[Current state of play] Numerous digitisation and data aggregation efforts are mobilising botanical specimen data. Although digitisation is not yet complete, it is likely that we now have a critical mass of data available from which we can determine patterns. [Problem] We know that many duplicate specimens exist, shared between separate botanical collections: these are digitised and transcribed in different herbaria and are yet to be comprehensively linked. Parallel digitisation efforts mean that the transcription of label data also happens in parallel, this results in some critical data fields (such as collector name) being much too variable to be easily used to resolve duplicates. Although not explicitly managed, we have the concept of a collecting trip (a sequence of collections from a particular individual or team). This research aims to uncover this implicit trip data from the aggregated whole. Once we have identified a collecting trip, we should be able to more easily resolve duplicates by cross linking on the trip identifier, along with the record number and date - i.e. avoiding the transcription variations that we often see in the collector field. [Method and input data] This talk will show the output of a clustering analysis run in Python using the machine learning library scikit-learn. The data analysed were drawn from aggregated botanical specimen data accessed via the GBIF portal. Input to the analysis was optimised to use numeric features wherever possible (collection date and record number) along with minimal textual features extracted from the collector team. [Results] The outputs of this clustering analysis will be used in a research context - to identify different kinds of collector trip – but also have immediate practical applications in data management: to identify duplicate specimens between herbaria, and to identify outliers and label transcription errors. Examples of each of these kinds of outliers will be shown. Numbers of geo-references which can be shared between institutions will also be included. Other applications of this clustering technique within problem domains relevant to biodiversity informatics (e.g. bibliographic reference management) will also be discussed.
