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Digitized biocollections data often contain taxonomic name errors, with less than 47% of names being currently valid. However, most names can be resolved with computer-aided tools, highlighting potential for improving legacy data.

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Area of Science:

  • Biodiversity informatics
  • Taxonomy
  • Data science

Background:

  • Digitized biocollections data are rapidly accumulating in global repositories.
  • Taxonomic names on specimen labels often suffer from misspellings, outdated nomenclature, and lack of clear provenance.
  • Accurate taxonomic data are crucial for reliable scientific research and biodiversity assessments.

Purpose of the Study:

  • To assess the scope and nature of taxonomic name issues in digitized biocollections data.
  • To create a human-vetted reference dataset for validating automated name-resolution tools.
  • To identify factors influencing the occurrence of taxonomic name problems.

Main Methods:

  • A random sample of 1000 verbatim taxonomic names from VertNet was manually curated and validated.
  • Human vetting focused on identifying misspellings, outdated names (synonymy), and Darwin Core compliance issues.
  • Logistic regression models were used to analyze potential drivers of taxonomic name errors, including geographic region, collection year, and data volume.

Main Results:

  • Less than 47% of the analyzed name strings represented currently valid taxonomic names.
  • Despite issues, nearly 97% of name combinations could be resolved to a valid name, indicating potential for automated correction.
  • Geographic region, collection year, higher-level clade, and institutional data volume were significant predictors of name issues.

Conclusions:

  • Significant challenges exist in ensuring taxonomic name accuracy within digitized biocollections.
  • Computer-aided approaches show promise for resolving and improving the quality of legacy biodiversity data.
  • The developed reference dataset can facilitate the evaluation and advancement of automated taxonomic name-resolution tools.