Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project

Abstract Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp. ) breeding colonies...

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Bibliographic Details
Published in:Scientific Data
Main Authors: Jones, Fiona M., Allen, Campbell, Arteta, Carlos, Arthur, Joan, Black, Caitlin, Emmerson, Louise M., Freeman, Robin, Hines, Greg, Lintott, Chris J., Macháčková, Zuzana, Miller, Grant, Simpson, Rob, Southwell, Colin, Torsey, Holly R., Zisserman, Andrew, Hart, Tom
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2018
Subjects:
Library and Information Sciences
Statistics, Probability and Uncertainty
Computer Science Applications
Education
Information Systems
Statistics and Probability
Antarctic
The Antarctic
Antarctic Peninsula
South Shetland Islands
Antarc*
Online Access:http://dx.doi.org/10.1038/sdata.2018.124
http://www.nature.com/articles/sdata2018124.pdf
http://www.nature.com/articles/sdata2018124
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Summary:Abstract Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp. ) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch , and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machine learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.

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