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

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Presentation transcript:

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

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)

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

BioTracker system architecture

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

The biodiversity crisis

Global collapse of commercial fisheries by 2053 The biodiversity crisis

A crisis in science

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

Powerful citizen science data

More species, less training Geocaching Bioblitzes

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

Content providers Databases Journals LifeDesks Public contributions Curating Commenting Tagging Commenting Tagging EOL is a content curation community

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

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

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

Pilot study – scientists’ motivational factors Faculty/ research position

Pilot study – volunteers’ motivational factors Years of experience

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?

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.

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

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.

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

Ipomoea lacunosa Must handle diversity of shapes Humulus japonicus

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

4. Search data set

System accuracy

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

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

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?

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

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

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

Questions and Discussion