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).

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 this paper (doi:10.1038/s41597-024-03324-x), Ammar explores this notion and compiled more than 200 maturity indicators in the category R1.3 resulting from 12 different community standards. For example, this includes minimal reporting standards. There is overlap in needs, but they often also have a different focus. The conclusion here: different (re)use cases have different needs, and data not usable to one use case can be sufficiently FAIR for another. Of course, ideally, it would be FAIR enough for all use cases.

Ammar formalizes the maturity indicators and links the comming maturity indicators to various use cases. That means that when you determine the indicator values for your data, people can immediately lookup how this data can be reused. And, the generator of the data can immediately see how the data would need to be improved to widen the reusability. How FAIR can we get?

His proposal has already been further explored in two other papers, one around data sharing (doi:10.1038/s41596-024-00993-1, see also this blog post) and one around QSAR modelling (doi:10.1016/j.impact.2023.100475, see also this blog post).

The below screenshot shows what an analysis using this approach can look like:

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.

So, there we are. This is where spreadsheets come in. If done well, they combine aspects of knowledge graphs with usability and can even cover a good bit of the learnability. This is what is described in this new paper about templates in the EU NanoSafety Cluster: A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials (doi:10.1038/s41596-024-00993-1).

The learnability comes in with the spreadsheet templates (“this is how we did it”) and a “wizard” around it guides the user with the selection of a template but also can provide feedback on the template. The technical term for that is “validator”, but it can be tought of as a spelling checker. Computers are good at finding contradictions (the lack of a pattern), though less good at ranking the alternatives (which is the cause of hallucinations in AI approaches).

And to return to the RDF, software like AMBIT can read these templates, use the semantics linked to the template, and make the FAIR static spreadsheets (good for archiving on Zenodo!) available as FAIR interactive data (good for exploration and machine learning), and as RDF (good for data integration).

Congrats to Nina and the various EU NanoSafety Cluster projects!

The idea was simple: write up which nanomaterial (type) activates which molecular initiating event. It would simply annotate each material with a unique identifier to link it to databases like eNanoMapper and NanoCommons and it would use unique identifiers for the Adverse Outcome Pathway) (AOP) key events. As such, it would make a direct link in the growing linked open data cloud between the AOPs and the nanomaterial databases.

Unfortunately, it was quickly discovered that actually reusing this new datasets requires rich annotation (metadata!) of the materials and the materials from the source paper were not yet in material databases. And then the cumbersome start was started, resulting in a very rich data model describing the key events, the materials, the assays used, and the original papers themselves:

But the work has not finished yet. The paper assigned ERM identifiers to all included materials, and now these need to be added to new ERM Identifier Database under development.

So, when I get the chance to see something where I worked on to make more FAIR actually being used, I love to push the boundaries of FAIR a bit extra. The study of representation of molecules and molecular systems is not quite a popular science, but I find it important. Two new papers got recently published to which I contributed from this perspective.

The first paper by Anna Niarakis et al. is about using the SARS-CoV-2/COVID-19 knowledge base we have collected of the past 4 years (doi:10.3389/fimmu.2023.1282859). For me, this started with a WikiPathways with early knowledge about the virus proteins. I think in this and earlier papers, we improved our open science and bioinformatics and are actually more ready for a next pandemic, which inevitably will come.

The second paper by Alfaro Serrano et al. is about how access to data remains key to many things, and this, obviously, includes the Sustainable Development Goals (SDGs) (doi:10.1039/D3SU00148B). When it comes down to the face/off of FAIR versus Open, I think Open has more impact, hands-down.

About the latter, I recently wrote up ten simple actions you can take to make your nanosafety research output more FAIR (doi:10.5281/zenodo.10533126).

The study has two sides to it: first, it looks into how far we are with QSAR in the field of nanosafety. We have limited data, but this paper got together 34 data sets, and in the model building many different possible factors are explored. Now, as a scholar, I would really want to know which factors are really important. We have been studying this for some time, e.g. in the past RRegrs paper (doi:10.1186/S13321-015-0094-2). Basically, I think we still don’t really understand the relation between the data characteristics and the modelling options. When is data rich enough to move from classification to regression? How much (many) exerimental data do we need, for the model to capture a certain applicability domain sufficiently?

Actually, I think the rise of deep learning approaches shows us a few things: more data actually does help. But also, with enough data, the representation becomes less important for the overall pattern. There are even hints that deep learning needs a certain level of noise. Did anyone study that phenomenon yet?

Now, the reader of this paper will not be disappointed. The design is complex and there are many small hints about what worked and what did not. But this gets us to the other side of this story.

The second side of this paper is the question whether the level of FAIR-ness helps this QSAR modelling. Earlier, Ammar studied the R1.3 aspects of nanosafety research. The R1.3 guiding principle expects that (Meta)data meet domain-relevant community standards. Ammar’s research (preprint doi:10.26434/CHEMRXIV-2022-L8VK8-V2) shows we can link this to actual reuse, where QSAR is one of those use cases. In their July paper, they show how we can integrate the use of the community standards in a reproducible way to support nanosafety research.

The following screenshot from the article (Figure 2, CC-BY) shows the relation between R1.3 maturity indicators and QSAR variables:

I think Furxhi and Ammar may actually have introduced a new community standard: this is how nanoQSAR research should be done from now on. Irini and Ammar, thanks for this great collaboration!

History

In this post, let’s look at FAIRsharing. It is “A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies” [0,1].

The ELIXIR Toxicology Community (we) maintains the toxicology corner of this database and members of our community have been adding toxicology-related databases, relevant standards. On the side of the policies we are falling a bit short: fairsharing.org/Toxicology.

Why adopt FAIRsharing

FAIRsharing is one place where metadata can be shared about your databases. It helps make your resources and research more FAIR and explains people how your work relates to other work (fairsharing.org/graph/3496):

What you can do

Get an account (with your ORCID or GitHub account) and add resources important to your research, your projects, your work generally. Particularly, (data) policies and standards you are expected to comply with are useful. Also, links between various resources. For example, if some (project) database complies with an important policy or standards, this is worth seeing show up.

Alternatively, join the ELIXIR Toxicology Community mailing list and post the missing resource there, or use our issue tracker at github.com/elixir-europe/toxicology-community/issues/.

Let’s make toxicology more FAIR.

0.https://www.nature.com/articles/s41587-019-0080-8 1.https://scholia.toolforge.org/work/Q64084285

However, first, you need to make RDF, you need to make assumptions explicit, you need to decide on meaning. Making RDF is not easy. It’s not hard, just a lot of administration and scientific thinking. What did I measure? What model do I use to describe the chemistry? You know, my research job.

Moreover, not only data should be FAIR. All research output (worth communicating) should be FAIR.

In the past, Andra Waagmeester invited me to co-author a recipe that explains the general steps of creating RDF. This was during the Open PHACTS project and with Carina Haupt. Writing recipes is something getting traction. They are a bit like vignettes from the R world.

In the past few years the FAIRplus project created a FAIR Cookbook with recipes and I wrote a few. Actually, I still have a few to finish, for which I cannot find the time. I retrospect, I spent too much time on perfecting the recipe to finish them earlier. The FAIR Cookbook is now a professional venue with editorial board. It is fully open source and welcomes your recipes. Oh, and it is now hosted as ELIXIR service, which is great to see!

Finally, the The FAIR Cookbook - the essential resource for and by FAIR doers paper is out. Go read it :)