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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Assessing data quality in citizen science
AU - Kosmala, Margaret
AU - Wiggins, Andrea
AU - Swanson, Alexandra
AU - Simmons, Brooke
N1 - This is the peer reviewed version of the following article: Assessing data quality in citizen science. Frontiers in Ecology and the Environment. doi: 10.1002/fee.1436 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/fee.1436/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
U2 - 10.1002/fee.1436
DO - 10.1002/fee.1436
M3 - Journal article
VL - 14
SP - 551
EP - 560
JO - Frontiers in Ecology and the Environment
JF - Frontiers in Ecology and the Environment
SN - 1540-9295
IS - 10
ER -