In the past year, we have been working in the NanoSafety Cluster on FAIR research output (for our group, via NanoCommons, RiskGONE, NanoSolveIT, collaborating with other projects, such as ACENano and particularly Gov4Nano), analyzing resources, deciding where the next steps are. Of course, in the context of GO FAIR (e.g. via the Chemistry Implementation Network), ELIXIR, RDA, EOSC, etc.

But there seems to be something going wrong. For example, adoption by some Open Science communities making FAIR the highest priority (but formally FAIR != Open; arguably it should be: O'FAIR), but also in the strong positioning of the data steward that will make research data FAIR. I never felt too comfortable, but we're about to submit an article discussing this.
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When I did my PhD and wrote my articles, Ron Wehrens introduced me to R as open source alternative to MatLab which was the standard in the research group otherwise. At some point, I got tired of making new plots, and I started saving the R code to make the plot. I still have this under version control (not public; will be release 70 years after my death; I mean, that's still the industry standard at this moment </sarcasm>).

As a multidisciplinary researcher I had to wait long to become an expert. Effectively, you have to be expert in more than one field. An, as I am experiencing right now, staying expert is a whole other dimension. Bit with a solid chemistry, computing science, cheminformatics, and chemometrics education, I found myself versatile that I at some point landed that grant proposal (the first few failed). Though, I have had my share of travel grants and Google Summer of Code projects (microfunding).
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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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