Part of the winning submission in the category 'best tool'.
A bit later than intended, but I am pleased to announce the winner of the Winter solstice challenge: Bianca Kramer! Of course, she was the only contender, but her solution is awesome! In fact, I am surprised no one took her took, ran it on their own data and just submit that (which was perfectly well within the scope of the challenge).

Best Tool: Bianca Kramer
The best tool (see the code snippet on the right) uses R and a few R packages (rorcid, rjson, httpcache) and services like ORCID and CrossRef (and the I4OC project), and the (also awesome) oadoi.org project. The code is available on GitHub.

Highest Open Knowledge Score: Bianca Kramer
I did not check the self-reported score of 54%, but since no one challenged here, Bianca wins this category too.

So, what next? First, start calculating your own Open Knowledge Scores. Just to be prepared for the next challenge in 11 months. Of course, there is still a lot to explore. For example, how far should we recurse with calculating this score? The following tweet by Daniel Gonzales visualizes the importance so clearly (go RT it!):


We have all been there, and I really think we should not teach our students it is normal that you have to trust your current read and no be able to look up details. I do not know how much time Gonzales spent on traversing this trail, but it must not take more than a minute, IMHO. Clearly, any paper in this trail that is not Open, will require a look up, and if your library does not have access, an ILL will make the traverse much, much longer. Unacceptable. And many seem to agree, because Sci-Hub seems to be getting more popular every day. About the latter, almost two years ago I wrote Sci-Hub: a sign on the wall, but not a new sign.

Of course, in the end, it is the scholars that should just make their knowledge open, so that every citizen can benefit from it (keep in mind, a European goal is to educate half the population with higher education, so half of the population is basically able to read primary literature!).

That completes the circle back to the winner. After all, Bianca Kramer has done really important work on how scientists can exactly do that: make their research open. I was shocked to see this morning that Bianca did not have a Scholia page yet, but that is fixed now (though far from complete):


Finally, follow her on Twitter and read her latest work posted as preprint at PeerJ: Open access levels: a quantitative exploration using Web of Science and oaDOI data! BTW, if you want to use Scholia to show more work together with Jeroen Bosman, try this.

Other papers that you should be read more include:
Congratulations, Bianca!
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  1. Hi all, as posted about a year ago, I moved this blog to a different domain and different platform. Noting that I still have many followers on this domain (and not on my new domain, including over 300 on Feedly.com along). So, please update your RSS/Atom reader with the following info:

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  2. This is my last post on blogger.com. At least, that is the plan. It has been a great 18 years. I like to thank the owners of blogger.com and Google later for providing this service. I am continuing the chem-bla-ics on a new domain: https://chem-bla-ics.linkedchemistry.info/

    I, like so many others, struggle with choosing open infrastructure versus the freebie model. Of course, we know these things come and go. Google Reader, FriendFeed, Twitter/X (see doi:10.1038/d41586-023-02554-0). My new blog is still using the freebie model: I am hosting it on GitHub. But following the advice from a fellow cheminformatician, I now front this with a owned domain name.

    See you at linkedchemistry.info!

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  3. Some days ago, I started added boiling points to Wikidata, referenced from Basic Laboratory and Industrial Chemicals (wikidata:Q22236188), David R. Lide's 'a CRC quick reference handbook' from 1993 (well, the edition I have). But Wikidata wants pressure (wikidata:P2077) info at which the boiling point (wikidata:P2102) was measured. Rightfully so. But I had not added those yet, because it slows me and can be automated with QuickStatements.

    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.
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  4. Just a quick note: I just love the level of detail Wikidata allows us to use. One of the marvels is the practices of 'named as', which can be used in statements for subject and objects. The notion and importance here is that things are referred to in different ways, and these properties allows us to link the interpretation with the source. For example, Max Born's seminal work Zur Quantenmechanik (doi:10.1007/BF01328531) uses a very short notation to cite other literature, as footnotes, and DOIs did not exist yet.

    So, in Wikidata, you can capture this like this:



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  5. I am still an avid user of RSS/Atom feeds. I use Feedly daily, partly because of their easy to use app. My blog is part of Planet RDF, a blog planet. Blog planets aggregate blogs from many people around a certain topic. It's like a forum, but open, free, community driven. It's exactly what the web should be.

    It turned out that planets do still exist, so I started a small corner on Wikidata: Q121134938, and a number of existing blog planets:


    The software used to run these planets is ancient, though. We need a new generation of software, replacing things like Planet. And I want something people can easily host on GitHub or GitLab Pages or the likes.

    I created a minimal shape expression but the Wikidata items for the planets still lack a lot of information that can be added. First, we can think of them as venues, perhaps, where people "publish" their work. Second, we can annotate the blog planets with 'main subject' for the topics the cover. Or we can list the people that are "author" on the planet; most planets are very transparent about which blogs they aggregate.

    Love to see where this is going. Who knows? Maybe we will see Postgenomic (see doi:10.1186/1471-2105-8-487) and Chemical blogspace resurface :)


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  6. This blog is almost 18 years old now. I have long wanted to migrate it to a version control system and at the same time have more control over things. Markdown would be awesome. In the past year, I learned a lot about the power of Jekyll and needed to get more experienced with it to use it for more databases, like we now do for WikiPathways.

    So, time to migrate this blog :) This is probably a multiyear project, so feel free to continue reading it hear. Why? Because I start with the old posts :) Along the way, I am fixing things, improving it. I still have plenty on my todo list, but already happy with having learned Font Awesome, which makes it easy to annotate with how I fixed broken links (or not). I now use three icons: a box for when I use the Internet Archive (they can use your donation); a 'recycle' icon when I found a new URL for the same page; and a broken URL link for other situations.

    This is what it looks like:



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  7. The role of a university is manifold. Being a place where people can find knowledge and the track record how that knowledge was reached is often seen as part of that. Over the past decades universities outsources this role, for example to publishers. This is seeing a lot of discussion and I am happy to see that the Dutch Universities are taking back control fast now. For example, Radboud University (>1k followers) already joined the Fediverse (Mastodon etc), making them independent from non-EU law and commercial interests. Scientific journals, Nobel Prize winners, etc already joined too, btw.

    This effort is calling for more universities to go into the direction of open infrastructures. I am looking forward to seeing all Dutch Universities post news on Mastodon, post videos on PeerTube, etc. 

    Would it not be awesome if the Fediverse would become the new multidimensional knowledge dissemination and peer review system we have all been waiting for?

    Update: universities with a Mastodon listed in Wikidata on the world map: https://w.wiki/6zR3

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  8. I am pleased to learn that the Dutch Universities start looking at rankings of a more scientific way. It is long overdue that we take scientific peer review of the indicators used in those rankings seriously, instead of hiding beyond fud around the decline of quality of research.

    So, what defines the quality of a journal? Or better, of any scholarly dissemination channel? After all, some databases do better peer review than some journals. Sadly, I am not aware of literature that compares the quality of peer review in databases with that in scientific journals. Also long overdue, in my opinion.

    I hope the Open Science community will help shape these scholarly dissemination channels, journals included. Some ideas, the outlet:

    • encourages post-publication peer review
    • communicates the post-publication peer review
    • allows updating easily small fixes and clarifications (no hiding behind the version-of-record)
    • ensures supp info / additional files undergo the same level of peer review
    • use modern solutions for communication (like semantic web technologies)
    • have clear licenses for all aspects of the research output
    • actively fight against visual representation only, but provides all data
    • guarantees that supp info / additional files are archived, as the output itself
    • adopts, promotes, requires community standards (including global, unique identifiers)
    Okay, these items are pretty broad. Many of them are part of FAIR, but that should not surprise you, because FAIR are just applying traditional scholarly approaches, like properly keeping notebooks. It's just a bit more "digital" then we have been taught.

    Do we know how to do this? Yes, pretty much. This is not a technical exercise, but one of social change and particularly willingness. Basically, if you want to keep the current way of doing things, the declare you want unreproducible, low quality research reporting. That's your academic freedom, of course. If I were a funder or a university, I would also expect a bit more in return for my money.

    Let me stress, glossy articles are fine! You do not have to stop that. Media appearances, key notes, these are all also fine. They are, however, complementary. We should not continue the habit of fancy narratives as replacement for quality research dissemination. Do both, if you must.

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  9. A bit over a year ago I got introduced to Qeios when I was asked to review an article by Michie, West, and Hasting: "Creating ontological definitions for use in science" (doi:10.32388/YGIF9B.2). I wrote up my thoughts after reading the paper, and the review was posted openly online and got a DOI. Not the first platform to do this (think F1000), but it is always nice to see some publishers taking publishing seriously. Since then, I reviewed two more papers.

    One of these latter two was not a more traditional paper, but a different kind of research output: a definition, about "Drive-by Curation" (doi:10.32388/KBX9VO). Now about this output type, collaboratively working on definitions is something core to ontology development (e.g. see doi:10.1186/s13326-015-0005-5), but there is a clear need to discuss terminology. The GRACIOUS project in the EU NanoSafety Cluster also recognized this and set up a tool for this, their Terminology Harmonizer (doi:10.1016/j.impact.2021.100366).

    This GRACIOUS tool, much more than what Qeios does, helps users. Unfortunately, and why how these topics nicely come together, writing definitions, thinking about when some zeta potential is different from another zeta potential, and the (drive-by) community curation, it needs transparency. I understand it, but landing on a login page is for me a recipe for a silent death as it disallows people to learn, without making an (time) investment first. That is what Qeios does differently: it is more FAIR.

    So, that brings me to my last point in this post. Jente Houweling and I wrote up a definition for "Research Output Management" (doi:10.32388/ZNWI7T), based on our discussions about her research insights. See the screenshot below.

    It has been reviewed internally, and by one independent peer (doi:10.32388/C3SJTN). But we would love to hear your review too. Just follow the instructions online. We are looking forward to reading your thoughts and to refining our definition.



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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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