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公民科学:趋势分析中消除观察者偏差的最佳实践。

Citizen science: best practices to remove observer bias in trend analysis.

作者信息

Gonsamo Alemu, D'Odorico Petra

机构信息

Department of Geography and Program in Planning, University of Toronto, Ontario, M5S 3G3, Canada,

出版信息

Int J Biometeorol. 2014 Dec;58(10):2159-63. doi: 10.1007/s00484-014-0806-8. Epub 2014 Mar 5.

Abstract

Citizen science, time series records over long periods of time, and wide geographic areas offer many opportunities for scientists to answer questions that would otherwise be impractical to investigate. Citizen scientists currently play active roles in a wide range of ecological projects; however, observer biases such as varying perception of events or objects being observed and quality of observations present challenges to successfully derive interannual variability and trend statistics from time series records. It is recommended that citizen science records, particularly those involving events such as plant phenology, should not be directly averaged across sites. The interannual variability expressed as an anomaly and trend expressed as a regression slope should be calculated for each site. Only the site level anomaly and regression slopes should be averaged to suppress observer biases.

摘要

公民科学、长时间的时间序列记录以及广泛的地理区域为科学家提供了许多机会,以回答那些否则将难以实际调查的问题。公民科学家目前在广泛的生态项目中发挥着积极作用;然而,诸如对所观察事件或物体的不同认知以及观察质量等观察者偏差,给从时间序列记录中成功得出年际变异性和趋势统计数据带来了挑战。建议公民科学记录,尤其是那些涉及植物物候等事件的记录,不应直接在各地点进行平均。应针对每个地点计算以异常值表示的年际变异性和以回归斜率表示的趋势。仅应将地点层面的异常值和回归斜率进行平均,以抑制观察者偏差。

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