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Melding human and machine capabilities to document the world’s living organisms

Melding human and machine capabilities to document the world’s living organisms. University of Maryland TMSP series March 7, 2011. Project Team.

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Melding human and machine capabilities to document the world’s living organisms

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  1. Melding human and machine capabilities to document the world’s living organisms University of Maryland TMSP series March 7, 2011

  2. Project Team ArijitBiswas (CS, Doctoral student); Anne Bowser (iSchool, Masters student); Jen Hammock (EOL); Derek Hansen (iSchool); David Jacobs (CS, UMIACS); Darcy Lewis (iSchool, doctoral student); Cyndy Parr (EOL); Jenny Preece (iSchool); Dana Rotman (iSchool, Doctoral student); Erin Stewart (iSchool Masters student); Eric (CS, Undergrad student)

  3. What we will talk about… • Research aims • Encyclopedia of Life (EOL) • Scientists, citizen scientists, enthusiasts • Identifying leaves: • Machine vision approach • Odd Leaf Out • Field Mission Games • Questions and Discussion

  4. BioTracker system architecture

  5. First research question • What are the most effective strategies for motivating enthusiasts and experts to voluntarily contribute and collaborate?

  6. The biodiversity crisis

  7. The biodiversity crisis Global collapse of commercial fisheries by 2053

  8. A crisis in science

  9. Citizen science Photo credit: Mary Keim NA Butterfly Association Fourth of July Count Photo credit: Cornell Univ. Audubon Christmas Bird Count

  10. Powerful citizen science data http://ebird.org

  11. More species, less training Bioblitzes Geocaching

  12. Imagine an electronic page for each species of organism on Earth. The Encyclopedia of Life

  13. EOL is a content curation community Content providers Databases Journals LifeDesks Public contributions Curating Commenting Tagging http://www.eol.org

  14. EOL statistics • 100+ partner databases700 curators/1000s contributors/46,000 members • 2.8 million pages500 thousand pages with Creative Commons content • Over 2 million data objects and >1 million pages with links to research literature • Traffic in past year: 1.7 million unique users, 6.2 million page views

  15. Scientists and volunteers "Scientists often have an aversion to what nonscientists say about science” (Salk, 1986) Collaboration is based on several factors: • Shared vocabulary, practices, and meanings • Mutual recognition of knowledge, competency, and prestige • Motivation to collaborate

  16. Motivations for participation Participation in social activities stems from personal and collective reasons Collectivism Principalism Egoism Altruism Batson, Ahmad, Tsang, 2002

  17. Pilot study – scientists’ motivational factors Faculty/ research position

  18. Pilot study – volunteers’ motivational factors Years of experience

  19. Second research question • How can a socially intelligent system be used to direct human effort and expertise to the most valuable collection and classification tasks?

  20. Mobile devices for plant species ID • Build new digital collections • Image-based search to assist in identification • Make this available on mobile devices • Use this platform to build user communities • Collaboration with dozens of people at Columbia University, the Smithsonian NMNH, and UMD.

  21. New images For EOL, people using mobile devices, highest quality images of live specimens. For Botanists: digitize 90,000+ Type Specimens at Smithsonian And for machines, images that capture leaf diversity

  22. Computer Vision for species ID • Use a photo to search a • data set of known • species. • Goal is to assist the user, • not make identification • fully automatic. • Take a photo of a leaf on a plain background.

  23. 2. Automatic segmentation and stem removal • Segmentation relies on value and saturation of pixels, EM algorithm, domain knowledge.

  24. Must handle diversity of shapes Humulusjaponicus Ipomoea lacunosa

  25. 3. Build shape descriptors • Inner Distance Shape Context • Multiscale histograms of curvature

  26. 4. Search data set

  27. System accuracy

  28. Incorporating games into the Biotracker platform Using games to direct human effort and computational resources towards species identification and classification • Data Validation Games • Field Data Collection Games

  29. Odd Leaf Out Using computer games for data validation and algorithm refinement

  30. Odd Leaf Out

  31. Odd Leaf Out

  32. Biotracker field missions Developing mobile-social games that motivate citizens to collect and validate useful scientific data Smart Phone as Data Collection Tool Inspirations • Geocaching • Letterboxing • BioBlitz • SFZero • Project Noah Biotracker Missions

  33. Biotracker field missions Next steps - prototyping and user testing Low fidelity prototypes Field testing at UMD

  34. Questions and Discussion www.biotrackers.net

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