Background & History
MetaPlantCode is a Biodiversa+ BiodivMon funded European research project that aims to improve plant biodiversity monitoring through DNA metabarcoding. By combining field research, lab work, bioinformatics, and data standardization, the project creates scalable tools and resources for use in science and nature conservation. Our objectives include: testing pan-European case studies, proposing best practice recommendations, optimizing workflows and pipelines, creating and improving plant reference databases, mobilizing and publishing FAIR+ data and tools, implementing AI for improved species identification, strengthening knowledge transfer and stakeholder engagement across sectors.
Our Team
Literature & Taxonomy. Responsible for Work Package 5
Data & Sampling. Responsible for Work Package 1
Coordinator of MetaPlantCode. Responsible for Work Package 6
Dag Endresen
Implement GBIF DNA-derived data publishing pipelines (eDNA occurrences to data) Responsible for Work Package 4
Work Package 4 - Databases & Standards
Coordinator of the GBIF Portuguese Node
Panagiotis Madesis
WP2 - Reference Database
Auguste Gardette
Machine Learning Work Package 3
Eugeni Belda Cuesta
Machine Learning Work Package 3
Work Package 5
Machine Learning - Work Package 3
Mihael Cristin Ichim
Data & Sampling - Work Package 1
Ancuta Cristina Manolica
Data & Sampling - Work Package 1
Madalina Oana Popa
Data & Sampling - Work Package 1
Stephanie J. Swenson
Work Package 1 and Work Package 6
Work Package 1
Work Package 1
Work Package 1
Donat Agosti
Literature & Taxonomy - Work Package 4
Edi Prifti
Literature & Taxonomy - Work Package 4
Daniel Lundin
Expert in metabarcoding pipelines
Work Package 1
Work Package 6
Vision & Mission
The goal of MetaPlantCode is to develop methods that allow faster, more scalable, and standardized biodiversity assessments of plant environmental DNA. We investigate how plant metabarcoding can be effectively used for long-term biodiversity monitoring, identify which molecular and analytical techniques provide the most reliable results, and work on improving reference databases to enable precise species identification. We link vegetation literature with taxonomic data by AI text analysis and develop and train AI models for DNA analysis and taxonomy. Ultimately, we aim to ensure that our findings contribute to enhanced plant metabarcoding analyses for research and nature conservation to better inform policy decisions.
What do we research?
How do we work?
Our work combines advanced molecular and digital technologies using complex environmental DNA samples to test bioinformatic pipelines and standardize laboratory protocols. Our bioinformatics pipelines perform quality filtering and species detection integrating public databases such as GBIF. Additionally, we mobilize and semantically enrich floristic literature and adhere to FAIR data principles throughout all stages of our research.
MetaPlantCode is committed to the FAIR data principles – making data Findable, Accessible, Interoperable, and Reusable. These principles guide how we manage, publish, and share our research data across all work packages.
- Findable: Data is assigned a unique identifier and described with rich metadata.
- Accessible: Data is retrievable using standardized protocols with clear usage licenses.
- Interoperable: Data uses shared vocabularies and formats to integrate with other data sources.
- Reusable: Data is well-documented and published under clear usage terms.