Machine learning for improving the quality of citizen science data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/76537413p

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute large quantities of data, data quality is often a concern due to variability in the skills of volunteers. In my thesis, I investigate applying machine learning techniques to improve the quality of data submitted to citizen science projects. The context of my work is eBird, which is one of the largest citizen science projects in existence. In the eBird project, citizen scientists act as a large global network of human sensors, recording observations of bird species and submitting these observations to a centralized database where they are used for ecological research such as species distribution modeling and reserve design. Machine learning can be used to improve data quality by modeling an observer's skill level, developing an automated data verification model and discovering groups of misidentified species.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-12-12T17:01:58Z (GMT) No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) YuJun2013.pdf: 9368871 bytes, checksum: 39e01a8ec00fd4a6085be458a4cabd93 (MD5)
  • description.provenance : Made available in DSpace on 2013-12-12T19:40:59Z (GMT). No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) YuJun2013.pdf: 9368871 bytes, checksum: 39e01a8ec00fd4a6085be458a4cabd93 (MD5) Previous issue date: 2013-12-03
  • description.provenance : Submitted by Jun Yu (yuju@onid.orst.edu) on 2013-12-12T09:24:18Z No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) YuJun2013.pdf: 9368871 bytes, checksum: 39e01a8ec00fd4a6085be458a4cabd93 (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-12-12T19:40:58Z (GMT) No. of bitstreams: 2 license_rdf: 1379 bytes, checksum: da3654ba11642cda39be2b66af335aae (MD5) YuJun2013.pdf: 9368871 bytes, checksum: 39e01a8ec00fd4a6085be458a4cabd93 (MD5)

Relationships

In Administrative Set:
Last modified: 08/14/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items