AAAI Publications, Second AAAI Conference on Human Computation and Crowdsourcing

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A Sensor Network Approach to Managing Data Quality in Citizen Science
Andrea Wiggins, Carl Lagoze, Weng-Keen Wong, Steve Kelling

Last modified: 2014-10-14

Abstract


For most citizen science projects in which volunteers act as intelligent sensors, data quality cannot be determined through comparison to an objective ground truth nor through consensus. In this paper we discuss the approach implemented by eBird, based on strategies used in autonomous sensor networks, to address the challenge of establishing the accuracy of humans at the tasks of detection and identification.

Keywords


citizen science; sensor network; data quality; human computation

References


Ediriweera, D. and Marshall, I. 2010. Understanding and managing large sensor networks. Drinking Water Engineering and Science Discussions, 3:149-175.

Kelling, S.; Yu, J.; Gerbracht, J.; and Wong, W. K. 2011. Emergent Filters: Automated Data Verification in a Large-scale Citizen Science Project. In Proceedings of the IEEE eScience 2011 Computing for Citizen Science Workshop.

Rachlin, Y.; Negi, R.; and Khosla, P. K. 2011. The Sensing Capacity of Sensor Networks. Information Theory, IEEE Transactions, 57:1675-1691.

Royle, J. A. 2004. Modeling Abundance Index Data from Anuran Calling Surveys. Conservation Biology, 18:1378-1385.

Schmidt, F. A. (2013). The Good, The Bad and the Ugly: Why Crowdsourcing Needs Ethics. In Proceedings of the Third International Conference on Cloud and Green Computing (CGC), 531-535.

Zhang, Y.; Meratnia, N.; and Havinga, P. 2010. Outlier detection techniques for wireless sensor networks: A survey. Communications Surveys & Tutorials, 12:159-170.


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