One aspect that limits the uptake of diamond open access models is that the scholarly community needs to change their trust model. We have seen a transition from societal publishers to commercial publishers, from scholarly-led to publisher-led. And commercial publishers certainly breached my trust (see also doi:10.5281/zenodo.4926030).

Many scholars prefer the certainty of a scholarly journal where they can trust that the editorial board and their reviewers take their job seriously. This matters. The notion that peer reviewers may reject your work is an extrinsic motivation for at least some researchers to do a better job (personal communication). And that feature works well: there are indications that research published as preprint sees limited change after the formal peer review process (doi:10.1038/d41586-026-02167-3 and doi:10.64898/2026.06.30.735556):

“For most scientists, reputation is important, and they would not upload something they would not be comfortable with publishing in a journal later.”

In an ideal situation, reputation would not be part of the whole equation: researchers never submit anything they think is not scientifically sound. Journals would never desk-reject an article that is scientifically sound. The question is mostly how to reach that. Everyone is busy, and it has not been so long ago that the journal impact factor was by many as indicator of scientific quality. The reality is far more complex than that.

Finding Diamond Open Access journals

So, let’s assume you are interested in the idea, and want to know what diamond open access journals exist in your research field, before you can even consider the quality of that journal, you need to know which journal is diamond open access. Therefore, I asked yesterday on Mastodon about databases that list which journal are diamond.

It actually turns out that the Directory of Open Access Journals (DOAJ) does not indicate if a journal is diamond. You can filter on zero APC, but that is not the same. In fact, zero APC is routinely used by big publishers to launch new journals. One they have critical mass, they will start charging non-zero APCs.

I already received some replies about possible options:

1. Diamond Discovery Hub: lists just over 4,000 diamond journals, but seems closed data (thx Najko! see also doi:10.15291/libellarium.4569)
2. wheretopublish.github.io: open data, accepts additions, but lists only 9 diamond journals (thx Anne!)
3. B!SON: a recommender that depends on DOAJ, so also without clear diamond indication (thx Jon!)

All pieces of the puzzle.

When I grew up as a scholar, I learned to publish in journals where you respect the research, where you see important research in your field get published. Nowadays, this is changing. In fact, young researchers (tho many older scholars alike) fear to publish in predatory journal by accident (really! [citation_pending]). The big publishers and the new publishers have been adding so many new journals, and academia has really pushed the “publish as many articles as you can” model, that it kind of makes sense why things are different now.

And, a central database with trustworthy information about the quality of diamond open access journals is just needed.

A final note, while I have not been able to create a list of diamond open access journals with OpenAlex, already in February 2025 I found a way to list articles tagged with a diamond status. This query lists over three thousand of such annoated articles for Maastricht University:

(Interestingly, the journal where the three most cited articles were published turned diamond already in 2004 and was discontinued about half a year go “Due to recent changes in operational resources”, according to Wikipedia.)

Building trust

So, while the above solution do allow you to increase your success rate or finding a diamond open access journal in your field, the next question is: what can you learn about this journal. For example, what does Wikipedia say about the journal (if anything)? Do you know people that have published in that journal? The OpenAlex screenshot shows

That make me wonder what Wikidata has to offer. After all, as one of the largest linked open data sources, they must have some information.

I am not disappointed. The type diamond open-access journal is actively used, and there is plenty of other of useful fields for these journals. I came up with this SPARQL query (with some QLever sugar for labels):

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?diamondJournal ?diamondjournalLabel
      # (SAMPLE(?url_) AS ?website)
      (SAMPLE(?issnl_) AS ?issnl)
      (GROUP_CONCAT(DISTINCT STR(?issn_)) AS ?issn)
      (SAMPLE(?lang_) AS ?language)
      (SAMPLE(?doaj_) AS ?doaj)
      (GROUP_CONCAT(DISTINCT STR(?subject);separator=", ") AS ?subjects)
WHERE {
  ?diamondJournal wdt:P31 wd:Q108440863 .
  OPTIONAL { ?diamondJournal wdt:P236 ?issn_ }
  OPTIONAL { ?diamondJournal wdt:P7363 ?issnl_ }
  OPTIONAL { ?diamondJournal wdt:P856 ?url_ }
  OPTIONAL { ?diamondJournal wdt:P407 / @en@rdfs:label ?lang_ }
  OPTIONAL { ?diamondJournal wdt:P5115 ?doaj_ }
  OPTIONAL { ?diamondJournal wdt:P921 / @en@rdfs:label ?subject }
  ?diamondJournal @en@rdfs:label ?diamondjournalLabel .
} GROUP BY ?diamondJournal ?diamondjournalLabel

This lists more than 1,500 diamond open access journals:

With Wikidata we can then list UM authors that published in diamond journals. That list is remarkably short, however, if you compare it with the earlier results from OpenAlex. That is because Wikidata only contains a subset of scientific literature and not all articles are correctly linked to the items about their authors. But I also spotted journals in the OpenAlex list that are not really diamond.

What is next?

Clearly, we need better metadata, but things are not looking that bad either! We have something to work with. What is your favorite diamond open access journal?

References

Then the internet came. Big publishers started sharing offering digital-only subscriptions. This would lower the cost and therefor the prize. That was the idea. But the big publishers remained expensive publishers.

Then open access came. The idea is that journal article would have a open license, often CC-BY or similar. This meant that researchers could share articles with colleagues and students without additional cost. The idea was that this would lower the cost of the journals. After all, libraries could reshare the articles, and contribute in funding the distribution of the knowledge. But the big publishers insisted to be the only source of the PDFs, and they remained expensive publishers.

Then came the article-processing charge (APC) model. The authors would contribute for the publishing and distribution process. For some years, new publishers showed, organized by and for scholars, keeping the APC close to the cost of production and distribution. The APC was estimated at somewhere between 50 and 600 euro per article, likely more now, after the inflation of the last five years. But scholars did not trust these publishers. Second, publishers saw a market and a business model in APC-based publishing. Libraries were pressured to keep access to big publishers that realized that if scholars wanted to keep publishing there, they could charge higher APC based on popularity. New expensive publishers entered the market, some with good intentions, some with bad intentions. And among all the confusion, the big publishers remained expensive publishers.

This is where we are now. Because sharing knowledge was a great idea, we want to keep the license, but we need to drop the financial incentives: we need to drop the APC.

Enter the world of diamond open access, where science is published under an open license, and neither the reviewers, not the readers, nor the authors pay, all key stakeholder in the exchange of scientific knowledge. And no tax by publishers.

Can we do this? More and more people think so, and diamond open access is gaining traction.

References

The full proposal can be read in the Open Science NL Collection on Zenodo (doi:10.5281/zenodo.19704231).

References

• 10.5281/ZENODO.19704231

Co-creation

The project is still running another few months, but the launch last week gives us the opportunity to include feedback from the stakeholders from the Dutch society, many of which have been involved in the project via designathons and hackathons (see doi:10.14573/altex.2407211).

The VHP4Safety platform is a co-creation created by most of the people working on the VHP4Safety grant. Some people focused on innovation and education (RL3), others on the regulatory questions (RL2), and some on the development of the platform (RL1). The research line 1 (RL1) included a work package on the technological development, work package 1.1, and that was led by Maastricht University (Ozan and me) and the Applied University of Utrecht (Dr. Marc Teunis). This project would not be together without the leadership by Prof. dr. ir. Juliette Legler, Dr. Cyrille Krul, and Prof. dr. Anne Kienhuis (see this video).

Not just technology

I have to give a huge shout out to Ozan whom had the daunting task to set up something like OpenRiskNet (doi:10.1016/j.toxlet.2018.06.617), a project with at least twice as much funding for operating and documenting just the technical platform, but also help other partners getting their work on the platform. Also shout outs to Luc who in our group first explored how to translate the OpenRiskNet platform to VHP4Safety with kubernetes from which we concluded that that was not an option for us. And to Sean in our group who introduced us to Docker Swarm.

But that is just one aspect of the technological layers. The design outlined in the original proposal is based on earlier projects, including OpenRiskNet, eNanoMapper, OpenRiskNet, Open PHACTS, NanoCommons, SbD4Nano and many others. It is based on open standards developed and/or adopted by ELIXIR Europe projects and many other organisations.

And then we have not even covered the content on the platform.

A virtual human

Building full virtual human is an ambition. Many digital twins capture just one part of human biology. For safety assessment we need many models, data from experiments, and knowledge bases. And we need a clear narrative that describes how those isolated solutions are integrated so that regulatory questions can be answered. That co-created combination is the launched virtual human platform.

Underlying the VHP is a good bit of open science, though it also integrated proprietary solutions, currently needed to be able to replace animal testing. And our modular co-creation resulted in many separate git repositories. This allows distributed development models and all contributors to take ownership of the development of their contributions. Marc and Frank introduced a agile computing approach that we adopted to guide the development of the full platform.

There is so much to write up about the platform (and we will), but for now I want to highlight a few essential git repositories underlying the 1.0 version of the platform we launched last week (along with the number of contributors in the past two years):

• virtual-human-platform (10 contributors): software that provide the platform UX
• cloud (11 contributors): collects the meta data about (computational) services
• ui-casestudy-config (7 contributors): collects the details of the narratives of the case studies

This includes Aniek, Fabian, Isaac, Ivo, Javier, Jente, Linde, Marvin, Myrthe, Saad, Shakira, and Youp, in addition to the earlier named Ozan and Marc.

This excludes the many contributions via the designathons and hackathons that are behind many of the commits to these repositories. And this also excludes the many other source code repositories for tools and services developed and made available on this VHP, with even more researchers.

Very much aware of all the work that is still ahead of us, I am happy with the important milestone of this release. Thank you to everyone who contributed to this co-creation, all the involved institutes, NWO for the funding, and the Dutch public for the important NWA question.

The project delivered!

References

• 10.14573/ALTEX.2407211
• 10.1016/j.toxlet.2018.06.617

In a recent paper, Yojana Gadiya, Javier Millán Acosta, and Tooba Abbassi-Daloii led a project called BioDataFuse (worked on at the biohackathons of ELIXIR in 2023 and 2024 and of SWAT4HCLS in 2024 and 2025) and the matching Python package, pyBiodatafuse (doi:10.1093/bioinformatics/btag064).

With a group of researchers from The Netherlands, Switzerland, Czech Republic, and the USA, multiple databases are wrapped in a uniform data model. The package allows the generation of a graph across the imported databases which can then be further analyzed and visualized. This is an example (RDF) graph that was generated:

Seeing this kind of interoperability brings back good memories.

Congrats to all authors!

References

• 10.1093/BIOINFORMATICS/BTAG064
• 10.37044/OSF.IO/MHSQP
• 10.37044/OSF.IO/PTMG5_V1

The FIP is a collection of FAIR implementation choices made by a community of practice for each of the FAIR Principles.

In the early GO FAIR days, people referred to the challenges and choices. FIPs are a formal approach to report the choices. The days of the implementation networks, like the Chemistry Implementation Network (doi:10.1162/dint_a_00035), or the AdvancedNano network (doi:10.1016/j.impact.2024.100513). For the first, we absolutely agreed on the InChI, for example, but we never wrote that down as a FIP. But even without FIPs, the idea was that communities could learn from each other, could converge. This is where the term FAIR Convergence Matrix comes from. (I had forgotten I was actually part of the matching Working Group. If only I had dedicated funding for it at the time.)

Now, I had on my wishlist to write up a list over FIPs around chemistry. This is relevant for the FAIR4ChemNL project, but obviously beyond that. And while we did a lot of FAIRification work in, particularly, the nanosafety cluster projects (eNanoMapper, NanoCommons, NanoSolveIT, RiskGONE, and SbD4Nano), a lot of this never was formalized as FIPs. Second, various relevant standards have been proposed in chemistry, for chemical compounds and transformation products, among plenty of other things. But during the first ELIXIR Toxicology Community workshop in Utrecht, we had FIPs on the agenda too (see this report).

FIPs in Chemistry

But while I had already several browser tabs open for months, I never could find the courage to start making the list. Silly. Therefore, this list should be considered a start. I don’t think it is exhaustive. I know there is an index somewhere, but I cannot find back that browser tab. Additions are most welcome: I will update this post (thanks to git and Rogue Scholar). In brackets I list (a selection of) the FAIR enabling resources mentioned in that FIP.

• Adverse Outcome Pathways FIP (DOI, AOP Wiki ID)
• Toxicogenomics (ISA-Tab, BioStudies, ArrayExpress)
• WorldFAIR WP04 NANOMATERIALS FIP01 (DOI, ORCID, UUID, ROR, InChI, DataCite, QMRF, RDF/JSON-LD, OWL; doi10.5281/zenodo.7378109)

Is that all? No, I do not think so, but this is all I can easily find (ironically). Well, I guess you now see why I have had a so much trouble starting to write down this post… Someone will surely give me some pointers…

Ackknowledgments

I also like to thank all the people involved in these FIPs. I strongly encourage you to look up all the people who wrote them down. I also like to thank the INTOXICOM workshop FIP experts Iseult Lynch and Gerhard Burger for sharing their knowledge.

Finally, I recommend checking out the FIP Wizard.

References

• 10.1162/dint_a_00035
• 10.1016/J.IMPACT.2024.100513
• 10.37044/OSF.IO/UN2RW
• 10.5281/zenodo.7378109

Thus, the idea came up, can we create a set of 1 million unique IUPAC names found in literature?

We started out with using Europe PMC to get JATS XML files for the full texts of open access articles. Parsing the XML is easy and the text paragraphs are passed through OSCAR and OPSIN. That has not changed.

What did change last weekend is something I had long on my todo list (but life interfered). The first approach was to ask for named entities using the Europe PMC APIs. But I quickly realized that with OSCAR and OPSIN we could get more names out of the articles. The next step was to move from Google Colab to a command line script. That gave another boost, as explained in this second post in the series. We reached 200 thousand names in june 2025 but then things slowed down again in the growth. Two months later we only had 75 thousand more. However, plenty of discussion was happening and there turned out to be other, larger collections of IUPAC names under an open license. Millions of names, actually.

But another problem emerged. We were still using the Europe PMC API and were basically asking for open access articles between two dates. Practically, the API could answer requests between 1 and max 3 days. Beyond that, times outs and 404s became an issue. Moreover, because these dates are publications dates and not the dates on which the JATS were deposited, I had to got back to previous months and redo the queries. That gave another 5 thousand names since last August. Something had to change.

The new Approach

Europe PMC, however, also provides the JATS XML files as download on their FTP site. Already that august 2025 I had a prototype and knew it would change the game. These gzipped XML files are about 150 to 250 MB. Unzipped, about 1 GB each. Better, these files are based on Europe PMC identifiers, hopefully resolving the issue with using dates in the queries.

Now, parsing a 1 GB XML files is a total non-issue. I have done it plenty of times before. Just use a Simple API for XML (SAX) parser. This is a streaming parser giving you full control of how to parse things. It is ideal for this siutation: you just keep the current paragraph of text in memory and release that when done with that paragraph. That is, you do not have to read the full file in memory, just the bits you are interested in. I used this for my Chemical Markup Language patches for Jmol and JChemPaint back in the nineties.

Last weekend I finally made the jump. Use SAX to extract the <p> elements one by one, running OSCAR on them, filter with OPSIN, output that name, and clear the memory. Effectively, each gzipped file processes with a Groovy script in about 1 to 2 hours.

The output is a mesmerizing stream of scientific literature (which I will use until someone points me to a Java CLI library that creates a Matrix-style falling letters equivalent), tho less so as a static image:

In this plot, an x means a new article to be processed. Each . and o that follows is a single <p> element and the difference is that an o means at least one IUPAC name was detected in the paragraph.

Each gzipped file gives 400 to 500 new IUPAC names. Indeed, going from 288 thousand to 300 thousand was a matter of a day and a half. And earlier this afternoon we passed the 400 thousand IUPAC names. With about 230 gzipped files. Now, I am going back in time, and the sizes of these files are shrinking: Another 500 files and the size has dropped to around 125 MB, so a rough estimate suggests that we will end up with 650 to 700 thousand names this way. This will be completed in a few weeks (and mostly because I need to focus first on other things again, because I can use our computing cluster do this).

Regarding the original goal, fortunately, we are still publishing at a higher rate every year, and more and more articles are available as open access. So, I still have good hopes we will reach the 1 million IUPAC names. Also, keep in mind, we know how to boost this by simple name variations to several millions, even with the 400 thousand we have today.

Oh, and our next milestone will be in the pocket before I visit Christoph Steinbeck’s cheminformatics team in Jena!

References

This is the second time a local Open Science Festival is organized in the area, with an Open Science Festival Maastricht in 2023. Of course, a year later Maastricht also hosted the 2024 national Open Science Festival.

This year’s event is co-organized by the (new) Open Science Community Parkstad and the Open Science Community Maastricht, with interdisciplinarity as main theme. The event will take day over two days, June 11 in Heerlen, and June 12 in Maastricht. You can register for either or for both days. Each days have plenary sessions and several parallel workshops, where you will dive into one of the many aspects of open science.

Looking forward to welcoming you in Limburg!

References

Still, Zotero 8 brought in prefix and suffix support, and I was wondering if this could be used for CiTO citation intent annotations. And it can. This was the result, and make of it what you want:

References

The meeting consisted of four keynotes, each one was quite interesting. Cornet gave a nice historic perspective of the venue and of the semantic web field, which is a great way to welcome the participants to your institute. The talk also touches on the main theme of the meeting: clinical data. It is a long standing (and important) research field, but progress is slow. Cornet comments along the lines that we have been talking about reasoning over patient data for more than twenty years, but we still have not solve it.

The problem is really not only privacy, but simple also lack of a common language. As Sabine Österle explains about sharing health/patient data in Switzerland, across 26 kantons and legislations and 4 national languages. Another issue is more technical, running SPARQL across hospitals involves more than just aligning ontologies, but also requires (too much) fiddling with SPARQL queries.

There was plenty of other content too, however. For example, I was pleasantly surprised by the RDF4RiskAssessment work, the RO-Crates for BioImaging, and FDPcrawleR. All these projects have direct links to research ongoing in our TGX team.

Hanna Bast gave the second keynote of the first day, about QLever (doi:10.1145/3132847.3132921). She talked about some of the recent improvements, something we really needed for Scholia. She showed a technical approach to make federated queries faster, tho it currently only works between endpoints that both run QLever. One thing I am looking forward to, is playing with the notion of materialized views, but the biohackathon was too short to get around to that during the Thursday.

The second day kicked off with a keynote by Janna Hastings, whose work I greatly admire. I was not disappointed today, and she showed the Behaviour Change Intervention Ontology and Chebifier (doi:10.1039/D3DD00238A).

The last talk I want to mention in the blog is by two researcher working with Michel Dumontier. They presented a study about deduplication in/of knowledge graphs. This is something I want to read in more detail.

References

• 10.1039/D3DD00238A
• 10.1145/3132847.3132921