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

2. Project Team Arijit Biswas (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?

7. The biodiversity crisis

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

9. A crisis in science

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

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

12. More species, less training Geocaching Bioblitzes

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

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

15. 100+ partner databases 700 curators/1000s contributors/46,000 members 2.8 million pages 500 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 EOL statistics

16. 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

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

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

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

20. 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?

21. 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.

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

23. 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. 1.Take a photo of a leaf on a plain background.

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

25. Ipomoea lacunosa Must handle diversity of shapes Humulus japonicus

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

27. 4. Search data set

28. System accuracy

29. 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

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

31. Odd Leaf Out Research Questions What will make this game more fun? What motivates users to play when the data is imperfect? How can the game assist with algorithm improvement?

32. Odd Leaf Out Next Steps Continue User Testing Analyze Game Play Logs and Surveys Preferred version What aspects give most accurate data Does this provide useful feedback into LeafSnap algorithm Place game on Mechanical Turk for additional data

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

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

35. www.biotrackers.net Questions and Discussion