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Recently I've been using Evernote to organize my notes. It has a nice phone app that I can use to take quick notes while away from my computer, a computer program, and a browser plugin that lets me clip articles. When it comes to notes I try to think that every time I record an idea I would have forgotten, it is roughly equivalent to thinking of one new idea.
I tend to write out outlines after I finish books or some interesting articles partially to see the arguments more clearly and to refer back to in the future.
It's always interesting/fun to go back browse old ideas I've forgotten about.
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The most important link is the Supplementary Material. I only found it through the reddit thread. (Not much else to go there for. Maybe the pirated paper itself, but that is basically just an extended abstract of the SOM.) Here is the link to the SOM:
http://www.sciencemag.org/content/suppl/2011/12/14/334.6062.1518.DC1/Reshef.SOM.pdf
Figures S5 and S6 (page 41) make me conjecture that compared to LOESS, this new method is an improvement only when the relationship is not a function (but a many-valued function). Not that I am really familiar with LOESS.
I'm far from an expert on LOESS (in fact, I hadn't heard the term before now), but it looks like it doesn't perform a comparable function to MIC. LOESS seems to be an algorithm for producing a non-linear regression while MIC is an algorithm to measure the strength of a relationship between two variables.
In the paper (figure 2A), they compare it to Pearson correlation coefficient, Spearman rank correlation, mutual information, CorGC, and maximal correlation on data in a variety of shapes. Basically, it is effective on a wider range of shapes than any of them.