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Fast, scalable, and automated identification of articles for biodiversity and macroecological datasetsNARA Subscribed
Aim 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 art...
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