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The commonly used method of data representation in virtual environment is by using a scenegraph structure.
Figure 9 shows correspondences of data representation in the real world to instances based on ontology.
XML is becoming the ubiquitous choice for data representation in a machine-readable format and for data exchange.
In particular, data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources.
The domain-specific data representation in XML for the item type is shown in Fig. 4 exemplified for the same item as used in Fig. 6.
Model parameterization and evaluation are presented along with the decisions made to overcome problems related to data representation in the catchment, in an effort to provide guidance on SWAT modeling in areas with similar characteristics.
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The data representations in the hidden layer present meaningful abstraction levels for the analysis of the learning performed.
Fig. 2 Examples of the multiple data representations in public displays that students used during the unit.
Glorot et al. [86] illustrate that Deep Learning can find intermediate data representations in a hierarchical learning manner and this representation can be used for other domains.
In this context, ontology has been recognized in the bioinformatics literature as a suitable technique for advancing knowledge and data representations in biomedicine.
The last one is introduced to avoid the duplication of data representations in the hierarchy, when data is exchanged between the graphs.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com