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talk · Tuesday 22 September · 12:00–⁠12:15 · SAL B

Geonomia: Metadata and Community for Georeferencing

Nicky Nicolson · Responsible AI, Open Digital Curation, and Round-Tripping for Biodiversity Data

Recording time 3:29:52–3:43:18Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionClustering specimens into collecting trips makes georeferencing and AI enrichment more efficient and should be done as a community effort.

Overview

What this was about

Nicky Nicolson presented Geonomia, a Kew project to make specimens' textual localities computable. Only 13% of Kew's 6.3 million digitised specimens appear on GBIF maps, yet analysis of download predicates behind citations showed two-thirds of users filter spatially, discarding most of Kew's data. Using density-based clustering of collector number and date (collectors move forward in time and space), the team groups specimens into collecting trips across all herbaria's GBIF data, finds outliers, and passes trip-level batches to an LLM to summarise itineraries, habitats and regions, publishing results with Datasette. She argued collecting trips are a useful entity between GBIF's related records and Bionomia's collector careers, and that georeferencing should be shared community work.

Why it matters. Tackles the large share of specimen data invisible to spatial queries and offers a reusable approach for batching records meaningfully before applying AI.

Key ideas

In the room

  • Kew has digitised 6.3 million specimens, but only 13% have coordinates on GBIF; many predate GPS and only have place descriptions.
  • Analysis of GBIF download predicates for citations of Kew data showed two-thirds used spatial predicates, discarding most Kew data; the same analysis is possible for any GBIF dataset.
  • Collector number plus date traces a collector's path; density-based clustering finds adjacent collecting events, groups variant locality strings and reveals transcription outliers.
  • Clusters give context for human georeferencing and tighter context batches for AI.
  • Used GBIF SQL downloads (vascular plants, species level, one country) to pre-process on GBIF's side, including screening recordedBy values like 'collector unknown'.
  • Processing ran on a shared HPC facility at the Crop Diversity Institute; LLMs summarised habitats, itineraries (linear traverse vs hub-and-spoke), place names and regions per trip.
  • Results published as a SQLite database with Datasette; example: a 1964 trip of almost 2,000 specimens with only about a third georeferenced, drawn from many institutions' datasets.
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Notable moments

In their words

Transcript

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Programme abstract

Geonomia: Metadata and Community for Georeferencing

Nicky Nicolson

There has been considerable progress and investment into the digitisation of natural history collections, particularly among larger institutions that hold globally distributed specimen collections. These specimen records provide the “what, where, when” evidence used as the basis of much research. Data handling pipelines often include data cleaning steps which enhance our understanding of the core “what, where, when” and other attributes of the specimen record. Attributing these enhancements, along with acknowledging their source, to the digital specimen record is difficult due to the distributed nature of research tasks, many of which take place outside the holding institution of the specimen.

Distributed curation and management of digital specimen records are a necessary step in their use to support specific research interests and objectives, resulting in rich but fragmented, unusable data. Data are easily transferable, though trust in the data is essential for passive cross-institutional collaboration, particularly for subjective and ad-hoc tasks such as georeferencing.

With a focus on improving geographic coordinates or retrospectively adding new spatial data, attendees will be presented with the Geonomia project, which clusters GBIF-mediated specimen data to uncover field collecting patterns. The system includes a georeferencing application which will be demonstrated alongside community workshop outputs focussing on the digital curation of the georeferencing process and outputs. The aim of the project is to encourage the re-integration of comprehensive georeference metadata and continually improve the context of field collecting patterns.

Nicolson, N., Bachman, S. and Whittaker, A. (2026) Geonomia. Available at: https://github.com/geonomia (Accessed: 27/05/2026)

Submitted to the conference programme before the talk, so the talk itself may differ.