Technical Concept for Implementation of eLTER Standard Data Products

The deliverable D10.4 presents a comprehensive technical concept for implementing eLTER Data Products within the European Long-Term Ecosystem Research Infrastructure (eLTER RI), developed under the eLTER PLUS project (EU Horizon 2020, Grant No. 871128). It synthesizes workflows from Research Challenges (RCs) in biodiversity loss, biogeochemical cycles, climate-water-food interactions, and socio-ecological systems, drawing on prior deliverables (e.g., D10.2, D10.3) and aligning with the Whole Systems Approach to ensure FAIR (Findable, Accessible, Interoperable, Reusable) compliance and interoperability with European data spaces like EOSC and the Green Deal Data Space. The document outlines a Reference Architecture based on Open Groups Architecture Framework TOGAF standard (10th edition)1 for modelling end-to-end data lifecycles from raw to FAIR data sources, emphasizing metadata management, persistent identifiers (PIDs), provenance tracking, and governance for reproducibility. Data products are defined as bundled, machine-readable resources following the Data Space Support Centre (DSSC) definition (blueprint draft)2, progressing through five processing levels (0-4: raw to modelled outputs). A review of related Research Infrastructures (e.g., ICOS, NEON, EMODnet) highlights multi-level categorizations, FAIR practices, and integration of external data. Stakeholder needs stress harmonized time-series, multi-scale datasets covering climate, soils, hydrology, biodiversity, and socio-economic variables to support integrated analyses and policymaking. eLTER data origins (standard, legacy, third-party) and types (continuous, sample-based, inventory-based, retrieval-based) are detailed, with governance frameworks ensuring quality, transparency, and reusability via ISO 19131-aligned documentation. Eight piloted data products (Levels 2-4) demonstrate applications, such as phytoplankton composition, vegetation data, soil water/temperature, ET-GPP-WUE, Budyko curves, carbon balance, land-use systems, and GPP estimates. Recommendations advocate a top-down/bottom-up approach for product development, prototyping, QA/QC, approval processes, and integration with Topic Centres. Emphasis is placed on FAIR-enabling resources (vocabularies, models), versioning, persistent identifiers incl. DOIs, open licenses, and automation to mitigate risks from emerging technologies like AI and Digital Twins, fostering a sustainable, interoperable ecosystem for long-term research.