA
Closing remarks at Biodiversity Informatics Horizons conference, Rome 6 September 2013

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Bih session7 panel2_summary

  1. 1. Structuring the biodiversity informatics community at the European level and beyond Panel Discussion: TOWARD HORIZON 2020 CONSORTIA Our goal, sine qua non, is to deliver predictive modelling
  2. 2. Structuring the biodiversity informatics community at the European level and beyond Panel Discussion: TOWARD HORIZON 2020 CONSORTIA We need : Clarity of vision, greater focus on end-goals; Good, simple tools with syntactic operability; Community identity; Links!
  3. 3. Structuring the biodiversity informatics community at the European level and beyond Panel Discussion: TOWARD HORIZON 2020 CONSORTIA We have a lot of data. Now we need to show that those data are actually useful. So, what questions can these data address? Stop mobilising just any data. Invert the system and direct what data are to be recovered by the question that is being addressed. This will also dictate the quality level.
  4. 4. Structuring the biodiversity informatics community at the European level and beyond Panel Discussion: TOWARD HORIZON 2020 CONSORTIA 3 key words: integration, cooperation, promotion. Integration, we have a number of directions like the virtual lab, and attention to data other than species data and the tools that can utilise the. Cooperation: if you want to model the whole biosphere you need data from over the whole world, not just Europe. The gap areas (Russia, China, Brazil) must be closed. Promotion: Europe should develop its leadership in this area. There are really only two different users: scientists & decision makers (effectively CBD and IPBES). If we satisfy these two, then everyone else should be able to get what they need. Thanks to Kepping Ma
  5. 5. Structuring the biodiversity informatics community at the European level and beyond Panel Discussion: TOWARD HORIZON 2020 CONSORTIA Our goal, sine qua non, is to deliver predictive modelling

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