After writing about the importance of good RSS feeds for a particular subset of the academic community it occurred to me that part of the reason that we have such hit and miss implementations of feeds by journals is that most academics don’t even know what a feed is let alone actually use a feed reader. If this is you then we still want you to be able to get regular updates from JE, so last night I setup a new feed using Feedburner.

I’d recommend checking out this post by River Continua about an impressively sophisticated phishing scam targeted at academics. They’re going to catch a bunch of folks with this one. UPDATE: Apparently this is something that the EPA does that the EPA employee who wrote the original post was unaware of. They definitely need to rethink the composition of the email though as I would have been (and obviously was) equally suspicious.

If you don’t have an easily accessible RSS feed available (and by easily accessible I mean in the browser’s address bar on your journal’s main page) for your journal’s Table of Contents (TOCs), there is a certain class of readers who will not keep track of you TOCs.

During the course of this long volume I have undoubtedly plagiarized from many sources–to use the ugly term that did not bother Shakespeare’s age. I doubt whether any criticism or cultural history has ever been written without such plagiary, which inevitably results from assimilating the contributions of your countless fellow-workers, past and present.

Beyond simple histograms there are two basic methods for visualizing frequency distributions. Kernel density estimation is basically a generalization of the idea behind histograms. The basic idea is to put an miniature distribution (e.g., a normal distribution) at the position of each individual data point and then add up those distributions to get an estimate of the frequency distribution.

I read a handful of experimental ecology papers the other day. I liked some of them and didn’t like some of them. It wasn’t that there was anything inherently wrong with the ones I didn’t like, they just didn’t fit in with my world view.

Well, I guess that grant season was a bit of an optimistic time to try to do a 4 part series on frequency distributions, but I’ve got a few minutes before heading off to an all day child birth class so I thought I’d see if I could squeeze in part 2. OK, so you have some data and you’d like to get a rough visual idea of its frequency distribution. What do you do know? There are 3 basic approaches that I’ve seen used: Histograms.

We just read this great piece from the Huffington Post by Todd Palmer and Rob Pringle on why including funds for NSF and NIH in the stimulus bill was a good idea (thanks to Ecotone for pointing us to the article). The great thing about the piece is that it doesn’t just make a cogent argument for the stimulus funds, but for why funding basic science is economically beneficial in general.

Dealing with frequency distribution data is something that we as ecologists haven’t typically done in a very sophisticated way. This isn’t really our fault. Proper methods aren’t typically taught in undergraduate statistics courses or in the graduate level classes targeted at biologists.

Nathan over at Flowing Data just posted an interesting piece on the emergence of a new class of scientists whose work focuses on the manipulation, analysis and presentation of data.

Over at EEB and Flow, Marc Cadotte suggests that we consider adding “aesthetically pleasing details“ to our figures. I’m a big fan of visually pleasing figures and the examples that Marc gives show how a little extra effort can really improve communication. The post made me think of one of the earliest examples of adding… a little something extra… to one’s figures – Stuart Hurlbert’s unicorn (Hurlbert 1990).