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Abstract PremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields. MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates. ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors. DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.
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A born‐digital field‐to‐database solution for collections‐based research using collNotes and collBook
PremiseThe digitization of natural history collections includes transcribing specimen label data into standardized formats. Born‐digital specimen data initially gathered in digital formats do not need to be transcribed, enabling their efficient integration into digitized collections. Modernizing field collection methods for born‐digital workflows requires the development of new tools and processes. Methods and ResultscollNotes, a mobile application, was developed for Android andiOSto supplement traditional field journals. Designed for efficiency in the field, collNotes avoids redundant data entries and does not require cellular service. collBook, a companion desktop application, refines field notes into database‐ready formats and produces specimen labels. ConclusionscollNotes and collBook can be used in combination as a field‐to‐database solution for gathering born‐digital voucher specimen data for plants and fungi. Both programs are open source and use common file types simplifying either program's integration into existing workflows.
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- PAR ID:
- 10460235
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Applications in Plant Sciences
- Volume:
- 7
- Issue:
- 8
- ISSN:
- 2168-0450
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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