The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations -the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects

Diabetes emerged as major risk factor for severe acute respiratory syndrome (SARS) and adverse outcome in patients with the coronavirus disease 2019 (COVID-19). Nevertheless, the role of admission hyperglycemia in patients with COVID-19 has not been wellexplored, yet. With this retrospective analysis, we report for the first time that hyperglycemia on day-1 is the best predictor of radiographic imaging of SARS-CoV2, regardless of the past medical history of diabetes. Admission hyperglycemia should not be overlooked, but adequately treated to improve the outcomes of COVID-19 patients with our without diabetes.

As standards in best practices in data quality assurance and quality control evolve, methods for discovery and transport of information relating to these practices must also be developed. An observation's history, from sensor descriptions, processing methods, parameters and quality control tests to data quality flags and sensor alert flags, must be accessible through standards-based web services to enable machine-to-machine interoperability. This capability enables a common understanding and thus an underlying trust in the expanding world of ocean observing systems. For example, a coastal observatory conducts several tests to evaluate and improve the quality of in situ time series data (e.g. velocity) and then generate an oceanic property (e.g. wave height). Using content-rich webenabled services, a data aggregation center will be able to determine which tests were conducted, interpret data quality flags and provide value added services, such as comparing the parameter with those from near-by observations. These additional processing steps may also be documented and sent along with the data to other participating ocean observing systems throughout the world. By utilizing standards-based protocol (Open Geospatial Consortium (OGC) frameworks) and welldefined community adopted QA/QC (Quality Assurance/Quality Control) tests and best-practices (Quality Assurance in Real-Time Oceanographic Data -QARTOD), information about the system provenance, sensor and data processing history needn't be lost.Are data providers ready, willing and able to describe sensors and processing history? And can we transport the information using a framework that offers semantic and syntactic interoperability? The group developing this community white paper has demonstrated that it can be and is being done. A project called Q2O, QARTOD to OGC (Open Geospatial Consortium), bridges the QARTOD community with the OGC community to demonstrate and document best practices in the implementation of QA/QC within the OGC Sensor Web Enablement (SWE) framework. This paper describes this demonstration project and documents the existence of parallel related efforts. With adequate funding to enable the strengthening and broadening of these communities, a solid foundation for ocean observing systems will be built with the assurance that best-practices of data quality are communicated in a meaningful way.

No abstract