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Abstract Classification of the biological diversity on Earth is foundational to all areas of research within the natural sciences. Reliable biological nomenclatural and taxonomic systems facilitate efficient access to information about organisms and their names over time. However, broadly sharing, accessing, delivering, and updating these resources remains a persistent problem. This barrier has been acknowledged by the biodiversity data sharing community, yet concrete efforts to standardize and continually update taxonomic names in a sustainable way remain limited. High diversity groups such as arthropods are especially challenging as available specimen data per number of species is substantially lower than vertebrate or plant groups. The Terrestrial Parasite Tracker Thematic Collections Network project developed a workflow for gathering expert-verified taxonomic names across all available sources, aligning those sources, and publishing a single resource that provides a model for future endeavors to standardize digital specimen identification data. The process involved gathering expert-verified nomenclature lists representing the full taxonomic scope of terrestrial arthropod parasites, documenting issues experienced, and finding potential solutions for reconciliation of taxonomic resources against large data publishers. Although discordance between our expert resources and the Global Biodiversity Information Facility are relatively low, the impact across all taxa affects thousands of names that correspond to hundreds of thousands of specimen records. Here, we demonstrate a mechanism for the delivery and continued maintenance of these taxonomic resources, while highlighting the current state of taxon name curation for biodiversity data sharing. 

Abstract Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We presentBeeBDC, a newRpackage, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducibleBeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. TheBeeBDCpackage with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducibleRworkflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.