Today we publish a new Data Release presenting a dataset of jellyfish sightings collected by citizen scientists from 2021 through 2023 within Hong Kong waters. This is the first example where our curation team have worked with a Citizen Science project to share their observations in the GBIF biodiversity database.

Note to self (basically rewriting last year's Finding citations of specimens). Bibliographic data supports going from identifier to citation string and back again, so we can do a "round trip." 1. Given a DOI we can get structured data with a simple HTTP fetch, then use a tool such as citation.js to convert that data into a human-readable string in a variety of formats.

If you compare the impact that BHL and Plazi have on GBIF, then it's clear that BHL is almost invisible. Plazi has successfully in carved out a niche where they generate tens of thousands of datasets from text mining the taxonomic literature, whereas BHL is a participant in name only. It's not as if BHL lacks geographic data.

Somewhat stunned by the fact that my DNA barcode browser I described earlier was one of the (minor) prizewinners in this year's GBIF Ebbe Nielsen Challenge. For details on the winner and other place getters see ShinyBIOMOD wins 2020 GBIF Ebbe Nielsen Challenge. Obviously I'm biased, but it's nice to see the challenge inspiring creativity in biodiversity informatics. Congratulations to everyone who took part.

Motivated by the 2020 Ebbe Nielsen Challenge I've put together an interactive DNA barcode browser. The app is live at https://dna-barcode-browser.herokuapp.com. A naturalist from the 19th century would find little in GBIF that they weren’t familiar with. We have species in a Linnean hierarchy, their distributions plotted on a map.

The following is a guest post by Bob Mesibov. There's still time (to 31 March ) to enter a dataset in the 2020 Darwin Core Million, and by way of encouragement I'll celebrate here the best and worst Darwin Core datasets I've seen. The two best are real stand-outs because both are collections of IPT resources rather than one-off wonders. The first is published by the Peabody Museum of Natural History at Yale University.

The following is a guest post by Bob Mesibov. You're feeling pretty good about your institution's collections data.

Yes, this is a clickbait headline, and yes, it may seem like shooting fish in a barrel to complain about crappy data in GBIF, but my point here is raise concerns about the impact of metagenomic data on GBIF, and how difficult it may be to track down the causes of errors.

Imagine you are a fish ecologist who compiled a list of fish species for your country. 🐟 Your list could be useful to others, so you publish it as a supplementary file to an article or in a research repository. That is fantastic, but it might be difficult for others to discover your list or combine it with other lists of species.

I've put a short note up on bioRxiv about ways to geocode nucleotide sequences in databases such as GenBank. The preprint is "Geocoding genomic databases using GBIF" https://doi.org/10.1101/469650.