Scholarly articles provide context to the factualness of statements in Wikidata, similar to the [citation needed] in Wikipedia. And just like the cited references in each scholarly article itself. The citation network is general seen as an essential part of (doing) science, even without citation intention annotation.
Scholia is a visual layer on top of Wikidata providing a rich user experience for browing scholarly research related knowledge. I am using the combinatie for various things, including exploring new research topics (a method, compound, or protein I do not know so much about yet), indexing notable research output (including citations), progress of Citation Typing Ontology uptake, etc.
Kasabi was an innovative RDF publishing platform from around 2011. Shortlived, and maybe just too early. I published two open datasets there. One was ChEMBL-RDF (see these posts). The second was a small data sets called ChemPedia, a open science effort to crowdsource chemical names. This is still very much needed, and possibly Wikidata could fill that gap, but it would first need to be able to handle all labels as statements itself.
Diabetes was already discussed in literature back in 1838-1839 (doi:10.1016/S0140-6736(02)96038-1, doi:10.1016/S0140-6736(02)96066-6, and doi:10.1016/S0140-6736(02)83966-6). These three papers show a short discussion.
This summer I am trying to finish up some smaller projects that I did not have time for to finish, with mixed successes. I am combing this with a nice Dutch staycation, and I already cycled in Overijssel and in south-west Friesland and learning about their histories. But this post is about an update on my Citation Typing Ontology use cases. And I have to say, a mention by Silvio Peroni is pretty awesome, thanks! First, the bad news.
I am still catching up with a lot of work, and found out I actually had forgotten to blog about this cool article by Denise Slenter: “Discovering life’s directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool” (doi:10.1039/D3DD00069A). This paper explains how various open science resources (Wikidata, Reactome, WikiPathways) are used to visualize the biological story of the data from two metabolomics experiments
About a year ago I started migrating my blogger.com blog to a git-version-controlled, Markdown-based blogging platform. I have to say, it has been a happy year. It actually is awesome to port old blog posts (follow that here) and to see what I have been working on some 17, 18 years ago. I do have a nasty bug to fix that causes the conversion of the Markdown to HTML is scaling badly.
In the CDK2024 grant we wrote about updating various software projects using the Chemistry Development Kit. We even wrote that “[r]equired API changes will be publicly shared and disseminated with the Groovy Cheminformatics with the Chemistry Development Kit book (egonw.github.io/cdkbook/)”. The Groovy Cheminformatics with the Chemistry Development Kit book is a project that has run since 2009.
Ammar is finishing up his PhD thesis with his research on the use of FAIR towards predictive toxicology. Or, “AI ready”, as the term FAIR is now sometimes explained. Any computational method needs good data, and just FAIR is not enough. It needs to meet community standards, as formalized in R1.3. To me, this includes meeting community standards like minimal reporting standards.
Noting that in the coming week I am not attending the ELIXIR All Hands in Uppsala. Having lived in (and around) Uppsala for more than three years, I am disappointed and with the first stories from colleagues coming in even more. But it has been a way too busy year, I have much to finish up, and I need to take care of myself too. I am not 32 anymore. But in the past two weeks I did attend two workshops.