Difference between revisions of "Transcription Hackathon"

From iDigBio
Jump to: navigation, search
(Development Resources)
(Development Resources)
Line 29: Line 29:
 
** Field notebooks  ideal response: link to be provided by Austin
 
** Field notebooks  ideal response: link to be provided by Austin
  
* Notes From Nature web interface:
+
* [http://www.notesfromnature.org/ Notes From Nature] web interface:
 
** Code available at https://github.com/zooniverse/notesFromNature
 
** Code available at https://github.com/zooniverse/notesFromNature
 
** Forked version available at: link to be provided by Alex
 
** Forked version available at: link to be provided by Alex

Revision as of 08:11, 5 December 2013

Notes from Nature/iDigBio Hackathon to Further Enable Public Participation in the Online Transcription of Biodiversity Specimen Labels

December 16–20 at the University of Florida, Gainesville

Agenda and Logistics

Coordination

Development Resources

  • Existing crowdsourcing datasets from Notes From Nature: datasets with transcriptions of different types of collections labels:
    • Herbarium labels: link to be provided by NfN or Austin
    • Entomology labels: link to be provided by NfN or Austin
    • Field notebooks: link to be provided by NfN or Austin
  • Existing solution datasets to assess quality of crowdsourcing consensus:
    • Herbarium labels ideal response: link to be provided by Austin
    • Entomology labels ideal response: link to be provided by Austin
    • Field notebooks ideal response: link to be provided by Austin
  • CYWG iDigBio Image Ingestion Appliance:
    • The appliance can be used to ingest the images to be used by the crowdsourcing service into the iDigBio storage, and made publicly accessible through HTTP. The relationship between the image filenames and the URL can be exported by the appliance in CSV format.
  • Code from the aOCR Hackthon:
    • HandwritingDetection (https://github.com/idigbio-aocr): an algorithm that separates images into sets with no handwriting, little handwriting (mostly text typed or printed), lots of handwriting, based on the noise generated by the OCR software. Read more at Ben's blog. This could be used to rank which images are in more need for human transcription.
    • Dictionaries to improve crowdsourcing consensus (e.g., names of collectors, scientific names): link to be provided by aOCR?