Search Constraints
Search Results
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2017
Conference paper (published)
Identifying Novel Features from Specimen Data for the Prediction of Valuable Collection Trips.
Primary biodiversity data provide “what, where, and when” data points: the assertion that a species occurred at a particular point in space and time. These are most valuable when associated with specimens stored in natural history museums and herbaria, which evidence the assertions with reference to a physical specimen. The...Nicolson, Nicky ; Tucker, Allan
Collecting trip, Identification, Specimen data, Data-mining, Collectors, and Metadata
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2019
Abstract
Examining Herbarium Specimen Citation: Developing a literature-based institutional impact measure.
Herbarium specimens are critical components of the research process - providing "what, where, when" evidence for species distributions and through type designation, providing the basis for un-ambiguous, standardised nomenclature facilitating the interpretation of scientific names. Specimen references are embedded within research article texts, by convention usually presented in a relatively...Nicolson, Nicky ; Paton, Alan ; Phillips, Sarah ; Tucker, Allan
Text classification, Specimen citation, Text mining, and Citation metrics
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2019
Abstract
Integrating Collector and Author Roles Across Specimen and Publication Datasets.
This work builds on the outputs of a collector data-mining exercise applied to GBIF mobilised herbarium specimen metadata, which uses unsupervised learning (clustering) to identify collectors from minimal metadata associated with field collected specimens (the DarwinCore terms , and ). Here, we outline methods to integrate these data-mined collector entities...Nicolson, Nicky ; Paton, Alan ; Phillips, Sarah ; Tucker, Allan
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2018
Conference paper (published)
Specimens as Research Objects: Reconciliation Across Distributed Repositories to Enable Metadata Propagation
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2022
Journal article
Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.
The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these...Walker, Barnaby E. ; Tucker, Allan ; Nicolson, Nicky
Machine learning, Natural history collections, Digitized herbarium specimens, Deep learning, and Computer vision