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The Changing Face of Citizen Science

Avatar for Arfon Smith Arfon Smith
October 05, 2011
Research
1
110

The Changing Face of Citizen Science

Presentation at http://www.europeanaconnect.eu/europeanatech/
Avatar for Arfon Smith

Arfon Smith

October 05, 2011
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Transcript

  1. Image: www.smartdestinations.com THE CHANGING FACE OF CITIZEN SCIENCE Arfon Smith

  2. Citizen science Volunteer Computing Crowdsourcing

  3. None
  4. None
  5. None
  6. None
  7. None
  8. None
  9. None
  10. None

  11. http://www.novacelestia.com

  12. None
  13. None

  14. 0 20 40 60 80 100 Professor

  15. 0 2,500 5,000 7,500 10,000 Professor Paper

  16. 0 12,500 25,000 37,500 50,000 Professor Paper PhD

  17. 0 250,000 500,000 750,000 1,000,000 Professor Paper PhD SDSS

  18. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 0 6

    12 18 24 30 36 42 48 Classifications per hour Hours 1 Kevin months Fukugita et al. 2007

  19. 170,000 Volunteers

  20. 50,000,000 Classifications

  21. Crowdsourced Science?

  22. None
  23. None
  24. None
  25. None

  26. (it works)

  27. Better than professionals

  28. Clean Super Clean

  29. None
  30. None
  31. None
  32. None
  33. None
  34. None
  35. None
  36. None
  37. None
  38. None
  39. None
  40. None
  41. None
  42. None
  43. None
  44. None

  45. https://github.com/zooniverse/Scribe

  46. Motivations

  47. None
  48. None
  49. None
  50. None
  51. None
  52. None
  53. None

  54. https://github.com/zooniverse/Talk

  55. 475,000 Volunteers

  56. 300,000,000 Classifications

  57. None

  58. Ethics of crowdsourcing

  59. Social contract

  60. 1. Collaborators not users 2. Contribute to real research 3.

    Don’t waste people’s time
  61. None

  62. Why crowdsource science?

  63. Serendipity (Measurable) Accuracy Scale Education

  64. None

  65. Future

  66. None
  67. None
  68. None
  69. None

  70. 0 25,000,000 50,000,000 75,000,000 100,000,000 Professor Paper PhD SDSS ?

  71. Expensive Noisy Classifier agents (Machine & Human)

  72. What are humans good at?

  73. Training Training

  74. None
  75. None

  76. Preference learning

  77. None

  78. Bar strength

  79. None

  80. Bar strength

  81. Classifier combination

  82. None

  83. ?

  84. None
  85. None

  86. Thanks [email protected] Twitter: @arfon