About us

We will highlight the feasibility of plant metabarcoding through a portfolio of complementary case studies of different source material, serving different monitoring needs and requiring different collection, laboratory and analysis workflows.

We will optimize reference databases, respectively gap identification and specification, provide best practice recommendations for the analysis of plant metabarcoding raw sequence data to taxon-level identification by screening and testing available tools, workflows and filters for datasets of different quality and complexity and develop optimized semi-automated tools and workflows for taxonomic identification via metabarcoding of plants.

We will use deep learning to combine metabarcoding data with biotic-geographic-habitat metadata to increase the accuracy of the taxonomic monitoring in respective geographic regions for monitoring programs, allowing the comparison of datasets over space and time.

We will enable the comparison of plant metabarcoding results at an European scale by cross-walking existing institutional and national SOPs regarding plant metabarcoding and the development of community supported “Best practices on data and metadata capture for FAIR sharing of metabarcoding data with GBIF and the INSDC databases” in collaboration with the GBIF, ELIXIR, BIOSCAN Europe and Biodiversity Genomics Europe (BGE) communities.

We will compile and provide descriptions of vegetation or identification of species occurring at the test sites, enriched with new entity recognition and relationship extraction modules to enhance species identification.

We will manage interoperability between the different work packages, coordinate joint training (internal/external), publications and discussions, develop a stakeholder engagement and outreach strategy and assessment, and coordinate, manage, monitor, and support outreach activities and dissemination.