• Keep your data tidy

    I came across this nice preprint by Hadley Wickham:

    Hadley Wickham (2014). Tidy data. Submitted.

    In this preprint, Wickham describes a way of organizing data he calls “tidy.” He then argues that tidy data and tidy tools (that both input and output tidy data) make data analysis much more efficient than any alternative approach can.

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  • Surviving the pre-tenure years at an R1 university

    A few days ago, Pröf-like Substance asked for posts with suggestions on how to survive the pre-tenure years. I went over my blogging history and realized that I hadn’t really written anything on this topic yet. Most of my advice to date is targeted at more junior scientists. So here is my attempt at giving some suggestions on how to make the most out of your years on the tenure track.

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  • Eat more gluten? Maybe not.

    A recent article in Time magazine argues that “gluten free” is a fad and should die. While the author makes a few good points, overall I think he misses the mark. I agree with the author that when it comes to products where the main ingredient is wheat (in particular bread, pasta, pizza base, cereals, cookies, cakes), gluten-free replacements usually aren’t that healthy. These replacement products are frequently made of rapidly digestible carbohydrates and tend to be nutrient poor. However, what the author fails to mention is that the products being replaced are also made of rapidly digestible carbohydrates and are nutrient poor. There’s really not that much of a difference between gluten-free bread made of tapioca flour and millet and regular gluten-containing bread made of wheat flour. Most people would be better off avoiding both.

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  • Share your preliminary work with other people, even if you think it’s crap

    It’s quite common for me to have students tell me “the analysis didn’t work out” or “the figure looks bad” or “I don’t have any useful results.” And it’s also quite common for the students to be wrong. Sometimes, students have amazing results but are all disappointed because the results aren’t what they had expected. These students fail to see the data for what they are. More commonly, the students may be right in that the data aren’t that great, but usually I can see something in the data that the student didn’t. In either case, it is important that we look at the data together, because jointly we will see more than either of us individually would have seen.

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  • How to develop a research question, Part II

    After my last post discussing how to develop a research question, Sergey Kryazhimskiy asked me to write about how to find the rare good research idea among the many mediocre ones.

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  • How to develop a research question

    One of the most daunting prospects for a fresh graduate student is having to develop a solid research question [1]. In my experience, many graduate students feel like they don’t even know where to start. The literature can seem overwhelming, everything has already be done by somebody, and in any case it’s impossible to really know all the literature there is anyway. Making matters worse, almost every cohort or lab inevitably has one or two students who just seem to be fountains of good ideas, who constantly come up with new research ideas they want to pursue. As a result, students who are less inventive or less imaginative can feel like they’re not cut out for a career in research, they’re never going to have the necessary ideas to sustain a research program.

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  • Uri Alon on creativity and staying sane in science: “Yes and ...”

    I just saw this nice TED Talk by Uri Alon about how new scientific insights are generated. Even though the talk is a year old I think it’s worth posting, so here you go.

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  • How to pick a thesis committee

    I was asked the other day what factors to consider when picking a thesis committee. And I realized that this is not a question I have pondered a lot. Normally, when one of my students needs to pick a committee, I just recommend people that seem a good choice. I don’t have a properly thought out, systematic framework to steer the selection process. So with this post I’ll try to develop a more systematic approach to this question.

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  • 6 reasons to do your graduate work in the lab of a junior PI, and 6 reasons not to

    One of the eternal questions of graduate schools is whether you should work with a junior or a senior PI. I have commented on this question before and argued that either decision can be the right one. Here, I present a more comprehensive list of arguments for and against. As you’ll see, there are plenty of arguments going both ways. You may assign more weight to some than others and thus arrive at a decision that is best for you. Ultimately, I think it doesn’t matter too much; there are other factors that are more important, such as whether you enjoy and fit in with the lab’s culture and approach to research, collaboration, publication, and so on.

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  • Understanding the graduate-school interview or recruitment event

    It’s the beginning of the new year, and with it comes graduate admissions time. If you are currently applying for graduate programs in the sciences, you hopefully have received or will soon receive one or more invitations to interviews or recruitment events. If you’re wondering what such an invitation means, how these events work, and how to best prepare yourself, read on.

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  • What does it take to be a computational biologist?

    I would like to talk about what it takes to be a computational biologist, specifically in comparison to being an experimental biologist. If you’re wondering whether instead of becoming a computational biologist you should become a race-car driver, fighter pilot, or ballet dancer, this post probably won’t help you. But if you’re wondering whether a computational biology lab is a better choice for you than an experimental biology lab, this post should provide you with some useful guidelines. To cut right to the chase, here is the take-home message: To become a computational biologist, you need to want to become a computational biologist.

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  • How glamour journals rose to prominence, and why they may not be needed anymore

    In the ongoing discussion about the value of glamour journals such as Nature, Science, and Cell, I think it’s worth looking back and asking: “How did they rise to prominence?” and “Are they still serving the same role they did when they arose?” So let’s take a quick trip into the history of science communication, before the internet. Then we can ask what the internet has changed, and how we could make the best of current technology.

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