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Data visualization and digital humanities research


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A presentation given at LITA National Forum 2011 in St. Louis. The presentation, by Erik Mitchell & Susan Smith, was about a project that was supported through a Wake Forest U Summer Technology Exploration Grant

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Data visualization and digital humanities research

  1. 1. Data visualization and digital humanities research:  a survey of available data sets and tools <br />LITA National Forum 2011<br />St. Louis, MO<br />Friday, September 30, 2011<br />Erik Mitchell, University of Maryland<br />Susan Sharpless Smith, Wake Forest University<br />
  2. 2. Motivation<br />“Digital humanities needs gateway drugs. Kudos to the pushers on the Google Books team.” <br />- Dan Cohen<br />“Linked open data could have the same leveraging effect that the World Wide Web had on computing, said Micki McGee, an assistant professor of sociology at Fordham University”<br /> -Steve Kolowich, The Promise of Digital Humanities, Inside HigherEd<br />
  3. 3. Birth of a word<br />“Imagine if you could record your life, everything you said, everything you did available in a perfect memory store at your finger tips. “<br /> - Deb Roy – The Birth of a Word<br />
  4. 4. Overview<br />Discuss examples of data-focused research tools<br />Explore tools<br />Consider roles for librarians<br />Wrap-up/Q & A<br />
  5. 5. Taxonomy of uses<br />
  6. 6. Searching and Discovery<br />Examples: <br />BYU Corpua<br />WOK Citation Mapping WOK<br />
  7. 7. Visualization<br />Free Visualization Tools<br />
  8. 8. Analysis and publishing<br />NodeXL<br />
  9. 9. Tool Comparison - linguistics<br />
  10. 10. Tool exploration<br />Discover / Search<br />What kinds of discovery tools exist and how common are the discovery features across different datasets / systems?<br />Visualization<br />What visualization features exist, are there products that are easy to use, are the skills transferable?<br />Analysis / Annotation<br />What analytical tools are included, what analysis techniques are common?<br />
  11. 11. Perseus<br /><br />
  12. 12. JSTOR Data For Research<br /><br />
  13. 13. Wordseer<br />AditiMuralidharan Marti Hearst<br /><br />
  14. 14. Google’s Ngram<br />But here's the rub. Google Books, as others point out, wasn't really built for research. . . That means Google Books didn't come with the interfaces scholars need for vast data manipulation . . . <br /><br />
  15. 15. Ted talk on Google NGRAM viewer<br /><br />
  16. 16. Concordancing<br />Eric Lease Morgan -<br />
  17. 17. Google’s public data explorer<br /><br />
  18. 18. Data analysis - NodeXL<br /><br />Analyzing Social Media Networks with NodeXL: Insights from a Connected World<br />
  19. 19. Data cleaning – Google Refine<br /><br />
  20. 20. Data visualization – Google Fusion Tables<br /><br /><br />
  21. 21. Research/teaching need<br />Researcher needs vary from advanced linguistic analysis and IT support to need for basic digital content/infrastructure<br />Corpus-based research<br />
  22. 22. Librarian contributions<br />Domain specific, tool-type specific comparisons<br />IT and research support – data analysis, data curation, tool/data sources identification <br />Shift from “reference” to “research” in sync with move from resource discovery to thematic analysis<br />
  23. 23. Next steps<br />Build new skills, develop new systems<br />Create tutorials guides<br />Explore connections between data/curation and publishing and these tools – so is there a connection<br />Explore role of library discovery systems and consider new feature implementation.<br />
  24. 24. Sites of interest<br />