In December I reported about Groovy code to create nanopublications. This has been running for some time now, extracting nanopubs that assert that some metabolite is found in some species. I send the resulting nanopubs to Tobias Kuhn, to populate his Growing Resource of Provenance-Centric Scientific Linked Data (doi:10.1109/eScience.2018.00024, PDF).

Each data set comes with an index pointing to the individual nanopubs, and that looks like this:

I wonder what options I have to to archive the full set up nanopublications on Figshare or Zenodo, and see that DOI show up here...

Ryan Miller and Friederike Ehrhart worked together in this paper on furthering our understanding of the Rett syndrome  (doi:10.3389/fgene.2019.00059). They looked at the following: our biological pathways are social constructs that help us think and talk about the biology in our body (and other animals, plants, etc, of course). What if we ignore the boundaries of those constructs, can we learn the pathways? Turns out, sort of.

This January two new Horizon 2020 projects started for me: RiskGone and NanoSolveIT. It kept me busy in the past few weeks, with the kick-off meeting of the latter task week in Athens. Both continue on previous work of the EU NanoSafety Cluster, and I'm excited to continue with research done during the eNanoMapper project.
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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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