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Elsevier TDM Policy
• Access through API only
• Text only- no images, tables
• Research must register details
• Click-through licence
• Terms can change any time
• Reproducibility of results
Knowledge Discovery in 3 Hops
• Human computers
• Shakespeare’s frugality with words
• Cancer diagnosis
Human Computers (1901)
http://marlowe-shakespeare.blogspot.nl/2009/02/on-mendenhall-and-compelling-evidence.html
Use of words(2009)
Marsden J, Budden D, Craig H, Moscato P (2013) Language Individuation and Marker Words:
Shakespeare and His Maxwell's Demon. PLoS ONE 8(6): e66813. doi:10.1371/journal.pone.0066813
Cancer diagnosis(2013)
• http://theconversation.com/shakespeare-and-cancer-diagnoses-how-bard-can-it-be-
15381ata sets.
The point?
• Multidisciplinary practitioner-led approach
• Potential will be more than we can begin to
imagine now
• Open underpins evolution
• Cannot errect artificial barriers
Thank You!
Any questions?
@skreilly
www.the haguedeclaration.com

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Launch: The Hague Declaration on Knowledge Discovery in the Digital Age

Editor's Notes

  1. Physicist T.C. Mendenhall hired 2 women to count the length of words in Shakepeares works. Word length frequency curve remains consistent- way to ascertain authenticity. Unlike most English authors he used more 4 letter words than 3 letter words. No correlation to Bacon but (as was discovered years later) was as similar to  Christopher Marlowe (another Elizabethan playwrite and poet)  as he was to himself
  2. Marker of Shakespeares writing is his comparative underuse of words and selections of words Identified 20 most used words across body of literature of period. New scoring of markers not just based on the use of words but on the underuse. Excellent methods for identifying markers in large datasets  fluctuations of the observed frequencies of words all to (infinitive) now ye
  3. Biomarker could be elevated enzyme levels, help to indvidualise treatment, panels of biomarkers more effective, increaing sensitivity and specificity. application of scoring method developed previously- mislabelled samples, significant outliers etc in big data