The METAPLANTCODE project is dedicated to advancing and optimizing pan-European case studies on metabarcoding. The project's objectives include providing best practice recommendations, optimizing analysis pipelines for species identification, and creating user-friendly reference databases. To accomplish these objectives, METAPLANTCODE will identify and address gaps in current methodologies, publish best practice documents on FAIR (Findable, Accessible, Interoperable, Reusable) data publishing for plant metabarcode data to GBIF (Global Biodiversity Information Facility) and the INSDC (International Nucleotide Sequence Database Collaboration), and implement ELIXIRcompatible multimodal deep learning (DL) models in novel tools for standalone metabarcoding analyses using various data sources.

A significant focus of the project is enhancing species identification accuracy through GBIF records and metadata. This involves mapping regional, national, and international botanical taxonomic checklists, red lists, and floras to the Catalogue of Life (COL) via the COL ChecklistBank. Additionally, taxonomic and floristic literature will be semantically enriched with new entity recognition and relationship extraction modules, supporting the enhanced identification of species through domain-specific descriptive and phenotypic features. An interface will link taxonomic names to treatments, identify homonyms and synonyms, and facilitate the conversion and annotation of floras, red lists, and ecological