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

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Presentation at the Human Computation for Science and Computational Sustainability workshop at NIPS 2012 in Lake Tahoe, NV.

Presentation at the Human Computation for Science and Computational Sustainability workshop at NIPS 2012 in Lake Tahoe, NV.

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Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Network Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Network Presentation Transcript

  • Crowdsourcing Citizen Science Data Quality with aHuman-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 QualityDogbird Catbird
  • Data QualityXDogbird Catbird
  • The eBird HCLNS 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 FiltersKelling, 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 ExpertiseEnvironmental Occupancy Detection Covariates (Latent) Detection Covariates oi dit Xi$ Zi$ Yit$ Wit$ t=1,…,Ti$ i=1,…,N$
  • Occupancy-Detection-ExpertiseExpertise ExpertiseCovariates vj Uj Ej j=1,…,M oi dit, fit Xi Zi Yit Wit$ t=1,…,Ti i=1,…,N
  • Average Detection Probabilities0.200.150.100.050.00 h o w n er al y h tc re o Ja s lo in h ru er ha Vi as-0.05 al rd ue Th H r ut Sw Ca d Th Bl e N de d lu oo n ed n ea d B er ow te W g -h at th as in Br ue re or re w G Bl N h- -b ug te hi Ro W n er th or N Common birds Hard-to-detect birds 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),
  • Emergent Filters + Expertise Spizella passerina
  • Emergent Filters + Expertise Spizella 50" 45" passerina 40" 35" 30" 25" 20" 15" 10" 5" 0" 8)Jan" 8)Feb"8)Mar" 8)Apr" 8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec"
  • Emergent Filters + Expertise Spizella 50" 45" passerina 40" 35" 30" 25" 20" 15" 10" 5" 0" 8)Jan" 8)Feb"8)Mar" 8)Apr" 8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec"50"45"40"35"30"25"20"15"10" 5" 0" 8)Jan" 8)Feb"8)Mar" 8)Apr"8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec"
  • Emergent Filters + Expertise Spizella 50" 45" passerina 40" 35" 30" 25" 20" 15" 10" 5" 0" 8)Jan" 8)Feb"8)Mar" 8)Apr" 8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec"50" 50"45" 45"40" 40"35" 35"30" 30"25" 25"20" 20"15" 15"10" 10" 5" 5" 0" 0" 8)Jan" 8)Feb"8)Mar" 8)Apr"8)May" 8)Jun" 8)Jul" 8)Aug" 8)Sep" 8)Oct" 8)Nov" 8)Dec" 1(Jan" 1(Feb"1(Mar" 1(Apr"1(May" 1(Jun" 1(Jul" 1(Aug" 1(Sep" 1(Oct" 1(Nov"1(Dec"
  • Improving Spatial CoverageLocations in NY with eBird submissions in 2009
  • Improving Spatial CoverageAreas 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@AndreaWigginsandrea.wiggins@cornell.eduwww.andreawiggins.comAcknowledgements • 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.