November 25, 2025Open Access

Initial biodiversity data space design with implementation plan

Key Points

The data space architecture enhances biodiversity monitoring and conservation efforts, integrating various data sources.
The design entails cloud-native infrastructure that supports harmonised data for effective policy implementation.
Analysis involves various work packages, focusing on data quality and harmonisation across multiple environments.
Sustainable modular growth supports FAIR data stewardship in biodiversity efforts, highlighting the need for ongoing collaboration.

Abstract

The Biodiversity Meets Data (BMD) project is creating a data space using cloud-native open infrastructure to support biodiversity monitoring, conservation, and policy across terrestrial, freshwater, and marine environments. This document (MS19) presents the initial architecture design for the BMD data space, led by Work Package (WP) 4 and developed in coordination with WP2 (data mobilisation), WP3 (harmonisation), WP5 (Virtual Research Environments), and WP6 (visualisation and Single Access Point). The BMD Data Space will host harmonised, FAIR-aligned data cubes derived from high-throughput and legacy sources. These cubes will support scalable workflows, reproducible analysis, and dynamic policy reporting. Whenever possible, data will be accessed directly from source data providers, with local replication, caching used only when needed for transformation, performance, or reliability and always preserving metadata and provenance. The architecture aligns with the Green Deal Data Space (GDDS) and EOSC (European Open Science Cloud) Interoperability Frameworks, and adopts open lakehouse principles (such as composability and the separation of storage, metadata, and compute layers) to maximise flexibility and interoperability. It builds on cloud-native formats like Parquet, Zarr and GeoParquet, which support scalable, reusable data infrastructure across analytical environments. BMD’s design adopts a dual-catalogue model (GeoNetwork for public metadata, open table catalogues for internal tracking) supports both transparency and operational efficiency. Integration across WPs ensures a cohesive backend for user-facing services, with stakeholder needs informing cube design and access patterns. A flexible approach to data quality will allow permissive integration initially, with tighter validation introduced over time. While infrastructure will be hosted for five years post-project, sustainability strategies remain under discussion. The architecture supports modular growth, cloud portability, and long-term FAIR data stewardship. This milestone reflects the first four months of development and sets a foundation for future implementation and stakeholder collaboration.

Initial biodiversity data space design with implementation plan

Key Points

Abstract

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