1. A wonderful explanation of Bregman divergences. I also learned that cool result of Banerjee & Gou.
2. I liked The Enduring Myth of the SPARQL Endpoint. This reminded of Titus Brown’ remark that scaling is the only CS problem that still matters.
Money quote: There is a reason there are no ‘SQL Endpoints’.
3. Why proxy measures fail: this applies to Journal Impact Factor, but will also apply to altmetrics. In fact, we can read it as arguing that metrics that are not widely used look better than metrics that are widely used. But it is fallacious to expect that proposed metrics will not go through the same process.
4. President of the AAAS faked her PhD.
5. This battle will eventually reach biology. (This is a much better exposition of the anti-ML position than Chomsky’s rants, by the way).
I think it will be a while before we can just predict any biological behaviour that is interesting, though.
However, how soon before somebody claims that
Every time I fire a biologist, the performance of our systems biology model goes up.
Is the curious task of machine learning to demonstrate to men how little they really know about what they imagine they can study?
1. The Tyranny of Formatting The vision for 2014 is off by at least 10 years, but compelling.
It is even worse when you realise that most of the time we are still reviewing these god-awful things in “manuscript format” where the reference are 10 pages down and the figures are split off from the captions &c. We pay the cost of formatting for submission and do not even get most of the benefits.
By the way, kudos to PeerJ on this matter:
We include reference formatting as a guide to make it easier for editors, reviewers, and PrePrint readers, but will not strictly enforce the specific formatting rules as long as the full citation is clear.
Styles will be normalized by us if your manuscript is accepted.
2. a href=”http://incubator.rockefeller.edu/?p=1256″>Want women in science, pay postdocs more
This is a probably wrong, but interesting, argument. (But, as a male postdoc, I’m all for the pay postdocs more.)
3. Euphemisms for non-significant
Probably the authors did a few manipulations to try to get the P value below 5% and failed. So, it is really non-significant.
4. Journalists have this expression that a story may be too good to check. It’s probably not true, but you just found such a good story that you don’t want to check before publishing.
Bioinformaticians should start talking about stories that are too good to debug. The Mad Scientist posts about exactly such stories
5. Are All Dictator Game Results Artifacts?
The answer is maybe.