A few days ago, Kubuntu 13.04 Raring Ringtail was released. I am a Kubuntu user myself, and to celebrate this new release, I wanted to share a few tips on how to set up the perfect Kubuntu environment for neuroscientists and psychologists. Of course, the ‘perfect’ environment is different everyone, but there are a few things that almost every researcher in this field will need: An office suite, a reference manager, graphics software, statistics and analysis software, and experiment building software.

What is Kubuntu?

Kubuntu is a Linux distribution. If you’re not familiar with Linux, this may not mean much to you, so let’s start with a little background.

Some relations between various flavors of Linux.

A Linux-based operating system is a layer cake. It consists of many layers of software that can be stacked and combined in an infinite number of ways. Only the bottom layer is constant: That’s the Linux kernel, which is part of all Linux-based operating systems, including Android. On top of the kernel, there can be different layers of software. Kubuntu is essentially one specific selection of software. Other Linux distributions, such as openSUSE, have slightly different selections. Some differences are clearly visible, such as different desktop environments (i.e. the software that controls the start menu, etc.). Other differences are largely under the hood, such as different system management tools.

A Linux distribution arranges the many layers of software in such a way that you, as a user, don’t have to worry about how it works.

The preference for one Linux distribution over another is entirely one of taste. I like Kubuntu because it is built on Ubuntu and Debian. These are major distributions that offer a lot of software out of the box, and you can rely on them to provide regular updates and support. The difference between Ubuntu and Kubuntu is the desktop interface. Ubuntu uses Unity, which is good-looking, but highly simplified. Kubuntu uses KDE, which offers more flexibility. I particularly like Kate, the default KDE text editor, which is truly excellent for programming. The entire Ubuntu family shares the same innards, so the tips from this post apply to all of them.

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Let’s consider a biologist with an interest in swan coloration. She goes on an expedition to an area where two groups of swans live, to investigate whether the two groups have different colors. The biologist takes her job very seriously, and first calibrates a photometer against two reference colors: One for the ideal black swan; one for the ideal white swan. She then measures the color (or rather luminance) of ten specimens from each group, obtaining a range of values where 0 is ideal black and 100 is ideal white:

To analyze her results, she runs an independent samples t-test on the measurements, which tells her that p = .0001. This leads her to conclude that the two groups have different colors. Just as she suspected all along:

Our biologist is probably satisfied at this point. But we are not. What exactly has she learned from this t-test and the resulting p-value? Let’s start with the basics: What exactly does p = .0001 mean? Well … it means that if the two groups were really of the same color, the chance of observing a color difference as extreme as she observed, or more extreme, is .0001. This is an odd and counter-intuitive statement. Yet it is the foundation of most research.

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I’m typing this blog on a Raspberry Pi, a £25 / €30 / $40 mini computer that is literally the size of a credit card.

The Pi is an adorable machine (if you’re into that kind of stuff): Just a small printboard with connectors for a monitor, mouse, keyboard, and an ethernet cable. The system boots from an SD card, so there is no hard disk. There is a choice of Linux-based operating systems, the most commonly used being Raspbian, a Debian spin-off that has been optimized for the Pi. This is also what I installed. And in case you’re wondering: I didn’t open the Pi up – It just doesn’t come with a casing!

Because the Pi is extremely cheap, some people have wondered whether it could be used to equip low-budget psychology labs. This is also how I came into the possession of this diminutive cutie: It’s a gift from Clayton, who wondered how well OpenSesame would fare on the Pi. Thanks Clayton!

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Based on (critical) responses that I received, and discussions that I had after this post, I have added some footnotes to elaborate on certain aspects. The main criticisms are that pre-registration is not necessarily as rigid as I depict it to be (which may be true), and that questioning statistical guidelines is dangerous (which is certainly true, but also a moralistic fallacy: something can be correct and dangerous to say at the same time). Also, see my (sort of) follow-up post The Black Swan and NeuroSkeptic’s response.

In response to the many recent cases of scientific fraud, a debate has ignited about how science can be made more transparent, and how some of the public trust can be regained. Suggestions include …

An evil scientist.

• making all research data publicly available, not just the summarized results.
• making all scientific papers publicly available (i.e. open access).
• investing more time in replicating results, those of others as well as your own (e.g., the reproducibility project).
• and pre-registering all studies.

A slightly mysterious, but influential voice in this debate is Neuroskeptic. In a recent post, Neuroskeptic interviews Jona Sassenhagen, a neurolinguist from the University of Marburg, who decided to pre-register his EEG study. So what does it mean to pre-register a study, and why would anyone do this?

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