Data from: Determining bad actors: A linear mixed effects model approach to elucidate behavioral toxicity of metal mixtures in drinking water

Mixtures of chemical contaminants can pose a significant health risk to humans and wildlife, even at levels considered safe for each individual chemical. There is a critical need to develop statistical methods to evaluate the drivers of toxic effects in chemical mixtures (i.e., bad actors) from exposure studies. Here, we develop a hierarchical modeling framework to disentangle the toxicity of complex metal mixtures. For this, we used a dataset generated in Babich et al., 2021 that assessed impacts on zebrafish larval development from exposure to drinking water sampled from 92 wells across Maine and New Hampshire, USA. We sought to leverage the more complex data structure of larval behavioral tests to estimate effects of individual metal elements and their combinations on zebrafish development. Metals identified as drivers of neurodevelopmental toxicity were experimentally evaluated using zebrafish larvae to validate the LMM findings from the complex metal mixtures screening dataset. Datasets herein include water sample parameter estimates, individual and paired metals comparisons, metal metrics, and experimental test results with zebrafish.