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Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
Asking Questions of Data
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Asking Questions of Data

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If the information I need is locked in a dataset, and requires some sort of database query to construct that information from the dataset, who do I turn to for help in accessing it? The library? And …

If the information I need is locked in a dataset, and requires some sort of database query to construct that information from the dataset, who do I turn to for help in accessing it? The library? And if not the library, who?

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  • 1. Asking questions of data: from simple search to advanced queries Tony Hirst Computing and Communications Dept, The Open University, UK blog.ouseful.info / @psychemedia
  • 2. Time was when…
  • 3. Search and replace
  • 4. Advanced search and replace
  • 5. Regular expressions
  • 6. How do we discover or recover information from data?
  • 7. A medical librarian is probably not a expert on zoonotic H5H1 influenza infections…
  • 8. How do we cope with new discovery regimes and environments?
  • 9. “DIY Dreamcatcher”, via flickr user: barbroandersen
  • 10. “DIY Dreamcatcher”, via flickr user: barbroandersen
  • 11. Friends of my friends who are librarians
  • 12. People who live in Bath and like the Future of Libraries
  • 13. When the databases really are databases..?
  • 14. USERS QUERY INFORMATION
  • 15. DATA USERS QUERIES INFORMATION
  • 16. An example – questions around local elections
  • 17. Which wards in my local council area were not contested by Conservative affiliated candidates?
  • 18. Can I get a list of all the candidates?
  • 19. Can I get a list of people standing in wards in Newport who live out of ward?
  • 20. SELECT * FROM candidates WHERE ward LIKE 'Newport%' AND address NOT LIKE '%Newport%'
  • 21. How many candidates did each party field?
  • 22. SELECT desc AS Party, COUNT(desc) AS Number FROM candidates GROUP BY Party ORDER BY Number DESC
  • 23. Which wards were not contested by Conservative affiliated candidates?
  • 24. SELECT DISTINCT ward from Candidates WHERE ward NOT IN (SELECT DISTINCT ward FROM Candidates WHERE desc LIKE '%Conservative%’)
  • 25. Which candidates were signatories in to other proposals?
  • 26. Time is now…
  • 27. @psychemedia blog.ouseful.info

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