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We start with the semantic harmonisation service developed by the team in Task 4.2 [ Bibliography#ref3939]. The development is conducted to support the use case "Iceland Volcano Ash". The goal is to support scientists to analyse Iceland behaviour using data provided by different research infrastructures during a specific time period.
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Figure 2: The Deployed service components for semantic harmonization [ Bibliography#ref3939]
Table 1 provides the mapping between Reference Model computational objects and the deployed service components. Among them, the Transformation component serves as a data broker to negotiate data access with data stores within heterogeneous research infrastructures. An (instance of the) semantic broker is implemented using the RDF store technology which provides the semantic mappings and translations.
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Figure 3: Datasets as provided by ICOS (above) with CO2 concentrations and by EURO-Argo (below) with ocean temperature measurements
Semantic mappings are based on observation statements. For example, the following observation statement declares the measurements about “air”:
“Observation of the CO2 concentration in samples of air at the Mace Head atmospheric station which is located at (53_20'N, 9_54'W): CO2 concentration of the air 25m above the sea level on Jan 1st, 2010 at 00:00 was 391.318 parts per million".
“Air” is represented as the concept of air in GEneral Multi-lingual Environmental Thesaurus (GEMET) by assigning the URI to it (entity naming). The GEMET concept of air is then defined as an instance of envri:FeatureOfInterest (entity typing).
The mapping rules are specified by using the Data cube plug-in for Google Refine. The mappings are executed to obtain RDF representations of the source data files. As such they are uploaded to the Virtuoso OSE RDF store and are ready to be queried at a SPARQL-endpoint.
The data harmonization process described above is captured by the Reference Model. As shown in Figure 4, the Information Viewpoint models the mapping of data according to 
mapping rules which are defined by the use of local and global conceptual model. Ontologies and thesauri are defined as conceptual models, and those widely accepted models such as, GEMET, O&M, Data Cube, are declared 
global conceptual models whereas the ENVRI vocabulary is specified as a localone, because it has been developed within the current project without being yet accepted by a broad community.
Figure 4: The RM Information specification related to the semantic harmonisation
Describing a process using the ENVRI Reference Model concepts is to instantiate the concepts that can be mapped to the process. Figure 5 illustrates the instantiation (all boxes with a dashed line) of the ENVRI Reference Model concepts focusing at the harmonization process described above. The same could be demonstrated for the EuroArgo dataset with the feature of interest being ocean. For each part of the observation mapping rules have to be defined to be able to query both datasets at a certain time period.
Figure 5: Mapping of the deployed information model with that of the the Reference Model
The tables below show the mapping between the harmonisation process and the concepts in the ENVRI RM information viewpoint. The example shows that both bottom up (from the applied operation to the model description) and top down approaches (from the model definitions back to the applied solution) can lead to a better understanding of the Reference Model itself and of how components should work properly in a complex infrastructure.
Table 2: Mapping between the Reference Model Information objects and those in the deployed service
| Information Object in RM | Component/Object in Task 4.2 |
| IV Information Objects#specification specification of measurements or observations | Observation of the CO2 concentration in samples of air at the Mace Head atmospheric station which is located at (53_20'N, 9_54'W): |
| IV States#mapped mapped | GEMET:245 is instance of FeatureOfInterest class |
| IV Information Objects#infobj_conceptual model | GEMET, O&M, DataCube |
| IV Information Objects#infobj_conceptual model | ENVRI vocabulary |
| IV Information Object Instances#local local concept | FeatureOfInterest (ENVRI vocabulary) |
| IV Information Object Instances#global global concept | Component Property, GEMET:245, FeatureOfInterest (O&M) |
| IV Information Objects#infobj_mapping rule | GEMET:245 create as instance of FeatureOfInterest class |
| IV States#publishedpublished | ICOS data CO2 of air, EuroArgo data ocean temperature |
Table 3: Mapping between the Reference Model Action Types and those in the deployed service
| Information Action Tyoes in RM | Operation in Task 4.2 |
| IV Information Action Types#infoaction_build conceptual models | Build ENVRI vocabulary as extension of DataCube and on basis of O&M concepts |
| IV Information Action Types#infoaction_setup mapping rules | Define rule: GEMET:245 create as instance of FeatureOfInterest class |
| IV Information Action Types#infoaction_perform mapping | Perform Mapping using Google Refine |
| IV Information Action Types#infoaction_query data | SPARQL query: |
Summary
This example demonstrate the feasibility of the design specifications of the reference model. Instances of selected model components can be developed into common services, in this case, a Model Overview#subsys_acc subsystem that supports integrated data discovery and access. Data products from different environmental research infrastructures including, measurements of deep sea, upper space, volcano and seismology, open sea, atmosphere, and biodiversity, can now be pulled out through a single data access interface. Scientists are using this newly-available data resource to study environmental problems previously unachievable including, the study of the climate impact caused by the eruptions of the Eyjafjallajökull volcano in 2010.
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