A bit over a year ago I got introduced to Qeios when I was asked to review an article by Michie, West, and Hasting: “Creating ontological definitions for use in science” (doi:10.32388/YGIF9B.2). I wrote up my thoughts after reading the paper, and the review was posted openly online and got a DOI. Not the first platform to do this (think F1000), but it is always nice to see some publishers taking publishing seriously.

And just like that, without a warning, Twitter changed policies again, and you now need a Twitter account and be logged in to see public tweets: Twitter has started blocking unregistered users (The Verge). Though I learned it first via Mastodon, of course. For example, this is what happens when you go to twitter.com/wikipathways: Fortunately, WikiPathways does have a Mastodon account, that anyone can see without having a Mastodon account.

Some years ago we started the ELIXIR Toxicology Community. It has been an interesting journey, partly covered in this whitepaper). We started with interaction we had in several projects already, but particularly the potential. I see this.

When last week in a large (and relevant) Dutch research event ChatGPT came up, and that this was going to change the world. Even the critiques came up, but were effectively disregarded with “these methods get better very quickly”. This is not untrue, but not really true either. I murmur “not even wrong”. I know how hard it is to get computers to find meaningful patters; I did a PhD in this in the early 21st century.

I think that if you want to make your knowledge FAIR, you should use an open license and RDF. Simple. Now, not everything is knowledge. A lot of data is, but a lot more is not, think raw data. Using RDF to explain a protein sequence is still something that makes me feel uneasy. However, first, you need to make RDF, you need to make assumptions explicit, you need to decide on meaning. Making RDF is not easy.

Okay, the Pilot is over ending with 17 papers, 16 of which have CiTO annotations (and so far 4 J.Cheminform. papers after the pilot), but my interest in the Citation Typing Ontology continues and we just need more adoption. Datasets as source of annotations So, here’s a quick Wikidata update.

Sometimes I think back about how Scholia started, and then I think I remember a Twitter discussion. Twitter was a social platform that was unable to fight hate speech. I left it last year in favor of Mastodon. Anyway, I did some digging today and found this thread from October 8-9 2016.

There are multiple initiatives to support the migration from Twitter to Mastodon (see also this blog post ). But Wikidata should not be forgotten here which has been tracking Mastodon accounts of things in their database: So, here are some Wikidata SPARQL queries to see the uptake: Universities with Mastodon All Mastodon accounts in Wikidata (or subset with also a Twitter account) Nobel Prize winners with Mastodon Academic journals

In August I reported about 2D depiction of (CX)SMILES in Wikidata via linkouts (going back to 2017). Based on a script by Magnus Manske, I wrote a Wikidata gadget that uses the same CDK Depict (VHP4Safety mirror) to depict the 2D structure in Wikidata itself: Note the depiction of the undefined (CIP) stereochemistry on two atoms. Thanks to Adriano and John for working that out.

Yeah, it has been hard to miss it (see e.g. Should I join Mastodon? A scientists’ guide to Twitter’s rival). Twitter is experiencing some turbulence and Mastodon has become a very attractive, open source, community-driven, inclusive alternative. It’s been around since 2016 and there is some research literature about it already. I got my account in 2018, but did not start actively using it until earlier this year.

Forget the journal impact factor and the H-index. You want your research being used. A first approximation of that is getting cited, sure. So, with the Nobel Prize week over (congrats to all winners! the Neanderthaler prize actually helped my work in Maastricht this week), let’s figure out of you are cited by a Nobel Prize winner.