license: cc-by-3.0
language:
- en
size_categories:
- 1M<n<10M
pretty_name: BIOSCAN-5M
Dataset Card for BIOSCAN-5M
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, We presents 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.
Dataset Details
Dataset Description
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
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
- Code repository: https://github.com/zahrag/BIOSCAN-5M
- Kaggle: https://www.kaggle.com/datasets/zahragharaee/bioscan-5m/data
- Paper: https://arxiv.org/abs/2406.12723
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
BibTeX:
@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},
}