构建全球生物多样性多模态数据集，支持精细生态研究。
BioCube: A Multimodal Dataset for Biodiversity Research
- 整合图像、音频、环境DNA等多源数据，实现跨模态信息融合。
- 覆盖2000–2020年全球范围，空间分辨率高，地理坐标统一为WGS84。
- 适用于生态建模、物种分布预测及气候变化影响分析的研究者。
生物多样性研究需要完整详尽的信息以在不同尺度上分析生态系统动态。数据驱动方法（如机器学习）在生态学和生物多样性研究中日益流行，为建模提供新路径。但要获得准确结果，需依赖大规模、经过筛选且多模态的数据集，具备精细的空间与时间分辨率。本文提出BioCube，一个用于生态与生物多样性研究的多模态、细粒度全球数据集。该数据集包含通过图像、音频记录和描述获取的物种观测数据，以及环境DNA、植被指数、农业与森林指标、土地使用信息和高分辨率气候变量。所有数据均基于WGS84地理坐标系统进行空间对齐，时间跨度为2000至2020年。数据集可在https://huggingface.co/datasets/BioDT/BioCube 获取，相关采集与处理代码位于https://github.com/BioDT/bfm-data。
原文摘要 · Abstract (English)
Biodiversity research requires complete and detailed information to study ecosystem dynamics at different scales. Employing data-driven methods like Machine Learning is getting traction in ecology and more specific biodiversity, offering alternative modelling pathways. For these methods to deliver accurate results there is the need for large, curated and multimodal datasets that offer granular spatial and temporal resolutions. In this work, we introduce BioCube, a multimodal, fine-grained global dataset for ecology and biodiversity research. BioCube incorporates species observations through images, audio recordings and descriptions, environmental DNA, vegetation indices, agricultural, forest, land indicators, and high-resolution climate variables. All observations are geospatially aligned under the WGS84 geodetic system, spanning from 2000 to 2020. The dataset is available at https://huggingface.co/datasets/ BioDT/BioCube, the acquisition and processing code base at https://github.com/BioDT/bfm-data.
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