In this live-action program viewers will learn that being able to make careful and accurate observations is the foundation of any lab based science class, such as chemistry, biology, or physics. Students will come to understand the ability to make observations allows scientists to collect and draw conclusions about their experiment from the data. They will discover that even the smallest observations make prove to be crucial to drawing accurate conclusions from the results of an experiment. Observations and Measurements (O&M) is an international standard which defines a conceptual schema encoding for observations, and for features involved in sampling when making observations. While the O&M standard was developed in the context of geographic information systems, the model is derived from generic patterns proposed by Fowler and Odell, and is not limited to spatial information. O&M is one of the core standards in the OGC Sensor Web Enablement suite, providing the response model for Sensor Observation Service (SOS).
The core of the standard provides the observation schema. An observation is an act that results in the estimation of the value of a feature property, and involves application of a specified procedure, such as a sensor, instrument, algorithm or process chain. The procedure may be applied in-situ, remotely, or ex-situ with respect to the sampling location. Use of a common model for observation metadata allows data to be combined unambiguously, across discipline boundaries. Observation details are also important for data discovery and for data quality estimation. An observation is defined in terms of the set of properties that support these applications.
O&M defines a core set of properties for an observation:
feature of interest
observed property
result
procedure – the instrument, algorithm or process used (which may be described using SensorML)
phenomenon time – the real-world time associated with the result
result time – the time when the result was generated
valid time – the period during which the result may be used
The key to the model is the division of the observation and its feature of interest, separating the concerns so that the appropriate information associated with the description of each object. This allows a unified treatment of in situ, ex-situ, and remote-sensed observations. The observation schema may also be understood as a corollary of the General Feature Model from ISO 19101, providing metadata associated with the estimation of the value of a feature property. The Observation model takes a user-centric viewpoint, emphasizing the semantics of the feature-of-interest and its properties. This contrasts with sensor oriented models such as SensorML, which take a process – and thus provider-centric viewpoint.
Many observations are made to detect the variation of some property in the natural environment, expressed as a spatial function or field, also known as a coverage. The relationship between observations, features and coverages is explained, in the context of ocean observations and modeling, in a report for GEOSS Architecture Implementation Pilot 3.
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