Dataset Card for BIOSCAN-5M

Overview

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size.

Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes:

• RGB image
• DNA barcode sequence
• Barcode Index Number (BIN)
• Biological taxonomic classification
• Geographical information
• Specimen size

Copyright and License

The images included in the BIOSCAN-5M dataset available through this repository are subject to copyright and licensing restrictions shown in the following:

• Copyright Holder: CBG Photography Group
• Copyright Institution: Centre for Biodiversity Genomics (email:CBGImaging@gmail.com)
• Photographer: CBG Robotic Imager
• Copyright License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
• Copyright Contact: collectionsBIO@gmail.com
• Copyright Year: 2021

Dataset Sources

• Dataset website: https://biodiversitygenomics.net/5M-insects/
• Google Drive: https://drive.google.com/drive/u/1/folders/1Jc57eKkeiYrnUBc9WlIp-ZS_L1bVlT-0
• GitHub repository: https://github.com/zahrag/BIOSCAN-5M
• Zenodo: https://zenodo.org/records/11973457
• Kaggle: https://www.kaggle.com/datasets/zahragharaee/bioscan-5m/data
• Paper: https://arxiv.org/abs/2406.12723

Citation

@misc{gharaee2024bioscan5m,
    title={{BIOSCAN-5M}: A Multimodal Dataset for Insect Biodiversity},
    author={Zahra Gharaee and Scott C. Lowe and ZeMing Gong and Pablo Millan Arias
        and Nicholas Pellegrino and Austin T. Wang and Joakim Bruslund Haurum
        and Iuliia Zarubiieva and Lila Kari and Dirk Steinke and Graham W. Taylor
        and Paul Fieguth and Angel X. Chang
    },
    year={2024},
    eprint={2406.12723},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    doi={10.48550/arxiv.2406.12723},
}