2013 hackathon data elements

Target Data Elements

   Primary scoring for critical items
       dwc:catalogNumber
       aocr:verbatimRecordedBy        
       dwc:recordedBy
       dwc:recordNumber
       dwc:verbatimEventDate
       aocr:verbatimScientificName
   Secondary scoring for other key items
       aocr:verbatimInstitution
       dwc:datasetName
       dwc:verbatimLocality
       dwc:country
       dwc:stateProvince
       dwc:county
       dwc:verbatimLatitude
       dwc:verbatimLongitude
   Lastly, scoring for optional items
       dwc:eventDate
       dwc:scientificName
       dwc:decimalLatitude
       dwc:decimalLongitude
       dwc:fieldNotes
       dwc:sex
       aocr:verbatimDateIdentified        
       dwc:dateIdentified
       dwc:identifiedBy

Given the discussion from the broader community, it may also be that we change our minds with respect to what belongs in categories above. For now, those fields above should be seen as the ones of general interest and we can be flexible and discuss our evaluation strategy further with regard to primary / secondary / last. Participants may decide what is more important. It's clear that who is using the data and for what purpose drives which fields are seen as of greater value. If you are trying to find duplicate voucher records, the "who" is very important. If you are an ecologist looking for evidence of an organism in nature, you are more interested in the "where" fields and not so much interested in who collected the physical object as a voucher.

Note extra credit will be figured in for those that manage to get their data from CSV to XML format. Extra credit may also be given for those that manage to get their CSV columns according to the order of the fields as their appear in the image.

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