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Potential examples include:\n\n - OBO Dashboard\n - GO Rules\n - Monarch\n - KG frameworks\n\nSee\nhttps://docs.google.com/document/d/1ld73pVz_BIH22jRBZuV0RVDeSiuGyQpD1u_F9Yv9gg0/edit#\n\nThe core components:\n\n - a schema for representing both metrics themselves and the results of evaluating them\n - TODO: code for downloading sources and loading them into a database\n - TODO: code for executing these metrics on these sources\n - TODO: lightweight view layer to generate an HTML grid summary\n\n## Schema\n\nBrowse the schema here: [http://cmungall.github.io/dasher](http://cmungall.github.io/dasher)\n\nSee the [schema/](schema/) folder\n\nThe source is in YAML (biolinkml)\n\nCurrently the main derived artefacts of interest are:\n\n - [JSON Schema](src/schema/semqc.schema.json)\n - [Python dataclasses](src/schema/semqc_datamodel.py)\n\n## Download/Preparation\n\nTODO\n\nThe idea is to use blazegraph-runner here.\n\nAssume each source is trivially convertable to RDF\n\n## Executing Metrics\n\nTODO\n\nAssumption that a number of generic mechanisms can be used:\n\n - OWL: Reasoning\n - Shapes/ShEx\n - JSON-Schema validation (requires JSON-LD framing)\n - SPARQL (e.g. robot checks)\n\nAdditionally, each implementation using this framework is free to add in their own procedural checks, e.g. in Python, although declarative is preferred\n\nThe code will generate json/yaml-ld conforming to the schema above\n\nImplementing pipelines e.g Jenkins can choose to do things like fail-fast based on queries of result set\n\n## Display\n\nTODO\n\nSomething simple such as mustache / liquid templates over the above json-ld\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmungall%2Fdasher","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcmungall%2Fdasher","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmungall%2Fdasher/lists"}