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):



Other papers that you should be read more include:
Congratulations, Bianca!
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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).

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).

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.

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.

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.

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.
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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.

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.

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.
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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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