The COVID-19 epidemiology and monitoring ontology
The covid-19 epidemiology and monitoring ontology (cemo) provides a common ontological model to make epidemiological quantitative data for monitoring the covid-19 outbreak machine-readable and interoperable to facilitate its exchange, integration and analysis, to eventually support evidence-based rapid response.
README
The COVID-19 epidemiology and monitoring ontology The COVID-19 Epidemiology and Monitoring Ontology (CEMO) is designed to make epidemiological quantitative data for monitoring the COVID-19 outbreak machine-readable and interoperable to facilitate its exchange, integration and analysis, to eventually support evidence-based rapid response. This ontology has built following knowledge-engineering standards and the OBO principles to bridge epidemiology into the semantic landscape of the biomedical…
Source attribution
- GitHub — github.com/nuriaqueralt/covid19-epidemiology-ontology
- Bioregistry — cemo
Related resources
A data model for managing information about chemical entities, ranging from atoms through molecules to complex mixtures.
The Chromosome Ontology is an automatically derived ontology of chromosomes and chromosome parts.
Babelon is a simple standard for managing ontology translations and language profiles. Profiles are managed as TSV files, see for example https://github.com/obophenotype/hpo-translations/tree/main/babelon. The goal of Babelon as a data model and vocabulary is to capture the minimum data required to capture important metadata such as confidence and precision of translation.
SO is a collaborative ontology project for the definition of sequence features used in biological sequence annotation. It is part of the Open Biomedical Ontologies library.
Bioschemas aims to improve the Findability on the Web of life sciences resources such as datasets, software, and training materials. It does this by encouraging people in the life sciences to use Schema.org markup in their websites so that they are indexable by search engines and other services. Bioschemas encourages the consistent use of markup to ease the consumption of the contained markup across many sites. This structured information then makes it easier to discover, collate, and analyse distributed resources. [from BioSchemas.org]
This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases.