Author

Daan R. Scheepens

University College LondonORCID 0009-0007-7574-9621Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:large language model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:metagenomics· 1

Frequent Co-Authors

Tim NewboldiD· 2 shared Joseph W. MillardiD· 1 shared Maxwell J. FarrelliD· 1 shared Robin FreemaniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 4 mutual co-author links

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2 shared papersEcology

Tim NewboldORCID

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2 papers

Recent·Most cited

EcologybioRxivPreprintOct 1, 2026

DAPHNE: A global database of pest herbivores and their natural enemies

Daan R. Scheepens, Robin Freeman, Tim Newbold

The paper constructs a global database of pest herbivores and their natural enemies by using a large language model to extract species interactions from scientific literature.

DOI 10.64898/2026.09.28.754864

large language modeldatabase / resource

𝕏 PostBluesky

EcologyMethods in Ecology and EvolutionPeer-reviewedMay 20, 2024

Large language models help facilitate the automated synthesis of information on potential pest controllers

Daan R. Scheepens, Joseph W. Millard, Maxwell J. Farrell, Tim Newbold

This paper evaluates GPT-4's ability to extract pest and controller information from ecological literature, showing high accuracy in species and location extraction.

36 citations

proteomicsmetagenomics

𝕏 PostBluesky