One of the outcomes of the EpiLipidNET COST action is a paper about the data analysis of experimental lipidomics data: Guiding the choice of informatics software and tools for lipidomics research applications (doi:10.1038/s41592-022-01710-0).

Our BiGCaT team wrote up BridgeDb for identifier mapping and WikiPathways for pathways/enrichment analysis. See also the WikiPathways Lipids Portal.

But I also wanted to map the tools from the article to ELIXIR databases, particularly FAIRsharing, bio.tools, and TeSS. I wish journals would just require this as part of the wish to make science more FAIR. While at it, I realized I could also add Wikidata item annotations and link to Scholia (see also these blog posts).

So, I had a lot of teaching and that besides project deliverables and final reports, a few project meetings, it left me with little time to blog my monthly BridgeDb NWO grant update. But here goes, as a lot did happen in the background.

Identifiers are central to FAIR. Our BiGCaT research group (see also this Scholia page) studies how to answer biological questions and identifiers are then essential to integrate experimental data (e.g. omics data) with existing knowledge (e.g. biological pathway databases). BridgeDb is our go to tool here, of course.

BridgeDb has since Open PHACTS (doi:10.1016/j.drudis.2012.05.016) support for identifiers.org (doi:10.1093/bioinformatics/btaa864).

When you get asked to contribute your expertise, you do. To me, this is perfectly in line with doing open science. Sometimes it's a skill rather than knowledge. But when this helps a project that practices open science I would be insane not to. When you get asked to be listed as an co-author, it is an honor. Author lists have changed and much can be said about this. Essential contributions should be reflected in co-authorships.
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Currently, I am mostly working on chemistry in Wikidata but recently also validated CAS registry numbers in Wikipedia. Previously, I added many CompTox Dashboard identifiers to Wikidata. Now, Wikipedia ChemBoxes are slowly picking up more data from Wikidata, but are hesitant for a number of reasons. One is, I understood, that Wikipedia is meant to be self-consistent. That is, it should have all the information itself. Exceptions include images from WikiMedia Commons.

There are multiple initiatives to support the migration from Twitter to Mastodon (see also this blog post).

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.

More about CXSMILES in Wikidata in this Dagstuhl meeting results write up.

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.

While Nature calls for more action on open metadata (doi:10.1038/d41586-022-02915-1), the concept of open citations remains a central feature of our knowledge dissemination. After the wild success of the Initiative for Open Citations (and still excited that the ACS joined eventually!), it is time to move on. Currently, the open citation metadata is focusing towards journal articles (yeah, we've heard that before), but citations to research output in general is important.
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This blog deals with chemblaics in the broader sense. Chemblaics (pronounced chem-bla-ics) is the science that uses computers to solve problems in chemistry, biochemistry and related fields. The big difference between chemblaics and areas such as chem(o)?informatics, chemometrics, computational chemistry, etc, is that chemblaics only uses open source software, open data, and open standards, making experimental results reproducible and validatable. And this is a big difference!
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