Abstract:
Researchers seeking to apply computational methods are increasingly turning
to scientific digital archives containing images of specimens.
Unfortunately, metadata errors can inhibit the discovery and use of
scientific archival images. One such case is the NSF-sponsored Biology
Guided Neural Network (BGNN) project, where an abundance of metadata errors
has significantly delayed development of a proposed, new class of neural
networks. This paper reports on research addressing this challenge. We
present a prototype workflow for specimen scientific name metadata
verification that is grounded in Computational Archival Science (CAS),
report on a taxonomy of specimen name metadata error types with preliminary
solutions. Our 3-phased workflow includes tag extraction, text processing,
and interactive assessment. A baseline test with the prototype workflow
identified at least 15 scientific name metadata errors out of 857 manually
reviewed, potentially erroneous specimen images, corresponding to a ~0.2%
error rate for the full image dataset. The prototype workflow minimizes the
amount of time domain experts need to spend reviewing archive metadata for
correctness and AI-readiness before these archival images can be utilized
in downstream analysis.
