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RECODE - Relational Ecological COrpus for Data Extraction.

Vasco V Branco1,2,3, Lidia Pivovarova4, Kari-E J Lintulaakso1

  • 1Finnish Museum of Natural History LUOMUS, University of Helsinki, Helsinki, Finland Finnish Museum of Natural History LUOMUS, University of Helsinki Helsinki Finland.

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View abstract on PubMed

Summary
This summary is machine-generated.

Researchers created RECODE, a new dataset for training AI models to extract species occurrence and trait data from scientific texts. This resource aids ecological research by making unstructured data machine-readable.

Keywords:
biodiversityfunctional diversityinsectslarge language modelsmachine learningnamed entity recognitionnatural language processingoccurrence dataspecies traitsspiders

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Area of Science:

  • Ecology
  • Conservation Biology
  • Taxonomy

Background:

  • Ecological research relies heavily on species location and trait data.
  • This crucial data is often unstructured text in publications, especially for invertebrates.
  • Automated data extraction using AI is challenging due to the need for labeled training corpora.

Purpose of the Study:

  • To introduce RECODE, a manually annotated corpus of ecological and taxonomic literature.
  • To facilitate the training and fine-tuning of AI models for automated data extraction.
  • To address the lack of standard datasets for ecological and taxonomic text mining.

Main Methods:

  • Manual annotation of ecological and taxonomic literature by subject matter experts.
  • Validation of annotations by experts familiar with the traits of spiders and insects.
  • Development of a corpus for training AI models in named entity recognition and relation extraction.

    • Main Results:

    • Creation of RECODE, a manually annotated corpus of scientific literature.
    • The corpus contains validated occurrence and trait data for spiders and insects.
    • RECODE provides a standardized dataset for AI model development in ecology.

    Conclusions:

    • RECODE is a valuable resource for advancing automated data extraction in ecology and conservation.
    • The availability of such annotated corpora is essential for improving AI model performance.
    • This work supports the development of more efficient methods for ecological data mining.