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Assessing data quality in citizen science
Author(s) -
Kosmala Margaret,
Wiggins Andrea,
Swanson Alexandra,
Simmons Brooke
Publication year - 2016
Publication title -
frontiers in ecology and the environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.918
H-Index - 164
eISSN - 1540-9309
pISSN - 1540-9295
DOI - 10.1002/fee.1436
Subject(s) - citizen science , suite , computer science , replication (statistics) , quality (philosophy) , resource (disambiguation) , data science , skepticism , raw data , data quality , political science , business , marketing , mathematics , metric (unit) , statistics , computer network , philosophy , botany , epistemology , law , biology , programming language
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.