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Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) |
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Overview |
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-------- |
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This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. |
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The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. |
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Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. |
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This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) |
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and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) |
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serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file |
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with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, |
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i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. |
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The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster |
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than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate |
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for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms |
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used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated |
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by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined |
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in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed |
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in a specific location of the data model. |
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:Domain: Chemistry |
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:Category: Chemistry |
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:Current Version: 1.1.0 |
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:Last Updated: 2017-10-19 |
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:Creator: Daniel Schober |
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:License: Creative Commons 4.0 |
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:Format: OWL |
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:Download: `Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) Homepage <https://terminology.tib.eu/ts/ontologies/NMRCV>`_ |
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Graph Metrics |
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------------- |
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- **Total Nodes**: 1596 |
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- **Total Edges**: 3951 |
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- **Root Nodes**: 184 |
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- **Leaf Nodes**: 662 |
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Knowledge coverage |
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------------------ |
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- Classes: 757 |
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- Individuals: 0 |
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- Properties: 0 |
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Hierarchical metrics |
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-------------------- |
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- **Maximum Depth**: 5 |
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- **Minimum Depth**: 0 |
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- **Average Depth**: 1.01 |
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- **Depth Variance**: 0.72 |
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Breadth metrics |
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------------------ |
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- **Maximum Breadth**: 273 |
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- **Minimum Breadth**: 2 |
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- **Average Breadth**: 103.83 |
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- **Breadth Variance**: 10836.47 |
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Dataset Statistics |
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------------------ |
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Generated Benchmarks: |
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- **Term Types**: 0 |
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- **Taxonomic Relations**: 792 |
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- **Non-taxonomic Relations**: 0 |
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- **Average Terms per Type**: 0.00 |
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Usage Example |
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.. code-block:: python |
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from ontolearner.ontology import NMRCV |
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# Initialize and load ontology |
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ontology = NMRCV() |
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ontology.load("path/to/ontology.owl") |
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# Extract datasets |
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data = ontology.extract() |
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# Access specific relations |
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term_types = data.term_typings |
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taxonomic_relations = data.type_taxonomies |
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non_taxonomic_relations = data.type_non_taxonomic_relations |
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