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Generally, every agency has their own internal practices and standards but we require a common understanding to ensure data is being accurately represented within the central platform. Water datasets can further be classified broadly or specific to a particular use (e.g., water used for irrigation). We adopted a SensorThings (ST) data model to develop a common “language” around the different types of water data. ST is a service focused on data streaming that supports integration of metadata within the data structure. For the purposes of organizing standards development webpages/activities, we group water datasets into two types, one focused on water quantity (e.g., groundwater levels, water use, ...) and another on water quality (e.g., chemical, biological, …) (see subpages on the left). Within each of these pages, we adopted a SensorThings (ST) data model to develop a common “language” around the different types of water data.
ST is a service focused on data streaming that supports integration of metadata within the data structure. ST organizes data into “Things” each of which has a particular location and associated datastreams that captured one or more observations from sensors that capture features of interest. This general framework allows for a flexible approach to standardize data across diverse types of datasets, ultimately enabling for data to be better described, easy to use, and discoverable. This is due to the inclusion of relevant metadata to make the data more complete and allowing for more convenient ways to query the data using ST APIs.
The basic elements of SensorThings are:
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Kyle Onda (Unlicensed) from the Internet of Water provided us an overview of the ST framework on . Check out the recording from his talk here. A copy of his slides are also available for reference:
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