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Reproducible Data and R Code for a Meta-analysis and Machine-learning Study of Aged Micro- and Nanoplastic Phytotoxicity
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Scientists combined data from 51 studies to see how "weathered" microplastics, the tiny plastic bits broken down by sun and weather, affect plant health differently than fresh plastic. This matters because microplastics are entering our food supply through crops, and understanding how environmental aging changes their toxicity helps researchers better predict risks to the food we eat.
This dataset contains the curated analysis data and R scripts supporting a meta-analysis and machine-learning investigation of the phytotoxic effects of environmentally aged micro- and nanoplastics. The prepared dataset comprises 1,978 observations from 51 studies. The machine-learning workflow evaluates two primary effect-size targets: virgin micro- and nanoplastics relative to controls (Virgin-ES) and aged relative to virgin micro- and nanoplastics (AVV-ES). Aged-ES is retained for meta-analysis and supplementary consistency checks. The repository includes analysis scripts, execution instructions, software environment documentation, and reproducibility reports. It supports reproducibility from the prepared analysis dataset but does not reproduce the original literature search or manual extraction process.
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Reproducible Data and R Code for a Meta-analysis and Machine-learning Study of Aged Micro- and Nanoplastic Phytotoxicity
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Scientists combined data from 51 studies to see how tiny plastic particles, especially ones broken down by sun and weather, affect plant growth. This resource itself is mainly a dataset and code for other researchers, but the underlying research helps us understand whether "aged" microplastics in soil are more or less harmful to crops than fresh ones, which matters since plants are part of how these particles could enter our food supply.
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