Taking a 'Big Data' approach to data quality in a citizen science project

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• 作者：Steve Kelling ; Daniel Fink ; Frank A. La Sorte ; Alison Johnston ; Nicholas E. Bruns…
• 关键词：Biodiversity monitoring ; Citizen science ; eBird ; Data quality ; Species distribution models
• 刊名：AMBIO
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：44
• 期：4-supp
• 页码：601-611
• 全文大小：1,745 KB
• 刊物类别：Earth and Environmental Science
• 出版者：Springer Netherlands
• ISSN：1654-7209

文摘

Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a ‘Big Data’ approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird’s data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a ‘sensor calibration’ approach to measure individual variation in eBird participant’s ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.