Let’s start with what the TDCC actually are: they are Thematic Digital Competence Centres:

The Thematic Digital Competence Centres (TDCCs) are network-based initiatives set up by NWO and the Dutch academic community to broker investments into research data management projects. The three TDCCs are national and discipline based, with one pillar each for the Social Sciences & Humanities (SSH), Natural and Engineering Sciences (NES) and Life Sciences & Health (LSH). The networks will help formulate and facilitate projects designed to promote the adoption of open data, software and research practices, alongside the development of the necessary expertise.

So, where initiatives like GO FAIR had centers of competencies (the implementation networks), they did not have funding for them. This was a main reason why the Chemistry Implementation Network (ChIN, doi:10.1162/dint_a_00035) did not take off. The TDCCs do not provide a lot of money, but enough to support disseminating expertise and promote some key ideas.

The idea is that combined with other efforts, it strengthens the level of FAIR in the Dutch research community. I have to say, this is much needed, as the level of FAIR data in journal publications is so much to wish for, and still mostly absent.

The FAIR4ChemNL project already had a networking activity during the writing of the proposal, the workshop already back in 2024 that I blogged about earlier (see also this report). The FAIRify project is coordinated by the group that was key in the Netherlands Metabolomics Center (NMC), now the BeneLux Metabolomics Center. During a postdoc at the NMC during my Wageningen days, we already did a lot of FAIR competency building with the MetWare project.

The Col-Lab Retreat

The TDCC-NES organized a networking event in August last year, the 2025 TDCC-NES Col-Lab Retreat. I am late with reporting on it, but there simply was too much project management that took priority. The meeting was in the wonderful Dutch town Schoorl, and the location is great for collaborative meetings. I had been there a year earlier for an Open Science Retreat and was happy to go back.

During the unconference-style meeting various topics were discussed in breakout groups, and because of the two TDCC projects, I was particularly interested in the Metadata and interoperability topic. Partly because this is how we can make eletronic lab notebooks automatically push metadata to registries (and Rory Macneil was also in Schoorl, of RSpace/ResearchSpace which already integrated with various open platforms), and partly because I wanted to continue explore nanopublications with Christian Meesters, which could be the envelope to distribute the metadata. For the last, I was looking at the Java library for nanopublications (see this PR.

The idea that ELNs automatically share metadata about experiments is something that is attractive. It would require no involvement from the researcher, would be fully automatic, and drive interest (users, peer reviewers) to experiments and experimental data. Something that is still absurdly hard is to do a search for experiments that measured the melting point of some chemical. How awesome would it be if ELNs would automatically register chemicals from the experiment in, for example, PubChem.

We had the idea of applying for a Lorentz Workshop, but the earliest deadline was too early, but maybe it is time to pick up that idea again. Interoperability standards already exist, like the aforementioned nanopubs, but also RO-Crates that are also studied by Jente Houweling in the VHP4Safety project (see this Data tab for a preview).

I do not remember exactly, but I guess we must have met on the 28th and 29th? Maybe already on Wednesday. During this meeting we discussed a common data model (yes, Jmol used the CDK data model at some point) and somewhere during the meeting we wrote down a name for the project. There was the Java Development Kit, so this could be the Chemistry Development Kit. The name stuck.

A quick post like this cannot do credit to the history of the CDK, nor of everyone involved in the past or still is. You can browse some of the history of the CDK in my blog and in Chris’ blog. It has been an amazing journey and with a small grant just behind us (with Alyanne de Haan, René van der Ploeg, and Marc Teunis from Hogeschool Utrecht), and all the awesome things ongoing (new JChemPaint, various extensions, upgraded downstream tools), the CDK is alive and kicking.

A huge congrats and thanks to everyone (and every company and organization) who contributed code to the CDK with this huge milestone. There are a few people that I want to particularly thank (see the AUTHORS file for all names): Chris, who in the late nineties made a difference with open source in chemistry, Dan, for Jmol and hosting this memorable meeting at Notre Dame University, Rajarshi Guha, who operated CDK Nightly for many years, well before Travis and Google Actions, Stefan, Miguel, Gilleain, and Christian, for many years of contributions to the CDK, and John Mayfield, the current CDK release manager.

The history tells me that our society may sound woke and leftish, in reality it is a continous fight for basic human rights. (Something that plenty have been saying for years.) In this case, a healtht life is the human right.

So, what can I do to make this report more FAIR?

Findable in Wikidata

Since this report has been mentioned in the news, it clearly is notable. The simplest thing to do is thus to just add it Wikidata. Because the DOI of the report had not been recorded yet, I could not let Scholia do it for me. But doing it manually is only a bit more work: Q135222054. The provided metadata on the RIVM website is minimal.

But we can do more. Particularly, because I want people to find this report when they look info knowledge about the 28 studied chemicals, I added main subject annotation using the information in Table 1 in the report. Using Scholia and the CAS registry number in the table, I crosscheck the information in Wikidata is consistent with the report (and visa versa). It was. I then added the Dutch name and acronym for most of them. Some already had the name as in the Table. That gives us a nice “Topic scores” plot for the Scholia page of the report:

The central PFAS bubble is also only one main subject but larger because many the specific PFAS compounds are subclassing PFAS. And you may also note many smaller bubbles. These actually come from main subject annotations of articles cited from the report. Because I added a few of them too. Not all, because many are not in Wikidata (yet).

Findable in WikiPathways

But since 16 of these compounds are readily found in human blood samples, that is handy knowledge when doing metabolomics (on blood samples). Or (and I leave that to later blog post), we can map the experimental data for Dordrecht versus the rest of The Netherlands to the PFAS compounds. That is relevant to research by VHP4Safety. There are many ways to see if you have PFAS in your dataset, but since we have many controlled lists of genes in metabolites, I added one for common PFAS in human blood samples. Well, the 16 common in Dutch blood samples:

Each metabolite here is annotated with their Wikidata identifier, allowing us to map experimental data on top of it. And we get links out to other databases almost for free:

And the link to Wikidata actually links to Scholia, so for the PFOA in the above example, we can quickly see the boiling point, decomposition point, and melting point of this PFAS. And literature with undoubtedly even more knowledge about this PFAS:

Now, these two steps were mostly manual: drawing WP5579 in WikiPathways and adding the report annotations (main subject and cites) in Wikidata.

Findable in the VHP4Safety Compound Wiki

As part of the VHP4Safety project, I am collecting information on chemicals studied in the context of toxicology, safety, and risk assessment. Often specific collections of compounds studied as a whole. This report is such a collection and provides experimental data on these compounds. So, I want this report to be findable for the VHP4Safety Compound Wiki too. Creating the collection is a manual step: Q5145.

Now, because both Wikidata and our VHP4Safety Compound Wiki (a Wikibase instance) are semantic and support, I can use SPARQL to create instructions to link the 28 compounds to the new collection. Now, arguably, that can be done manually too, and maybe faster, for larger collections this is harder. So, I dug up my earlier notes and got some useful things together.

This query lists all 28 PFAS linked to the report in Wikidata:

SELECT ?pfas ?pfasLabel WHERE {
  wd:Q135222054 wdt:P921 ?pfas .
  ?pfas wdt:P31 wd:Q113145171 .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],mul,en". }
}

Using federation powers, I can use this for a SPARQL query, and return the results in QuickStatements that say this VHP compound is part of the VHP collection:

PREFIX wb: <https://compoundcloud.wikibase.cloud/entity/>
PREFIX wbt: <https://compoundcloud.wikibase.cloud/prop/direct/>

SELECT (SUBSTR(STR(?cmp),45) AS ?qid) ?P21 WHERE {
  ?cmp wbt:P5 ?wikidata .
  SERVICE <https://query.wikidata.org/sparql> {
    wd:Q135222054 wdt:P921 ?pfas .
    ?pfas wdt:P31 wd:Q113145171 .
    BIND (substr(str(?pfas),32) AS ?wikidata)
  }
  BIND ("Q5145" AS ?P21)
}

I actually had to add 5 PFAS compounds in the VHP4Safety Compound Wiki first. That follows the same procedure for how I have been adding chemical compounds to Wikidata (see also this preprint). The input cas.smi has the (missing) SMILES, Wikidata QID, and English label:

C(CS(=O)(=O)O)C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F       Q27063662       6:2 Fluorotelomer sulfonate
CN(CC(=O)O)S(=O)(=O)C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F Q126605979      MeFHxSAA
CN(CC(=O)O)S(=O)(=O)C(C(C(C(F)(F)F)(F)F)(F)F)(F)F       Q126682412      MeFBSAA
C(=O)(C(C(F)(F)F)(F)OC(C(C(F)(F)F)(F)F)(F)F)O[H]        Q29387971       2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)propanoic acid
C(C(C(=O)O)(F)F)(OC(C(C(OC(F)(F)F)(F)F)(F)F)(F)F)F      Q81981675       4,8-Dioxa-3H-perfluorononanoic acid

For reference, this is the command line I used to create QuickStatement instructions:

groovy createWDitemsFromSMILES.groovy -w compoundcloud.wikibase.cloud -c Q2368 -d P5 -l -i wikidata -a P11

Final remark

Are these 16 the only PFAS in our body? With 28 studied out of a potential seven million, I doubt it.

In turn, that poster described work of at least three years, going back to adding identifiers in 2015 and chemical structures in early 2016. I started using scripts two months later. This helped a lot with migrating pKa data from a custom Semantic MediaWiki installation to Wikidata and with adding thousands of EPA CompTox identifiers in 2017.

But that 2018 conference paper never happened. Because Scholia did. And even on the ICCS poster, Scholia was used to visualize chemistry data in Wikidata. To be honest, not just that, of course. About a year ago I had a serious go at finishing the paper, and it was sent around to co-authors. But I realized at the time, that the paper was lacking some good suggestions how the peer review our actual contributions to Wikidata. I could hardly expect readers of the paper browse the individual histories of all, by then, 1.3 million chemical compounds. And during the holidays I collected a few tools, which I had lined up to add to the manuscript.

However, another thing happened, the COVID-19 pandemic. While all the experience helped a lot with getting knowledge together around SARS-CoV-2, it also made something else clear: the software behind Wikidata does not scale well (enough). This lead to plans to split the RDF graph representation into two separate SPARQL endpoints. And that breaks many, if not most, of Scholia’s SPARQL queries, including those for the chemistry aspects. The situation in Summer 2024 was that there was a significant chance Scholia would not survive the split. And the Scholia Chemistry paper had to wait. You cannot publish an article of which the website is gone before it is formally accepted.

Let me make clear, this graph split is not solved and the risk is not gone. But a serious of unfunded, weekend hackathons allows us to refactor Scholia to give us a chance. It started with making Scholia more configurable. We had the first hackathons in October and November, and I had four more hackathon weekends this April.

The graph split into a main graph and a scholarly graph happened on May 9. Currently, we have been granted extra time and can use a legacy server with the full graph, but a lot less hardware, so slower. A final patch, merged in last week, allows us to define which SPARQL endpoint a query should run. So, each time we port a SPARQL query, we can directly update Scholia, making the migration somewhat more manageable.

But, with those uncertainties out of the way, it was time to finish the Scholia Chemistry paper!

The preprint (doi:10.26434/chemrxiv-2025-53n0w) brings 10 years of research together, and describes details of the used methods not formally peer-reviewed before. We describe in detail how chemical structures are added, the choices of Wikidata on how to represent chemical structures, how we curate the quality, and how we visualize chemical structures and data with Scholia. As you can expect, the Chemistry Development Kit has an important role, along with the InChI.

The paper introduces three new Scholia aspects for chemicals, chemical classes, and elements. Each aspect is a template for a page with information about molecular entities and chemical substances, compound classes (like fatty acids), and elements (like carbon). Each template provides relevant information. Of course, any compound, class, or element can also still be opened in the Scholia “topic” aspect, listing relevant literature.

With this paper we aim to show that Wikidata is a innovative platform that meets the needs for a chemical structure database, with detailed data provenance, and scalable community curation.

I welcome your strongest peer review on the preprint. I don’t liking settling for anything less. Here’s the abstract:

Sharing knowledge on chemicals in the digital age has been the playground of databases such as the Chemical Abstract Services and PubChem. Wikipedia complements this field by providing context to chemicals aimed at a broad audience, but is not easily read by machines. Wikidata was started as a database service to improve the machine readability of the knowledge captured in Wikipedia. Wikidata has an open license, application programming interfaces, and a strong provenance model. Scholia uses the features to provide access to chemical knowledge. This study reviews the chemistry in Wikidata, shows how thousands of new chemicals were added, extends Wikidata with new properties for chemical representation and external links to additional databases, and shows how we extended Scholia to represent the chemistry in Wikidata.

Thanks to Finn, Denise, Daniel, and Adriano!

We streamline the discoverability of your research by incorporating machine-readable chemical information into many of our published articles. This includes the conversion of chemical structures from submitted ChemDraw files to InChI strings and validating them using open-source tools.

The idea is far from new and has been around for two decades. But the two Beilstein journals (both diamond Open Access), actually integrated into their active publishing model. That has been trialed and put in action before. For example, there was (is?) Project Prospect (2007), chemical structure annotation in Nature Chemistry (2009), SMILES in the ACS Journal of Medicinal Chemistry (2014) (doi:10.1021/jm5002056), and FAIR chemical structures in the Journal of Cheminformatics (2021) (doi:10.1186/s13321-021-00520-4).

But this announcement is a new step. I like how validation of the chemical structures is part of the approach, and I like how they use the Bioschemas extention of schema.org. The last because they use two Bioschemas types/profiles that contributed to or initiated, respectively: MolecularEntity and ChemicalSubstance.

First stop for me is to check the schema.org annotation with a validation tool, like Google’s Rich Results Test. That gives an idea how they may have have their search engine pick it up. The test article I was given on LinkedIn is Xiao et al.’s Molecular diversity of the reactions of MBH carbonates of isatins and various nucleophiles (doi:10.3762/bjoc.21.21) in the Beilstein Journal of Organic Chemistry, and we indeed see the schema.org annotation show up:

And because of the use of open standards, extracting the information is not so hard with, for example here, Bacting (doi:10.21105/joss.02558), based on a 2022 script from the NanoSafety Cluster projects NanoCommons and SbD4Nano:

@Grab(group='io.github.egonw.bacting', module='managers-rdf', version='1.0.4')
@Grab(group='io.github.egonw.bacting', module='managers-ui', version='1.0.4')
@Grab(group='io.github.egonw.bacting', module='net.bioclipse.managers.jsoup', version='1.0.4')

bioclipse = new net.bioclipse.managers.BioclipseManager(".");
rdf = new net.bioclipse.managers.RDFManager(".");
jsoup = new net.bioclipse.managers.JSoupManager(".");

articles = [
   args[0]
]

kg = rdf.createInMemoryStore()

for (article in articles) {
    htmlContent = bioclipse.download(article)

    htmlDom = jsoup.parseString(htmlContent)

    // application/ld+json

    bioschemasSections = jsoup.select(htmlDom, "script[type='application/ld+json']");

    for (section in bioschemasSections) {
        bioschemasJSON = section.html()
        rdf.importFromString(kg, bioschemasJSON, "JSON-LD")
    }
}

turtle = rdf.asTurtle(kg);

println "#" + rdf.size(kg) + " triples detected in the JSON-LD"
// println turtle


sparql = """
PREFIX schema: <http://schema.org/>
SELECT ?entity ?inchikey ?smiles WHERE {
  ?entity a schema:MolecularEntity .
  OPTIONAL { ?entity schema:inChIKey ?inchikey }
  OPTIONAL { ?entity schema:smiles ?smiles }
}
"""

results = rdf.sparql(kg, sparql)

for (i=1;i<=results.rowCount;i++) {
  println "${results.get(i, "inchikey")}\t${results.get(i, "smiles")}"
}

The output is a simple table:

MGAPJMNPGGTFHJ-JEIPZWNWSA-N     CN1C(=O)/C(=C/2\C3=CC(=CC=C3N(CC4=CC=CC=C4)C2=O)Cl)/C(=P(C5=CC=CC=C5)(C6=CC=CC=C6)C7=CC=CC=C7)C1=O
XEWMQVUVGAHESA-UHFFFAOYSA-N     CC1=CC=C(C=C1)NC2=C(C3C4=CC(=CC=C4N(CC5=CC=CC=C5)C3=O)C)C(=O)N(C)C2=O
UVTJORFYHPGJDZ-PYCFMQQDSA-N     CCCCN1C2=CC=C(C)C=C2/C(=C(\C#N)/CNC3=CC=C(C)C=C3)/C1=O
ILWGDUYVQRAMMG-PGMHBOJBSA-N     CCCCN1C2=CC=C(C)C=C2/C(=C(\C#N)/CNC3=CC=C(C=C3)Cl)/C1=O
CAFIBKBZWJFZCW-FXBPSFAMSA-N     CCCCN1C2=CC=C(C)C=C2/C(=C(\C#N)/CNC3=CC=CC=C3)/C1=O
UOJSFLANMVIMBV-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=C(C=C4)Cl)C1=O
VNJBTGZXAGHCSO-OAPYJULQSA-N     COC(=O)/C(=C\1/C2=C(C=CC=C2)N(CC3=CC=CC=C3)C1=O)/C=P(C4=CC=CC=C4)(C5=CC=CC=C5)C6=CC=CC=C6
KJXQRAKSOANQTJ-GFMRDNFCSA-N     CC1=CC=C(C=C1)NC/C(=C\2/C3=C(C=CC=C3)N(CC4=CC=CC=C4)C2=O)/C#N
IGEBJMZDOPBFGF-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=CC=C4)C1=O
SSANVPNESOMKOM-AWQADKOQSA-N     C1=CC=C(C=C1)CN2C3=CC=C(C=C3/C(=C(/C#N)\C=P(C4=CC=CC=C4)(C5=CC=CC=C5)C6=CC=CC=C6)/C2=O)Cl
GEHWHSHQSIOZKL-NVQSTNCTSA-N     CCCCN1C2=CC=C(C=C2/C(=C\3/C(=P(C4=CC=CC=C4)(C5=CC=CC=C5)C6=CC=CC=C6)C(=O)N(C)C3=O)/C1=O)Cl
PALRSQOHFLRWDH-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=C(C=C4)OC)C1=O
KBFODZMDSAFLFR-UHFFFAOYSA-N     CN1C(=O)C(=C(C1=O)NC2=CC(=CC=C2)Cl)C3C4=CC(=CC=C4N(CC5=CC=CC=C5)C3=O)Cl
JCGAVVZYXDJPBU-GFMRDNFCSA-N     CC1=C(C=CC=C1)NC/C(=C\2/C3=C(C=CC=C3)N(CC4=CC=CC=C4)C2=O)/C#N
DZFPCPDEQGLPLY-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=C(C)C=C4)C1=O
XMRNJCJUOXYXJU-DAFNUICNSA-N     CC1=CC=C(C=C1)NC/C(=C\2/C3=CC(=CC=C3N(CC4=CC=CC=C4)C2=O)C)/C#N
SSDSNBBHEUUKGI-UHFFFAOYSA-N     CC1=CC=C2C(=C1)C(C3=C(C(=O)N(C)C3=O)N(C)C4=CC=CC=C4)C(=O)N2CC5=CC=CC=C5
USFYPRDMNXMWPO-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=C(C=C4)Br)C1=O
XYHTWFULRHTEAG-MUGXBBEHSA-N     CCCCN1C2=CC=C(C)C=C2/C(=C(/C#N)\C=P(C3=CC=CC=C3)(C4=CC=CC=C4)C5=CC=CC=C5)/C1=O
XALDZIBHNNIVAM-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NC4=C(C=CC=C4)O)C1=O
TUTWQHBRQPMLME-OAPYJULQSA-N     COC(=O)/C(=C\1/C2=CC(=CC=C2N(CC3=CC=CC=C3)C1=O)Cl)/C=P(C4=CC=CC=C4)(C5=CC=CC=C5)C6=CC=CC=C6
IYEHFTMZZMIPRU-UHFFFAOYSA-N     CC1=CC=C(C=C1)NC2=C(C3C4=CC(=CC=C4N(CC5=CC=CC=C5)C3=O)Cl)C(=O)N(C)C2=O
KBSDGNPLIPXCEX-UHFFFAOYSA-N     CCCCN1C2=CC=C(C)C=C2C(C3=C(C(=O)N(C)C3=O)NCC4=CC=CC=C4)C1=O
BQGIUMITIGHBSD-UHFFFAOYSA-N     CCCCNC1=C(C2C3=CC(=CC=C3N(CC4=CC=CC=C4)C2=O)C)C(=O)N(C)C1=O
PNSOLOPHIVUPOZ-MNDPQUGUSA-N     CCCCNC/C(=C\1/C2=CC(=CC=C2N(CCCC)C1=O)C)/C#N
HLTBKJRJOIZCMJ-PYCFMQQDSA-N     CCCCN1C2=CC=C(C)C=C2/C(=C(\C#N)/CN(C)C3=CC=CC=C3)/C1=O
FFLHFLUBMRBQTB-UHFFFAOYSA-N     CCCCN1C2=CC=C(C=C2C(C3=C(C(=O)N(C)C3=O)NC4=CC=C(C)C=C4)C1=O)F
FOQOVOLYYARWPA-NKFKGCMQSA-N     C1=CC=C(C=C1)CN2C3=C(C=CC=C3)/C(=C(\C#N)/CNC4=CC(=CC=C4)Cl)/C2=O
KLEPCAQFOXJLNV-UHFFFAOYSA-N     CC1=C(C=CC=C1)NC2=C(C3C4=CC(=CC=C4N(CC5=CC=CC=C5)C3=O)Cl)C(=O)N(C)C2=O

That also made me realize that there are not chemical names in the annotation. That would be really useful to move things forward. Then again, PubChem will likely just generate the IUPAC name, since they have access to such software anyway. They have teamed up with PubChem which will index it, but I will be interested in seeing how to use this for main subject annotation in Wikidata.

A final note for now, the model they use is annotate the article with chemical substances (ChemicalSubstance) with (one or more?) molecular entities (`MolecularEntity’). That is a model that scales well to their other journal, the Beilstein Journal of Nanotechnology. But scraping that is for another post.

But in the past two weeks I did attend two workshops. The first was a workshop by the ELIXIR Toxicology Community, which was held in Utrecht/NL. The programme was around FAIR and included two really nice hands-on sessions where we developed drafts for FAIR Cookbook recipes (see also doi:10.1038/s41597-023-02166-3) and for FAIR Implementation Profiles (doi:10.1007/978-3-030-65847-2_13). We will write up a BioHackrXiv report.

The second workshop was last week, the FAIR4ChemNL workshop, which was also held in Utrecht/NL. The topic was FAIR in chemistry, and we discussed various aspects. There was a significant participant group from the German NFDI4Cat project (“Cat” is short for (chemical) catalysis), which recently published a nice analysis of several ontologies (doi:10.1186/s13321-024-00807-2). And there was also a lot of mention of RDF and SPARQL.

I think it is time for a new special issue around semantic web technologies.

I just need a few SPARQL queries to list to which statements the qualifiers needs to be added. Basically, all boiling points which has the book as a reference and that do not have the pressure info. First, there are values with ‘unknown value’, which results in blank nodes (by the time you read this, they likely are already fixed):

SELECT ?cmp ?bp ?pressure WHERE {
  ?cmp p:P2102 ?bpStatement .
  ?bpStatement prov:wasDerivedFrom/pr:P248 wd:Q22236188 ;
    ps:P2102 ?bp .
  ?bpStatement pq:P2077 ?pressure .
  FILTER (contains(str(?pressure), "http://"))
}

So, to get the list for which I want to write the QuickStatements which does not have any P2077 qualifier yet, I use this query:

SELECT ?cmp WHERE {
  ?cmp p:P2102 ?bpStatement .
  ?bpStatement prov:wasDerivedFrom/pr:P248 wd:Q22236188 ;
    ps:P2102 ?bp .
  MINUS { ?bpStatement pq:P2077 ?pressure }
}

At the time of writing, this lists 54 boiling points.

I can the WDQS create CSV-styled QuickStatements with:

SELECT (SUBSTR(STR(?cmp),32) AS ?qid) ?P2102 ?qal2077 WHERE {
  ?cmp p:P2102 ?bpStatement .
  ?bpStatement prov:wasDerivedFrom/pr:P248 wd:Q22236188 ;
    ps:P2102 ?P2102 .
  MINUS { ?bpStatement pq:P2077 ?pressure }
  BIND ("101.325U21064807" AS ?qal2077)
}

Here, the SPARQL variables double as QuickStatement instructions. Finally, note to use of “U21064807” which is the Wikidata item for kilopascal (wikidata:Q21064807).

I also need to “add” the boiling point again, to make sure QuickStatements knows which statement to add the qualifier to. I think this can be done better, but not sure how to target statements directly. This is not fool proof: I noted that this approach ignores the situation where there are two statements with the (exact) same boiling point, but different error margins. But that I will monitor and where needed correct manually.

So, when I was asked to work with Chemical Abstracts Service (CAS) on a new, bigger than ever version of Common Chemistry (which started as a project between CAS and Wikipedia), I welcomed the project. I don’t quite remember the first meetings, but roughly my task became to work with the new content and match this against Wikidata and Wikipedia. It aligned well with BridgeDb, Scholia, and our metabolomics research, so I even could find sufficient research time for it. This work is now published in the JCIM: CAS Common Chemistry in 2021: Expanding Access to Trusted Chemical Information for the Scientific Community (doi:10.1021/acs.jcim.2c00268).

Figure 2 from the article. Detailed record for caffeine in CAS Common Chemistry (image: CC-BY).

About Wikidata, the paper writes (CC-BY):

The latest release of CAS Common Chemistry has also supported updates and corrections to CAS RNs in Wikidata and Wikipedia. (22) InChIKeys were calculated from CAS SMILES using Bacting 0.0.31 (23) with the Chemistry Development Kit 2.7.1 (24) and were matched with content in Wikidata. The CAS RNs were then compared. References to CAS Common Chemistry were added for CAS RNs that matched. Mismatches have been shared with the Wikidata and Wikipedia communities so that they can manually review and correct the misleading entries using CAS Common Chemistry as a reference. Because Wikidata also curates identifiers from other data sources, validated CAS RNs in Wikidata may also be used to cross-reference with other resources. Scripts are provided in the Supporting Information.

The alignment is a continuous process, as new chemical compounds get added to Wikidata on a weekly basis. The comparison of Common Chemistry with Wikidata and Wikipedia resulted in a wealth of curation data, e.g. inconsistent CAS numbers linked to InChIKeys, where Common Chemistry had a different match than Wikidata or Wikipedia.

CAS registry numbers were not added to Wikidata in this process, only confirmed or reported as different. The latter allowed manual curation by the community, which it did. Reports look like this. When a InChIKey-CAS RN combination in Wikidata was confirmed, it was recorded as a reference, like this:

Screenshot of Wikidata with two references, one reflecting a confirmation by the English Wikipedia (potentially the result of the original Common Chemistry project) and the second as outcome of the now published project.

Thanks to everyone on this project and Andrea Jacobs particularly for leading this open science project.

Twitter profile.

It giet oan! That it a Frisian phrase for something unlike is going to happen, like and particularly related to the Elfstedentocht.

ChemCuration 2019 is a go. The website is online, the Twitter account and hashtag are ready, we got a poster prize, and here is the call for posters!

On December 3 the first ChemCuration conference will take place. ChemCuration 2019 is a one day, online-only conference around data curation and curated data in the chemistry domain. During the entire conference day, you can participate by tweeting about the poster that you uploaded, along with the meeting hashtag, and responding to questions about your poster in the 24 hours of the conference day. The poster must be available in an online repository (e.g. Zenodo or Figshare) under the CCZero, CC-BY or CC-BY-SA license prior to the conference.

This is the meeting scope: anything around data curation and curated data of open science data in chemistry. This includes but is not limited to: 1. a new release of curated open data; 2. FAIR metadata around open data; and 3. open source tools for data curation.

How do I participate in ChemCuration?
You can participate in this online poster conference by presenting your poster on Twitter during the conference day. You do this by first archiving your poster via Figshare or Zenodo, with an open license (e.g. CCZero or CC-BY). Then, during the day you tweet an image of (part of) your digital poster with the #chemcur2019 hashtag, a short summary, and a link to your online poster with its DOI. The archived poster should be a regular A0 poster (WxH = 841 x 1189 mm or 33.1 x 46.8 in)

Do I need to register?
Registration is not obligatory to participate. However, if you would like to be eligible for a poster prize, then registration is required, by Nov. 30th, 2019. The registration form is found at https://github.com/chemcuration/chemcuration2019/issues/new/choose

More information can be found on the website (https://chemcuration.github.io/chemcuration2019/) and on Twitter https://twitter.com/chemcuration

Glucuronide functional group.

Now that the ChemCuration 2019 online poster conference is nearing, and my upcoming talks about chemistry in Wikidata (also needing curation), and the much longer process of curation of metabolite (-like) structures in WikiPathways, I decided that something I tweeted earlier this week is actually quite useful, and therefore something I should really write up in my lab notebook.

Glucuronide is an example (biological) functional group. And there are several databases that represent the stereochemistry now always correct. That is an interoperability (and thus FAIR) problem. Correcting this is not trivial, particularly if you have to redraw the same glucuronide group again and again.

So, not looking forward to that, I invested a bit of time to find a SMILES trick. What if I had a SMILES snippet that I could easily copy/paste and attach to the SMILES of the chemical structure it is attached to? Here goes.

O1[C@H](C(O)=O)[C@H]([C@H](O)[C@@H](O)[C@@H]1O9)O.

I just realized that the original 3 I used can better be a 9, which is less likely to occur in the SMILES of the rest of the molecule. The period at the end is also deliberate. That way, I can just copy past the SMILES of the rest directly after that period. Then I get a disconnected structure, but I only have to put a 9 next to the atom that is binding to the glucuronide. So, let’s see the R group is methane, I get:

O1[C@H](C(O)=O)[C@H]([C@H](O)[C@@H](O)[C@@H]1O9)O.C9

Now, next stop: CoA and other common biological tags.