Title |
Taking a ‘Big Data’ approach to data quality in a citizen science project
|
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Published in |
Ambio, October 2015
|
DOI | 10.1007/s13280-015-0710-4 |
Pubmed ID | |
Authors |
Steve Kelling, Daniel Fink, Frank A. La Sorte, Alison Johnston, Nicholas E. Bruns, Wesley M. Hochachka |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 30% |
Australia | 1 | 10% |
Unknown | 6 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 90% |
Scientists | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 1% |
United Kingdom | 4 | 1% |
Spain | 2 | <1% |
Germany | 1 | <1% |
Colombia | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Unknown | 351 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 68 | 19% |
Student > Ph. D. Student | 64 | 18% |
Researcher | 55 | 15% |
Student > Bachelor | 31 | 8% |
Other | 21 | 6% |
Other | 54 | 15% |
Unknown | 72 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 89 | 24% |
Environmental Science | 70 | 19% |
Computer Science | 36 | 10% |
Social Sciences | 19 | 5% |
Earth and Planetary Sciences | 12 | 3% |
Other | 46 | 13% |
Unknown | 93 | 25% |