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A Novel Automated Mass Digitisation Workflow for Natural History Microscope Slides.

E Louise Allan1, Laurence Livermore1, Benjamin W Price1

  • 1Natural History Museum, London, United Kingdom Natural History Museum London United Kingdom.

Biodiversity Data Journal
|March 14, 2019
PubMed
Summary
This summary is machine-generated.

The Natural History Museum developed a semi-automated workflow for digitizing 70,663 parasitic lice slides. This process uses barcodes for efficient data management and automated image processing, improving collection inventories.

Keywords:
PhthirapteraData Matrix barcodesautomationmass digitisationmicroscope slidesnatural history collections

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Area of Science:

  • Natural History
  • Museum Science
  • Digital Curation

Background:

  • Natural History Museum (NHM) London is digitizing collections to enhance accessibility and research.
  • High-throughput digitization requires efficient workflows and robust infrastructure for large scientific collections.

Purpose of the Study:

  • To present a novel semi-automated mass digitization workflow for the entire Phthiraptera (parasitic lice) microscope slide collection.
  • To improve the efficiency and accuracy of creating digital inventories for large scientific collections.

Main Methods:

  • Development of a semi-automated mass digitization process for 70,663 microscope slides.
  • Utilized temporary and permanent barcode labels encoding unique identifiers, location, and taxonomic data.
  • Implemented automated file renaming, image processing, and bulk import into the NHM's collection management system.

Main Results:

  • Demonstrated significant reductions in image post-processing time through automated file renaming.
  • Minimized human error in data handling and collection management.
  • Showcased the adaptability of the workflow for various collection types.

Conclusions:

  • The developed semi-automated workflow enhances the efficiency of mass digitization for scientific collections.
  • Barcode integration streamlines data management, reduces errors, and facilitates bulk import.
  • This approach provides a scalable model for digitizing large natural history collections.