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Makelabels: a Bash script for generating data matrix codes for collection management.

Theresa A Dellinger1, Victoria Wong1, Paul E Marek1

  • 1Virginia Tech, Blacksburg, United States of America Virginia Tech Blacksburg United States of America.

Biodiversity Data Journal
|May 15, 2025
PubMed
Summary
This summary is machine-generated.

Digitizing natural history collections requires unique specimen catalog numbers. A new open-source script, makelabels, efficiently generates data matrix barcodes and human-readable numbers for mass label production.

Keywords:
collections managementcurationdata matrix barcodedigitizationentomology collectionnatural history collectionopen source

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Area of Science:

  • Biodiversity Informatics
  • Data Curation
  • Natural History Collections

Background:

  • Digitization of natural history collections enhances access to biodiversity data.
  • Unique catalog numbers are essential for identifying digitized specimens.
  • Existing open-source barcode generators lack mass production capabilities for natural history collections.

Purpose of the Study:

  • To develop a low-cost, open-source solution for generating unique data matrix barcodes and catalog numbers for natural history specimens.
  • To address the need for mass production of barcode labels in digitization workflows.

Main Methods:

  • Developed the 'makelabels' script.
  • Utilized open-source Unix packages: libdmtx and ImageMagick.
  • Generated specimen labels with human-readable catalog numbers and machine-readable data matrix barcodes.

Main Results:

  • The makelabels script enables the generation of unique specimen labels.
  • The solution facilitates mass production of labels with both human-readable and machine-readable symbology.
  • Increased efficiency in digitization and specimen processing workflows.

Conclusions:

  • The makelabels script provides a flexible and efficient method for specimen management in natural history collections.
  • This open-source solution supports the digitization and reuse of valuable biodiversity data.
  • The approach improves workflow efficiency for digitization and specimen processing.