https://github.com/cthoyt/twin-identifier-analysis

How many local unique identifiers overlap between each biomedical vocabulary in the Bioregistry?

https://github.com/cthoyt/twin-identifier-analysis

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
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  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (4.9%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

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How many local unique identifiers overlap between each biomedical vocabulary in the Bioregistry?

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  • Host: GitHub
  • Owner: cthoyt
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 401 KB
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Created over 3 years ago · Last pushed over 3 years ago
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README.md

twin-identifier-analysis

How many local unique identifiers overlap between each biomedical vocabulary in the Bioregistry?

This analysis is inside the Jupyter notebook in this repository. Note that running this notebook requires quite a bit of setup for pyobo and caching of data, so I haven't carefully added installation/reproduction instructions.

Results: There are a bit more than 300 resources in the Bioregistry that can be parsed with PyOBO. 134 of those were parsable and had more than 10 identifiers. Almost all of them have meaningful overlap with at least one other resource.

Take-away message: Using prefixes is vital to disambiguate between different namespaces. Endpoints like the neXtProt term endpoint (as discussed in https://github.com/biopragmatics/bioregistry/issues/721) could have collisions between local unique identifiers from resources like MeSH and NCIT.

Outputs:

Owner

  • Name: Charles Tapley Hoyt
  • Login: cthoyt
  • Kind: user
  • Location: Bonn, Germany
  • Company: RWTH Aachen University

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