A couple of weeks ago I was grumpy on the Internet (no, really) and complained about museum websites and how their pages often lacked vital metadata tags (such as rel=canonical or Facebook Open Graph tags). This got a response: Vince's lovely line "diddle with semantic data" is the inspiration for the title of this post, in which I describe a tool to display links across datasets, such as museum specimens and scientific publications.

These are simply notes to myself about taxonomic classifications in Wikidata. Classifications in Wikidata can be complex and are often not trees. For example, if we trace the parents of the frog family Leptodactylidae back we get a graph like this: Each oval represents a taxon in Wikidata, and each arrow connects a taxon to its parent(s) in Wikidata.

Given my renewed enthusiasm for Wikidata, I'm trying to get my head around the way that Wikidata models biological taxonomy. As a first pass, here's a diagram of the properties linked to a taxonomic name. The model is fairly comprehensive, it includes relationships between names (e.g, basionym, protonym, replacement), between taxa (e.g., parent taxon), and links to the literature.

Came across Microsoft's announcement of a "A planetary computer for a sustainable future through the power of AI", complete with a glossy video featuring Lucas Joppa @lucasjoppa (see also @Microsoft_Green and #AIforEarth). On the one hand it's great to see super smart people with lots of resources tackling important questions, but it's hard not to escape the feeling that this is the classic technology company approach of framing difficult

I haven't posted on iPhylo for a while, and since my last post back in January things have obviously changed quite a bit. In late January and early February I was teaching a course on biodiversity informatics, and students discovered the John Hopkins coronavirus dashboard, which seemed like a cool way to display information on a situation that was happening on the other side of the world. All fairly abstract.

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.

Just a note that ORCID serves data using terms from schema.org, and has done for a while (since April 2018), but somehow I missed this. You can get linked data in JSON-LD using content negotiation.

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.

This week I attended the SWAT4(HC)LS (Semantic Web Applications and Tools for Healthcare and Life Sciences) meeting in Edinburgh. Although a relatively small meeting, SWAT4(HC)LS attracts some big names in the field and featured keynotes by Denny Vrandečić (founder of Wikidata), Dov Greenbaum, Birgitta König-Ries, and Helen Parkinson.

It’s been a while since I’ve posted on iPhylo. Since returning from a fun and productive time in Australia there have been a bunch of professional and personal things that have needed attending too. In amongst all this I attended Biodiversity Next in Leiden, a large (by biodiversity informatics standards) conference with the tag line "Building a global infrastructure for biodiversity data.