# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## Data and Metadata Representation and Storage
### Geospatial and Environmental Data Modeling and Storage
Regarding data storage, although some general-purpose scientific array data managers have already been developed [29; 30] their use is still not widespread among the scientific communities, which still rely on the direct recoding of coverages in files with formats such as GeoTIFF and NetCDF. As it was already stated in the introduction, to achieve smart datasets and smart data infrastructures, a key issue is the availability of high-quality metadata. The representation of metadata for geospatial datasets and services is also standardized through the ISO 19115 Geographic metadata standard. The metadata specified by this standard includes descriptions of the identification, spatial and temporal extension, data quality and provenance (lineage), other spatial and temporal characteristics, contents, visualization, distribution, etc. Identification metadata includes an identifier, title, abstract, date, organization or person responsible, geographic and temporal extents, languages used, information about scale and resolution, URL for on-line access, keywords, access restrictions, etc. The standard includes both mandatory and optional metadata elements, enabling the specification of a minimum set of metadata for a broad range of application domains, but also providing an extension mechanism to overcome specific requirements. Metadata related to provenance and observed features and properties are particularly important to enable the correct interpretation of environmental sensor observation data. These metadata go beyond the dataset level metadata represented by ISO 19115, thus specific data models are required. Therefore, various designs have been proposed in the environmental data management research domain. As an example, the observations data model (ODM) [31] has been developed as part of the data layer of the hydrological information system (HIS) of the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) [32]. This model enables the recording of hydrological observed data values together with metadata that includes the sites where they were measured, the variables that were measured and the methods used to perform the measurement. The VOEIS (Virtual Observatory and Ecological Information System) observation data model (VODM) is based on ODM and it has been designed to represent observation data in the VOEIS Data Hub [33], VODM extends ODM with data streams and dataset level metadata. ODM has also been extended and generalized in [34] to deal with discrete Earth observations. In [35], the authors review more than 40 systems for water management and propose a new water management data model (WaMDaM). In parallel with the above research initiatives, and aligned with them, the OGC has been defining also its own general-purpose data model for environmental observation data. The general structure of this model, called Observations and Measurements (O&M) [11] (also ISO 19156), is depicted in the UML class diagram of Figure 6. As it is shown in the figure, the central class of the model is the OM_Observation, which represents and observation produced at a given resultTime by a given observation process (OM_Process), generally based on some sensor system. The observation provides a value (result) for an observed property (GF_PropertyType) of a given feature of interest (GF_Feature) that applies at a given phenomenonTime. Besides, an observation may also have parameters (key-value pairs that provide additional context information), general metadata and specific data quality metadata. A specific subtype of features is defined by the standard to represent sampling features (SF_SamplingFeature), i.e., intermediate features related to the sampling method from which data has to be recorded. When the location of this sampling feature is important then what we have is a spatial sampling feature (SF_SpatialSamplingFeature). Examples of spatial sampling features are the elements of a network of sampling stations, which are platforms where the processes (sensors) are hosted.