In Good uses for fake data (part 1), I expounded on the virtues of fake – or “toy” – datasets for understanding statistical analyses. But that’s not the only good use for fake data. Fake data (this time, maybe a better term would be “model data”) can also be extremely useful in planning and writing up research. Once again, let me assure you that I’m – of course – not advocating data fakery for publication! Instead, fake data can help you think through how you’re going to present and interpret results of an experiment or an analysis (or perhaps, even if you can interpret them), before you actually spend effort getting data in hand.
I’d put this in the context of “early writing”, which is a strategy that interweaves the writing of science with the doing of science – as opposed to doing the science first and writing it up when it’s “done”, which always seemed to me so obvious I never thought to question it. Early writing makes writing easier, and can help you spot problems with your work’s design before it’s too late. Continue reading
Graphic: Results of a discrete-time simulation with two competitors having a shared predator. Exercise for the reader: which trace is the predator?
Warning: wonkish. Of interest primarily to those who teach upper-level ecology courses.
I don’t have an important message today, or a big unresolved question to talk about. I just thought I’d share some teaching resources. If you teach ecology (past the introductory level), you may find this useful.
One of the major themes in my 3rd-year population ecology course is the diversity of population dynamics that can emerge even in fairly simple systems: extinction, stable equilibrium, damped oscillations, stable limit cycles, neutral cycles, chaos, and so on. We spend a lot of time on the kinds of ecology that tend to favour oscillations (things like time lags and enemy-victim interactions) as opposed to those that tend to favour stable equilibria (things like immediate density-dependence and, under some circumstances, interspecific competition). Continue reading
Photo: Stockholm public library, Marcus Hansson, CC-BY-2.0 via flickr.com
I’ve always loved libraries. As a young boy, I combed the stacks of my town library, finding great books that took me far beyond the world I knew (40 years later, I’m sharing those same books with my son*). As a no-longer-young academic, I love libraries just as much; but my reasons have changed a bit, and so has my understanding of what a library is.
As a boy, I loved the library because I loved books, and I understood the library as a building full of books. Libraries still have a lot of books, but fewer than they used to, as information has moved online and more square footage is allocated to meeting rooms, study space, computer stations, and so on. It’s not hard to find someone declaring with anguish the death of the library; it’s also not hard to find someone declaring that same death with smug pleasure. For a while I was tempted to join the anguished camp; but instead I’ve come to see the library not as a building full of books, but as a building full of librarians. And if books are wonderful, well, librarians are pretty great too. Continue reading
Photo: Poster session, SunShot Grand Challenge Summit and Technology Forum, Denver; Dennis Schroeder/NREL via flickr.com. Public domain (US government agency).
I guess I’ve had conferences on the brain lately, with posts about why conferences have themes and about how I survive conferences as an introvert. But there’s one question about conferences I hear asked more than any other: Should I give a poster or a talk? Continue reading
Graphic: A fake regression. You knew those were fake data, right? I may spend my entire career without getting a real regression that tight.
If you clicked on this post out of horror, let me assure you, first off, that it isn’t quite what you fear. I don’t – of course – endorse faking data for publication. That happens, and I agree it’s a Very Bad Thing, but it isn’t what’s on my mind today.
What I do endorse, and in fact encourage, is faking data for understanding. Fake data (maybe “toy data” would be a better term) can help us understand real data, and in my experience this is a tool that’s underused. Continue reading
Graphics: Cover design for The Scientist’s Guide to Writing (Princeton University Press, April 2016). Twirling DNA: by brian0918; own work, released to public domain, via wikimedia.org. DNA structures: by Thorwald, released to public domain via wikimedia.org.
Warning: an unusual foray into biochemistry is coming at you. Stick with it, though; there’s an interesting story here, on a couple of levels.
If you’ve been reading Scientist Sees Squirrel, you probably haven’t missed my occasional oh-so-subtle references to my forthcoming writing book, The Scientist’s Guide to Writing (Princeton, April 2016). I was very excited last month to finally see the cover design, and last week to be given the all-clear to release it publicly. I did so on Twitter, but within hours three sharp-eyed followers replied to my celebratory tweet with the news that there was something wrong with the graphic design. Continue reading
Image: Joe Wolf via flickr.com CC BY-ND 2.0
While I was grumpy recently about conferences having themes, I’m not at all anti-conference. In fact, I agree entirely with Terry McGlynn here that going to conferences and meeting people in your field is really important. In fact, I just registered for two of the four conferences I plan to attend this year (a personal record high). But here’s the thing: while I’m looking forward to them, I’m also not, because like a fair number of academics I’m an introvert*. I find conferences, and all the people at them, exhausting. So I’ve been running through in my mind some of the strategies I use to cope. If you’re a little like me, maybe you’ll find some value in my writing them out**. Or at least in knowing that you aren’t the only one. Continue reading