Assessing data quality in citizen science
Assessing data quality in citizen science
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DOI：
10.1002/fee.1436
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发表时间：
2016-12-01
影响因子：
10.3
通讯作者：
Simmons, Brooke
中科院分区：
文献类型：
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作者：
Kosmala, Margaret;Wiggins, Andrea;Simmons, Brooke
生态和环境公民科学项目具有巨大的潜力，可以通过生成原本无法生成的数据集来推进科学知识、影响政策和指导资源管理。然而，只有在数据集高质量的情况下才能实现这种潜力。尽管科学家常常对无偿志愿者生成准确数据集的能力持怀疑态度，但越来越多的出版物清楚地表明，不同类型的公民科学项目可以生成与专业人士相当或超过准确性的数据。成功的项目依赖于一套方法来提高数据准确性并消除偏差，包括迭代项目开发、志愿者培训和测试、专家验证、志愿者之间的复制以及系统误差的统计建模。因此，每个公民科学数据集都应该根据项目设计和应用进行单独判断，而不是仅仅因为志愿者生成了它就认为它不合格。
Ecological and environmental citizen-science projects have enormous potential to advance scientific knowledge, influence policy, and guide resource management by producing datasets that would otherwise be infeasible to generate. However, this potential can only be realized if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen-science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen-science dataset should therefore be judged individually, according to project design and application, and not assumed to be substandard simply because volunteers generated it.