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  # Dataset Card for BIOSCAN-30k
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- <!-- Provide a quick summary of the dataset. -->
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  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30000 samples.
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  ## Dataset Details
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- ### Dataset Description
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  The BIOSCAN-5M dataset is a multimodal collection of over 5.15 million arthropod specimens (98% insects), curated to advance biodiversity monitoring through machine learning. It expands the earlier BIOSCAN-1M dataset by including high-resolution images, DNA barcode sequences, taxonomic labels (phylum to species), geographical locations, specimen size data, and Barcode Index Numbers (BINs). Designed for both closed-world (known species) and open-world (novel species) scenarios, it supports tasks like taxonomic classification, clustering, and multimodal learning.
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  **This dataset is a randomly chosen subset of 30,000 samples across all splits from the Cropped 256 dataset**
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- Key Features:
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  * Images: 5.15M high-resolution microscope images (1024×768px) with cropped/resized versions.
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  * Size Metadata: Pixel count, area fraction, and scale factor for specimens.
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  - **Curated by:** International consortium led by the Centre for Biodiversity Genomics, University of Guelph, University of Waterloo, and Simon Fraser University.
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  - **Funded by :** Government of Canada’s New Frontiers in Research Fund (NFRF), CFI Major Science Initiatives, Walder Foundation, NVIDIA Academic Grant.
 
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  # Dataset Card for BIOSCAN-30k
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  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30000 samples.
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  ## Dataset Details
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  The BIOSCAN-5M dataset is a multimodal collection of over 5.15 million arthropod specimens (98% insects), curated to advance biodiversity monitoring through machine learning. It expands the earlier BIOSCAN-1M dataset by including high-resolution images, DNA barcode sequences, taxonomic labels (phylum to species), geographical locations, specimen size data, and Barcode Index Numbers (BINs). Designed for both closed-world (known species) and open-world (novel species) scenarios, it supports tasks like taxonomic classification, clustering, and multimodal learning.
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  **This dataset is a randomly chosen subset of 30,000 samples across all splits from the Cropped 256 dataset**
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+ #### Key Features:
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  * Images: 5.15M high-resolution microscope images (1024×768px) with cropped/resized versions.
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  * Size Metadata: Pixel count, area fraction, and scale factor for specimens.
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+ ### Dataset Description
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  - **Curated by:** International consortium led by the Centre for Biodiversity Genomics, University of Guelph, University of Waterloo, and Simon Fraser University.
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  - **Funded by :** Government of Canada’s New Frontiers in Research Fund (NFRF), CFI Major Science Initiatives, Walder Foundation, NVIDIA Academic Grant.