“An interoperability framework for biodiversity monitoring in Europe”
Published: October 2026 | DOI: 10.5281/zenodo.23399501
Biodiversity monitoring produces valuable data for research, policy and practice, but differences in how organisations describe, structure and share datasets make this information difficult to discover and integrate. This report presents an interoperability framework to support more effective data access, exchange and reuse across institutions and countries.
Developed through a review of existing approaches and expert workshops, the framework builds particularly on the European Open Science Cloud (EOSC) Interoperability Framework and the Cross-Domain Interoperability Framework (CDIF). It supports the implementation of the FAIR principles and aims to strengthen connections between biodiversity monitoring organisations, research infrastructures and global initiatives such as GBIF.
The report sets out 24 recommendations, combining general principles with technical examples and practical steps for implementation. The framework is intended to evolve through application and developments in standards and technologies.
What the framework recommends
The recommendations address four complementary technical areas:
- Use shared data models, vocabularies and formats. Compatible structures and common definitions help ensure that information from different sources can be interpreted consistently. The framework recommends building on existing biodiversity standards and making the relationships between different models and vocabularies explicit and reusable.
- Publish standardised, machine-readable metadata. Dataset descriptions should provide information about their content, provenance, licences and access conditions in ways that both people and software can understand.
- Assign persistent identifiers. Datasets and metadata records should have globally unique, persistent and resolvable identifiers so they can be reliably referenced, located and linked over time.
- Provide documented interfaces for automated access. Application programming interfaces (APIs) should enable software to retrieve data and metadata without repeated manual transfers. These interfaces should be documented in machine-readable formats and support appropriate access controls where data are restricted.
Technical scope and recommended standards
The framework combines biodiversity-specific standards with broader approaches to metadata publication, shared vocabularies and automated data access. Its recommendations include:
- Data exchange models: the Darwin Core Data Package (DwC-DP) for biodiversity data exchange and EBVCube for spatial raster data.
- Metadata standards: Ecological Metadata Language (EML) and Data Catalog Vocabulary (DCAT), for documenting datasets, with descriptions enriched by terms from shared vocabularies and ontologies.
- Machine-readable descriptions: JSON-LD for publishing metadata and describing data structures, alongside RDFS, SKOS and OWL for expressing conceptual models, controlled vocabularies and ontologies.
- Publishing vocabularies and ontologies: making these resources available through Semantic Artefact Catalogues, such as EcoPortal or BiodivPortal, and documenting them using the MOD ontology.
- Reusable mappings: the Simple Standard for Sharing Ontological Mappings (SSSOM) for sharing mappings between terms, alongside recommendations to document and publish mappings in repositories such as the Metadata Schema and Crosswalk Registry (MSCR).
- Access conditions: the Open Digital Rights Language (ODRL) for describing permissions, conditions and obligations in machine-readable form where appropriate.
- Advanced data integration: the Data Documentation Initiative – Cross-Domain Integration (DDI-CDI) model for cases requiring more detailed descriptions to support complex data integration.
Putting the framework into practice
The report outlines an implementation sequence that can be summarised in four stages:
- Start with discoverability. Improve existing dataset descriptions, enrich them with shared vocabularies and publish them in formats that can be collected and searched across organisations.
- Make data structures explicit. Describe how datasets are organised and use common exchange formats to help other systems interpret them. This can support interoperability without necessarily replacing an organisation’s internal database.
- Enable automated access. Develop or improve APIs so that software can retrieve datasets, metadata and descriptions of their structure. Existing services and infrastructures can help reduce implementation effort.
- Address more complex data integration. Where datasets are harder to combine, introduce richer descriptions of their structure and meaning, supported by the training and technical expertise needed to use them.
Organisations can adapt this approach to their existing systems, capacities and resources. Successful implementation requires sustained commitment and investment. Collaboration with research infrastructures can help share tools, expertise and costs, while sustained investment in skills, governance and infrastructure is essential to sustain interoperability over time.
