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Examples

The examples use a provenance chain created according to the Common Provenance Model (CPM) and annotated with types from the Provenance of Biological Material Ontology (PBM-O) and the Provenance of Data Generation Ontology (PDG-O), which correspond to ISO 23494-3 and ISO 23494-4, respectively. The examples show the general use of the model, with queries specific to these two ontologies.

Traversal of the provenance chain

A user accesses the provenance chain (Figure 1) at an entry point. A generic traversal algorithm searches the traversal information of the provenance components for the relevant attributes. Once the relevant attribute is found, the traversal continues to the domain-specific part of the component, where domain-specific extensions or configurations apply.

Provenance chain of five components: Preanalytics - outside lab, Preanalytics - inside lab, Data Generation, Samples storage, and AI Training. Each component contains traversal information linked to the neighbouring components and domain-specific provenance.
Figure 1. Provenance chain.

Query related to sample acquisition

Figure 2 shows a query related to sample acquisition. The traversal starts at the entry point in the AI Training component and follows the provenance chain to the Preanalytics - outside lab component, in which the sample acquisition (pbm:Acquisition) and its domain-specific provenance are found.

The provenance chain of Figure 1 with the traversal entry at the AI Training component; in the Preanalytics - outside lab component, the sample acquisition activity and its domain-specific provenance are highlighted.
Figure 2. Query related to sample acquisition.

Query related to data generation

Figure 3 shows a query related to data generation. The traversal starts at the same entry point and follows the provenance chain to the Data Generation component, in which the data generation (pdg:DataGeneration) and its domain-specific provenance are found.

The provenance chain of Figure 1 with the traversal entry at the AI Training component; in the Data Generation component, the data generation activity and its domain-specific provenance are highlighted.
Figure 3. Query related to data generation.

Further extensions specific to a domain, to a stage of the sample or data life cycle, or to a scenario are expected.