From literature to biodiversity data: mining arthropod organismal and ecological traits with machine learning

Preprint ARPHA Preprints https://doi.org/10.3897/arphapreprints.e153174 (18 Mar 2025) https://doi.org/10.3897/arphapreprints.e153174 (18 Mar 2025) Published in: Biodiversity Data Journal https://doi.org/10.3897/BDJ.13.e153070 Other versions: - Preprint InfoPreprint Info - CiteCite - MetricsMetrics - CommentComment - RelatedRelated - CitedCited ARPHA Preprints doi: 10.3897/arphapreprints.e153174 First posted 18 Mar 2025 Authors Dalle Molle Institute for Artificial Intelligence Research (IDSIA USI-SUPSI), Lugano, Switzerland SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Dalle Molle Institute for Artificial Intelligence Research (IDSIA USI-SUPSI), Lugano, Switzerland SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Plazi, Bern, Switzerland Digital Society Initiative, University of Zurich, Zurich, Switzerland Dalle Molle Institute for Artificial Intelligence Research (IDSIA USI-SUPSI), Lugano, Switzerland SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Robert M Waterhouse - Corresponding author Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Conflict of interest The authors have declared that no competing interests exist. Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal. Supporting agencies SNF - Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.