1. Melding human and machine capabilities to document the worlds 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. 7. The biodiversity crisis
8. The biodiversity crisis
Global collapse of commercial fisheries by 2053
9. A crisis in science
10. Citizen science
Photo credit: Mary Keim
NA Butterfly Association
Fourth of July Count
Photo credit: Cornell Univ.
Audubon Christmas Bird Count
11. Powerful citizen science data
http://ebird.org
12. More species, less training
Bioblitzes
Geocaching
13. The Encyclopedia of Life
Imagine an electronic page for each
species of organism on Earth.
14. EOL is a content curation community
Content providers
Databases
Journals
LifeDesks
Public contributions
Curating
Commenting
Tagging
http://www.eol.org
15.

100+ partner databases700 curators/1000s contributors/46,000 members

16. 2.8 million pages500 thousand pages with Creative Commons content 17. Over 2 million data objects and >1 million pages with links to research literature 18. Traffic in past year: 1.7 million unique users, 6.2 million page viewsEOL statistics
19. 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
20. Motivations for participation
Participation in social activities stems from personal
and collective reasons
Collectivism
Principalism
Egoism
Altruism
Batson, Ahmad, Tsang, 2002
21. Pilot study scientists motivational factors
Faculty/
research position
22. Pilot study volunteers motivational factors
Years of experience
23. 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?
24. 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.
25. 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
26. 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.
27. 2. Automaticsegmentationand
stem removal
Segmentation relies on value and saturation of pixels, EM algorithm, domain knowledge.
28. Must handle diversity of shapes
Humulusjaponicus
Ipomoea lacunosa
29. 3. Build shape descriptors

Inner Distance Shape Context

30. Multiscale histograms of curvature