The cross-institutional nature of the ORNIS network will enable an approach to error-detection that includes both temporal and spatial elements (i.e., "collecting events"). Collector's specimen locality records can be ordered by date of collection, and distances traveled during one day or a few days can be calculated. These distances can then be filtered to detect unexpectedly long distances that might indicate erroneous data records. Implementation of this tool requires a community architecture such as ORNIS because most collectors' specimens are scattered across multiple institutions.
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