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Microplastics monitoring protocol: natural objects
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This is an educational image dataset and protocol for training students and teachers to distinguish natural objects (biological or mineral particles) from microplastics in surface water samples. It supports a citizen science microplastics monitoring initiative.
This project aims at Beta-testing the "Microplastics monitoring protocol in surface waters" developed by Deakin University in collaboration with Labter-Crea MN and Globe Italia. As part of the project, students and teachers will be trained on a newly-established image dataset that allows recognition of common items expected to be present in surface waters. This dataset includes a selection of naturally occurring particles. These could be of biological or mineral origin.
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Microplastics monitoring protocol: synthetic fibres
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This is an educational image dataset and protocol for training students and teachers to identify synthetic fibers in surface water samples as part of a citizen science microplastics monitoring program. It supports a project developed by Deakin University in collaboration with environmental education partners.
Microplastics monitoring protocol: wool fibres / animal hair fibres
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This is an educational image dataset and protocol for training students and teachers to identify wool and animal hair fibers in surface water samples and distinguish them from synthetic microplastic fibers. It supports a citizen science microplastics monitoring project.
Microplastics monitoring protocol in surface waters
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This citizen science project beta-tested a microplastic monitoring protocol for surface waters developed by Deakin University, training students and teachers to identify common particles in environmental samples using a reference image dataset. Researchers assessed the protocols usability and training outcomes in classroom and field settings. The project supports development of scalable, education-integrated monitoring approaches for freshwater microplastic surveillance.
Microplastics monitoring protocol: cotton and cellulose fibres
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This is an educational image dataset and protocol for training students and teachers to identify cotton and cellulose fibers in surface water monitoring samples and distinguish them from microplastics. It is part of a citizen science monitoring program.
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
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Researchers created a new open-source dataset of microscopy images for training AI models to automatically detect and classify micro- and nanoplastics. The dataset fills an important gap in available tools for microplastic research and provides a foundation for developing faster, more efficient methods to identify plastic contamination across environmental samples.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.