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Abstract Detail

Conference Wide

Weiblen, George [1].

Crowd sourcing and citizen science: engaging the public in natural history collections digitization.

Developing websites that allow everyone to participate in collections-based research online is a major opportunity to simultaneously engage in educational outreach and make natural history information more accessible. This workshop will introduce crowd-sourced approaches to transcribing herbarium specimen labels through the Macrofungi Collection Consortium. This effort to rapidly database 1.4 million mycological specimens explores (1) current crowd-sourcing approaches, (2) how to effectively engage public audiences in citizen science, and (3) challenges for data management and publication. Hands-on activities will demonstrate how MaCC digitization is coordinated using the Symbiota platform and how label transcription is performed through the Notes from Nature project at “All-star” citizen scientists will share the rewards of participating in collections digitization. Mari Roberts will discuss how to attract diverse audiences including dedicated naturalists, school groups, corporate volunteers, master gardeners, and mechanical turks to digitization projects. By transcribing digital images of specimen labels from fungi, workshow participants will test-drive Notes from Nature and Symbiota as alternative crowd-sourcing platforms. Approaches to reconciling independent observations and validating records will be reviewed and will afford for a discussion of trade-offs between the accessibility, efficiency, accuracy, and completeness of transcription. Opportunities to form new projects through the Worldwide Engagement for Digitizing Biocollections intiative will also be presented. This workshop is supported by the NSF ADBC MaCC TCN. Participants are encouraged to bring laptops or touch pads.

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1 - University of Minnesota, Herbarium

none specified

Presentation Type: Workshop
Session: WS08A
Location: Salon 13/The Shaw Conference Centre
Date: Sunday, July 26th, 2015
Time: 1:00 PM
Number: WS08A001
Abstract ID:197
Candidate for Awards:None

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