The integration of massive, crowdsourced citizen-science datasets has revolutionized ecological and environmental research, but it also introduces severe geographic and socioeconomic sampling biases based on where participants live and travel. For faculty teaching or conducting research in computational modeling, how do we best train students to detect and algorithmically correct for these population-density artifacts? At what point does a dataset require too much statistical normalization to remain a reliable proxy for true biodiversity?
