> For the complete documentation index, see [llms.txt](https://frameworks.greendealdata.eu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://frameworks.greendealdata.eu/11-data-interoperability/provenance-and-traceability.md).

# Provenance & Traceability

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*This section might be updated based on the latest developments in the SAGE consortium, specifically considering the WP5 working group. Since the project runs till 2028, the final GDDS deliverable is expected to have additional information on these sections.*
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## Importance of trust and reuse 

Provenance and traceability are critical enablers of trust, accountability, and reuse within the GDDS. They provide transparent evidence about where data originates from, how it has been created or transformed (including evidence about ownership, custody and originating location), and how it is subsequently accessed and used. Across the SAGE use cases, provenance consistently emerged as a first‑order requirement, not only for regulatory compliance but also for scientific validity, business trust, and operational decision‑making. 

## Provenance: lineage and methodology 

In GDDS, provenance is understood as backward‑looking information that describes the lineage of a data product. This includes original data sources, collection methods, processing and transformation steps, responsible actors, and applicable methodologies. Examples from the use cases include documentation of emission calculation methods, model parameters used in environmental valuation, and metadata inherited from CF‑compliant NetCDF products in Earth observation workflows. 

Such provenance information enables users to assess fitness for purpose, comparability, and limitations of the data prior to reuse, and is particularly important where results support reporting, certification, or policy evaluation. 

Traceability: use and downstream impact 

Traceability complements provenance by being forward‑looking, capturing how data is accessed, shared, combined, or transformed after publication within the data space. For many Green Deal use cases — including sustainability reporting, emissions accounting, circular economy monitoring, and policy assessment — it is essential to understand how data contributes to downstream outputs and decisions, and under which access and usage conditions. 

## Domain‑specific needs and reuse of existing models 

The SAGE use cases show that provenance and traceability requirements vary significantly across domains. Some use cases require fine‑grained, model‑level and parameter‑level provenance, while others prioritise source attribution, responsibility, and validation status. GDDS therefore does not enforce a single, monolithic provenance schema. Instead, it promotes the reuse of established provenance models and patterns, complemented by domain‑specific extensions where required. 

Consequently, provenance and traceability generate additional data that must itself be semantically represented and interpretable across participants. This requires dedicated data models covering two complementary aspects, as proposed by DSSC handbook: 

* Generic provenance and traceability aspects, which are applicable across multiple domains and scenarios. Common standards and ontologies supporting these capabilities include PROV-O and PAV, which provide mechanisms to represent provenance, derivation, authorship, and versioning information. Some data spaces also model operational or business events occurring within the ecosystem, for which approaches such as CloudEvents can be adopted. 
* Data-space-specific provenance and traceability aspects, capturing governance, contractual, policy, transaction, or domain-specific events and interactions unique to a particular data space. These aspects can be represented through extensions or specialisations of generic provenance models such as PROV-O or PAV. 

Provenance data is treated as a structured, linkable asset that can be queried, audited, and, where appropriate, reused independently of the primary dataset. 

## Observability, governance, and value creation 

Provenance and traceability are closely linked to observability within GDDS. Observability covers the monitoring of key control‑plane events, such as publication, discovery, contract negotiation, access decisions, and data transactions. Together, provenance, traceability, and observability support auditing, certification, dispute resolution, and compliance verification, while also enabling operational insights into how the data space is used. 

Provenance and traceability information is itself represented through interoperable data models and policy vocabularies, enabling machine-readable interpretation, automated compliance verification, and integration across participants. In particular, policy models such as ODRL can be used to express access conditions, usage constraints, obligations, and purpose limitations associated with provenance records. Combined with provenance ontologies such as PROV-O and PAV, these models support interoperable and policy-aware traceability across the data space ecosystem. 

From a governance standpoint, provenance and traceability information is subject to the same sovereignty, access control, and policy enforcement rules as primary data. Not all provenance data is necessarily public, and GDDS therefore emphasises controlled access, clear purpose limitation, and proportionality when defining provenance requirements. 

Finally, GDDS recognises provenance not only as a compliance artefact, but also as a value‑enabling resource. Well‑described lineage and usage information increases trust, improves discoverability, supports reproducibility, and enables advanced services such as quality assessment, benchmarking, certification, and automated reporting. By embedding provenance and traceability as core technical capabilities, GDDS ensures that Green Deal data remains credible, reusable, and impactful over time.