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).

Using Neo4J and Cytoscape she visualizes the data onto a network created with RDF, SPARQL from the above resources:

The whole approach uses open science, making the work very reproducible. This is essential, as our knowledge about metabolic processes continues to grow, if not only for the human lipids, but also from molecular imaging technologies. Moreover, a lot of biological detail is yet to be encoded on pathway databases, such as cellular location of proteins and metabolites, which proteins are expressed in which tissue, or the kinetics of metabolic reactions. All knowledge that can be pulled it via knowledge graphs becomes immediately available by using this FAIR approach.

One last note, the reader may notice a focus on the shortest path. Of course, the biological relevant path may not be the “shortest” path. But from a network analysis perspective that question is purely academic. Neo4J, like other tools, support finding all paths. But validation which paths (the shorter or any of the longer) is biologically most relevant first depends on actually more biological knowledge to become FAIR. After this, it is just push button.

In this study, Denise explored how we can take advantage from molecular pathway databases to link biomarker information: “Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis” (doi:10.1186/s13023-023-02683-9).

Figure 4 shows how such a visualization of those biomarker interactions can look like:

And I am hugely proud of the open science approach, from GitHub repo, open source R code, SPARQL queries. Thank you, Denise! And thanks to Dr Laura Steinbusch for this nice collaboration! Further acks in the article.

From a WikiPathways there is additional complexity, with modified proteins involved in lipid metabolism, the acyl-carrier proteins. They look like this, and the R group is a protein:

We have quite a few of them in WikiPathway and they also show up in ChEBI (and likely Reactome), LIPID MAPS, and KEGG.

During this years Dagstuhl we used up one session to continue working on it (report pending). Part of the results is that Wikidata (see doi:10.7554/eLife.52614 and doi:10.7554/eLife.70780) now has a property for CXSMILES. CDK 2.0 (doi:10.1186/s13321-017-0220-4) already supported CXSMILES and the above image is actually created with CDK Depict (thx to John!).

So, that means I can now start adding all those ACPs to Wikidata :) Here’s hexadecanoyl-[acp] (or this Scholia page):

Note the manual adding of peaks at 10 and 100 m/z to get the real peaks somewhere in the middle instead of on the left and right border of the graph.

Meanwhile, the search page is now autogenerated too, and the types of searches allowed (min, max in the picture) again depends on the XML Scheme data type defined in the MetWare SKOS:

The game board should look familiar:

I finally found a worthy follow up for Civilization :)

However, personal circumstances strengthened an older wish of me and my family to seek the adventure of living abroad, and a vacancy was available in the group of Prof. Wikberg. So, we are moving to Sweden. There, I will extend my research on effectively combining chemoinformatics (sometimes misspelled as cheminformatics ;) and chemometrics, as I did in my PhD, which fits well with the development of proteochemotrics methodology and Bioclipse as platform to transform scientific hypotheses into data queries.

The original 150ppm results:

The 750ppm results:

And for 1000ppm (1250ppm did not further improve):