crowdsourcing citizen science data quality with a human-computer learning network
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Crowdsourcing Citizen Science Data Quality with a
Human-Computer Learning Network
Wiggins, Gerbracht, Lagoze, Yu, Wong, & Kelling
7 December, 2012 ~ Lake Tahoe, NVWorkshop on Human Computation for Science and Computational Sustainability
Crowdsourcing Scientific Work
eBird
• Online checklist program for bird abundance & distribution
• Data (mostly) from recreational birders; used widely
• Over 100 million records & growing eBird observations per month
Data Quality
Dogbird Catbird
Data Quality
Dogbird Catbird
X
The eBird HCLN
S Kelling, C Lagoze, W-K Wong, J Yu, T Damoulas, J Gerbracht, D Fink, C Gomes. 2012. eBird: A Human/Computer Learning Network to Improve Biodiversity Conservation and Research. Artificial Intelligence.
Emergent Filters
Kelling, S., J. Yu, J. Gerbracht, and W. K. Wong. 2011. Emergent Filters: Automated Data Verification in a Large-scale Citizen Science Project. Proceedings of the IEEE eScience Conference.
Modeling Expertise
Yit$Zi$
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Xi$ Wit$
t=1,…,Ti$
Environmental Covariates Detection
Occupancy (Latent)
Detection Covariates
oi dit
Yit Zi
i=1,…,N
Xi
t=1,…,Ti
Ej Uj j=1,…,M
Wit$
Expertise Covariates
Expertise vj
oi dit, fit
Occupancy-Detection-Expertise
Average Detection Probabilities
Yu, J., W. K. Wong, and R. A. Hutchinson. 2010. Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling. IEEE 10th International Conference on Data Mining (ICDM),
Hard-to-detect birds Common birds
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Blue Ja
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uthatc
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Great B
lue H
eron
Brown T
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Blue-he
aded
Vireo
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gh-w
inged
Swallow
Wood T
hrush
Emergent Filters + Expertise
Spizella passerina
Emergent Filters + Expertise
Spizella passerina
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Emergent Filters + Expertise
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Emergent Filters + Expertise
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8)Jan" 8)Feb"8)Mar" 8)Apr" 8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec"
Improving Spatial Coverage
Locations in NY with eBird submissions in 2009
Improving Spatial Coverage
Areas with enough data for emergent filters
Future Work
• Preliminary studies integrated into eBird for better data quality on multiple levels
• Resulting human-computer learning network will use eBird data in new ways
• Evaluation of motivation, learning, and skills related to expertise ranking & birding routes
Thanks!
www.ebird.org
@AndreaWiggins
andrea.wiggins@cornell.edu
www.andreawiggins.com
Acknowledgements
• Leon Levy Foundation
• Wolf Creek Foundation
• National Science Foundation Grants OCI-0830944, CCF-0832782, ITR-0427914, DBI-1049363, DBI-0542868, DUE-0734857, IIS-0748626, IIS-0844546, IIS-0612031, IIS-1050422, IIS-0905385, IIS-0746500, IIS-1209589, AGS-0835821, CNS-0751152, CNS-0855167.
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