Wow, it’s been almost a year since the last post. Here’s some non-science for getting me in shape.
It’s been a couple of weeks since the FIFA World Cup final. Amazing falls of favorites and rises of underdogs are what people always look for in such events. Croatia, who had barely qualified for the tournament showed a great performance until the very last game, losing only to the young France team. Discussing the team’s chances one can’t help but compare their countries’ population sizes. Indeed, isn’t it amazing that Croatia, a nation of 4.1 million people, could leave behind England (55 million), Russia (144 million), Argentina (44 million), and Nigeria (186 million)? Or is it not? Below is some semi-serious attempt to analyze population – football performance relationship. Continue reading “Big nations – better players?”
Since the grad school I have been using R for data analysis and (mainly) for preparation of nice plots. As with LaTeX, it was a pretty steep learning curve. But after a year or so I managed to become comfortable with writing a new script for each new experiment. After some time, however, I started feeling that it wasn’t enough. Many experiments were almost identical, so breeding 99%-similar scripts didn’t seem to be an efficient way of handling data. That’s when I first started to think about making ‘reusable’ apps for specific purposes. Continue reading “My first Shiny app: fitting sigmoid curves”
An interesting case study of a correct structure assignment for aquatolide appeared in JOC. It’s interesting from several points of view. First, it nicely shows how one can effectively use reach information from free induction decay (FID), which is lost (or masked) in Fourier-transformed spectra. Second, it emphasizes importance of data sharing and demonstrates crucial role of ‘research parasites‘ in scientific ecosystem. Third, the paper has seven-point manifest in the conclusions section. Continue reading “True power of 1D NMR”
It’s really entertaining to watch the (over)reaction of Twitter on the controversial editorial in NEJM about data sharing and open science. As usual, it’s pretty hysteric but has a potential to cause some real-world consequences. The problem is that the authors were reckless enough to use term “research parasites” for those scientists who use the data from other labs without conducting their own experiments. Continue reading “Research parasites”
Today chemical biology generates new high-throughput methods of studying biomolecules almost as quickly as organic chemists report total syntheses. Whole genome, transcriptome, proteome, lipidome, glycome etc. analyses are flourishing and delivering vast amounts of data. Bioinformaticist are trying to cope with the data flow by archiving them in various databases. This has led to a situation when the number and diversity of databases became incomprehensible for a human being. Continue reading “The database of databases”