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Monthly Archives: November 2010

And the lights are back on.

Well, I finished with my exam this evening and sent it off.  We’ll see how that goes – I still have to put together and do a presentation in two weeks time.  But since I’m back, in the mean time I thought I’d take the time to shift gears and do some writing again.

And one of the things that caught my eye yesterday was this piece by Satoshi Kanazawa over at Psychology Today, fetching entitled “What if it turns out the Earth was flat after all?” Kanazawa is lamenting the rise of the Freudian explanation for homosexuality in the 1960s, which displaced nascent explanations based on genetics and in utero development.  In his last paragraph, Kanazawa writes:

What happened?  How did we go wrong?  How could scientists in the early 1960s abandon (what we know today to be) the true theory of male sexual orientation for such Freudian nonsense?  In 1966, I was in kindergarten; I was too busy writing a (not terribly original) sequel to 101 Dalmatians to stay abreast of the cutting-edge frontiers in sex research.  (I also believed that girls had cooties, so I would not have made a good objective scientist then.)  But if this kind of reversal of knowledge can happen, if scientific knowledge is not cumulative but cyclical, as sociologists and philosophical conventionalists and relativists would have you believe, then how can we trust any of the knowledge that we produce?  How do we know, for example, that the earth is not flat after all?  We once believed that the earth was flat, but the notion was abandoned in preference for the new idea that the earth was round.  How do we know that, at some point in the future, it will not turn out that the earth was flat after all, as the ancients always believed?

Kanazawa makes two surprisingly basic errors here.  The first is exceedingly obvious:  the ancients didn’t believe that the world was flat.  Eratosthenes, for example, had already worked out an estimate for the circumference of the Earth as far back as 240 B.C.  (I’m nearing the end of a great book that deals with the way our view of the Earth has evolved over the centuries;  I think that Kanazawa would find it an enlightening read).

His second error is a little less obvious, but much more important.  The error is committed in the last paragraph, but it’s set up in the first:

Science is a cumulative endeavor.  We build on past knowledge to attain even greater knowledge than before in a progressive manner.  Unfortunately, however, science doesn’t always work as it should.

The view that Kanazawa puts forth of science, as a straight forward and exceedingly linear progression, is fairly simplistic.  Reality is a bit more complicated:  science progresses, and it will always move forward (eventually), but the speed and direction of the movement of scientific knowledge is dependent on evidence.  When evidence comes quickly and in large amounts, science moves forward quickly and with increasing accuracy.  When evidence is scant or non-existent, scientific knowledge can meander back and forth or even double back.

And it is this context that Kanazawa misses.  In the 1960s, genetics was still a relatively young empirical science, and the evidence concerning possible genetic underpinnings to homosexuality was still weak, at best. With little to no evidence allowing scientists to make a selection between two competing theories, it is no surprise that the scientific community could be dragged off-course.  And, of course, when the evidence began to accumulate for a more complete view of the origin of homosexuality, one based on a better understanding of the interaction between genetics, development, and environment – well, the scientific community adjusted course appropriately.

As to the notion that scientific thought is cyclical, well, I don’t have a lot of time for that nonsense.  People who believe that science is cyclical or just a matter of convention need to stop using antibiotics, using their computer, wearing modern clothes, eating food, … well, my point should be obvious.  Science isn’t infalliable by any means, but the community is not going to suddenly ignore mounds of accumulated evidence and adopt a false belief.  It may go astray when there is no evidence to lead it by the hand, but there is little need to worry:  the world is still round.

Confounding variables of the week.

Via Devour, I saw this heart-warming video:

And it looks like a great idea.  I’d love to see it implemented all over the place.  But the suggestion in the video that it reduces speeding is … challenging, unless there were cameras set up in in other places that we didn’t see.  The implication is that the lottery (the “fun”) caused a reduction in speeding, but the drivers who sped were being punished by receiving a speeding ticket.  So, assuming the reduction in speed is real and not a fluke, there’s 4 plausible scenarios:

  1. The reduction in speeding was just an artifact of the bright sign that said “Hey, there’s a speeding camera here.”  We know that the simple act of observation can change people’s behaviour, and maybe that happened here.
  2. The reduction in speeding was due solely to the punishment of receiving (or the prospect of receiving) a speeding ticket.
  3. The reduction in speeding was due to the reward of being entered into the lottery.
  4. The reduction in speeding was due to some combination of the previous three variables.

I’d love to see this studied a little more rigorously;  have some signs without cameras, have some signs that just send tickets, have some signs that just enter into the lottery, have some tickets sent from hidden cameras, have some entries sent by hidden cameras, and so on.  With a proper design, it should be possible to tease these variables apart, though (in fairness) it would be a laborious process.

But wouldn’t it be great if we could prove that fun makes people speed less?

Interesting new site -> “Phylointelligence: Evolution for everyone”

At this point, my site stats tell me that this blog has somewhere around 1 reader, and that might actually just be me (wait, am I violating some sort of blogospheric fourth wall here?). So, I feel a little ridiculous posting links that no-one will read,  but this site is such an interesting idea that I’m willing to shout into the void:

Phylointelligence: Evolution for everyone.

The site is well-designed and though the content is still a little rough, I like the idea of cataloging the evidence for evolution in a way that is accessible to general readers.  I also like the focus on the overlapping lines of evidence which support evolution.  It’s a site that I’ll be keeping an eye on, and if I can find the time, maybe I’ll even contribute something to it…

[h/t: Pharyngula].

An iPad in the land of academentia…

I recently attended ISBE (the conference for the International Society of Behavioral Ecology) in Perth, Australia, and at the conference I received no end of comments about my iPad from fellow scientists. Of course, I wasn’t going out of my way to hide it; I was using it in every talk I went to to take notes, and referring to the PDF version of the conference schedule to plan my next move. And more than one person asked me, just as they have at home when I carry it around school, what I use it for. It’s (usually) not a malicious question – they just don’t understand the use case.

 

My set-up...

 

And so, I thought it might be worthwhile to explain, for the curious souls out there, why I’m using an iPad in my academic work and why I love it so much!

For context, my setup is this: I have a black 13″ Macbook as my first line of computing, which includes anything that requires heavy amounts of editing and for all of my simulation work. Tools that I routinely use include Firefox, Mathematica, R, LaTeX (TeXShop and BibDesk for the win here), Aquamacs (for code editing), Dropbox (which *everyone* should use), and various command line tools like ssh. I write code in Python, C / C++, Objective C, and Lisp when the mode takes me (and I have a book on Haskell that I’ve been meaning to get around to!), and I do source control with git.

All of my simulation and numerical work is tested on my Macbook, and then I upload it to a 8-core Xeon server that I built myself, running the latest Ubuntu flavor. Since I’m currently in Edmonton and my lab is in Montréal, I remote administer the server over ssh and sometimes graphical tools like VNC if I need to, but that’s pretty rare. I shuttle files back and forth over vanilla sftp or, as is more common these days, I simply do all of my work in my Dropbox and let the software handle syncing files.

But wait: this post was supposed to be about the iPad, wasn’t it? Well, it is – but it makes sense to mention what I don’t do with my iPad before I talk about what I do do with it.

And in truth, much of what I used to do with my laptop is now handled by the iPad. For instance, all of my reading is now done on the iPad, unless I absolutely cannot find an electronic copy. I read all of my journal articles using iAnnotate, which I can use to read and read articles before dumping them back into my Dropbox for filing away in Bibdesk. More and more books these days, even academic books, are available in PDF or other eBook formats, and for these I use Goodreader. When watching talks, I take my notes using Notetaker HD with a Pogo Sketch stylus, which I will later review and in some cases, transcribe. For other notes, I use InScribe, but to be honest I’m not entirely happy with it. I’m hoping that Circus Ponies will finally get around to releasing their version of Notebook for the iPad.

I also use the iPad to organize my thoughts on topics I’m working on. To do this, I’m loving Corkulus, which I use in a nonlinear way, adding notes and images that are relevant to the topic to keep all of my thoughts in one place. For mathematical thoughts, I use SpaceTime, and instead of napkins, I scribble on iDraft. And to record what exactly it is I know and why, it’s actually with Safari; I use a Tiddlywiki on Tiddlyspot, putting together my own version of The Book using a personal wiki.

And of course, there’s the tasks of every day life;  I have to keep track of my to-do list, my calendar, deal with administration of my research, and so on.  For todos, I use the aptly-named Todo so that I can keep my life synced across my iPad, my Mac, and my phone.  I used to use Things, but frankly the glacial pace of development at Cultured Code drove me to look for a new solution.  Calendaring is iCal on all three devices, synced through Google Calendar, and I keep in touch with my servers using iSSH.

When it comes time to do something with all of that material that I’ve read and all those notes I’ve taken, I usually do my writing on my laptop. But even some of that is migrating over to the iPad; for MS Office documents that are foisted on me, I use Quickoffice, and I’m currently trying out TexTouch for modifying LaTeX files.  I recently picked up the Apple wireless keyboard to help with long-form text entry when I’m on the road; for instance, I wrote this entire post using my iPad and the keyboard.

The final step is to show others what I’ve done. At ISBE, I effectively left my laptop at home (I brought it to work on a model with a colleague, but aside from that I never took it out of my bag), and instead of driving my presentation off my laptop or a USB key, I put it together in Keynote and used the iPad Keynote with the VGA connector to run the presentation. A killer feature of the iOS version of Keynote is that you can hold your finger down on the screen of the iPad and a laser pointer will show up on the presentation screen.

So, to round up, I use my iPad to read articles, take notes in a variety of situations, write documents, and give presentations. These were all tasks that I used to do on my laptop, but which have now migrated to the iPad. In fact, I would say that about 70% of the time that I used to spend on my laptop is now spent on my iPad. And that is what I use my iPad for and how I use it!

Science quote of the day

All sciences are connected; they lend each other material aid as parts of one great whole, each doing its own work, not for itself alone, but for the other parts; as the eye guides the body and the foot sustains it and leads it from place to place.

- Roger Bacon

This is one of the things that I love most about science: the interconnected nature of the enterprise, where every question leads you down another path of curiosity and lets you traipse through someone else’s backyard of knowledge. We can be too focused on our own domain sometimes, possibly as a defensive reaction to the massive flood of information coming at us from our own little corner of our own little subfield. But we must never forget that we are traversing a web, and that nothing we do makes sense without pulling back to see its entirety.

You must post your source code in science.

An image of the Ctrl-Alt-Delete source code.

Show me the source!

I’ve made it pretty clear by now that I do a lot of computational work in my research, so you can imagine that my metaphorical ears perked up when I came across this article in Nature by Nick Barnes about releasing scientific source code when you publish research papers.  I liked the article he wrote, but since this is my blog, I’m allowed to go even further than he did.

I’ve written a lot of software as a hobbyist over the years, ever since I began programming in elementary school.  A lot of it I simply tinkered with and forgot, but I’ve released source into the wild before, though I’m not an open source / free software zealot in general;  for example, I think that the FSF is, frankly, a little out to lunch about the issues.

However, when it comes to science I am militant:  if you publish a scientific paper based on the results of code that you have written, then that code is a part of your methodology and must be made available to others in the scientific community so that they can examine it and replicate your results.  Nobody is allowed to get away with saying “well, we did this DNA sequencing, but I’m not going to tell you how we did it, what materials or equipment we used, or what our procedures were – you just have to trust us that our results are right”.  That wouldn’t fly in any reputable journal, and it shouldn’t be allowed when it comes to source code.  The Barnes article implies that some people are too ashamed of their code to release it – but if you’re too ashamed to let other people see it, why are you publishing results based on it?

The other excuses he mentions in the article are equally rubbish, but one that he didn’t mention (which I’ve actually come across) is “oh, well, I can’t give you the source code because you’ll use it to do further work and publish papers that I want to do”.   This infuriated me when I heard it.  How the author in question managed to justify this in my head mystifies me;  where in science do you get to claim that you can’t release your methodology because other people might replicate or extend your work?  That’s the whole point of science.  You don’t get to write a paper which proposes a great new method and then admonish people that they can’t use it until you’re done publishing on it!

The only possible exception that I can see to this is in cases where the paper describes a finished product that is being made available to the scientific community (either free or for pay), but I don’t think too much about these cases because they strike me as being more of an advertisement than anything else.  There’s nothing inherently wrong with that, but it will also be fairly rare.  I would also distinguish between specific products (like, say, a GIS tool) and a new method, like a statistical analysis package.  The latter should release the code, no exceptions, because otherwise we can’t validate the method for ourselves.  An example of doing it right comes from the Laland lab at St. Andrews, who published a new method for measuring the spread of information across a network (network based diffusion analysis; NBDA).  Along with the paper, they released the source code and a package for R to help users implement and use the method themselves.

In the end, science thrives on the free exchange of information for the advancement of our collective knowledge. Anyone who feels that their source code is not a part of that exchange is not only wrong, they’re doing bad science.

Image credit: ptufts