Understanding broad‐scale ecological patterns and processes is necessary if we are to mitigate the consequences of anthropogenically driven biodiversity degradation. However, such analyses require large datasets and current data collation methods can be slow, involving extensive human input. Given rapid and ever‐increasing rates of scientific publication, manually identifying data sources among hundreds of thousands of articles is a significant challenge, which can create a bottleneck in the generation of ecological databases.

中文翻译：

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快速，可扩展和自动识别生物多样性和宏观生态数据集的文章

如果我们要减轻人为驱动的生物多样性退化的后果，必须了解广泛的生态模式和过程。但是，此类分析需要大型数据集，并且当前的数据整理方法可能很慢，需要大量的人工输入。鉴于科学出版的速度越来越快，手动识别数十万篇文章中的数据源是一个巨大的挑战，这可能会成为生态数据库生成的瓶颈。