Earlier this year I was a guest on Talk Python , a wonderful podcast on my favourite programming language. Did you come into Python from a computational science side of things? Were you just looking for something better than Excel or Matlab and got pulled in by all the Python has to offer? That’s great! But following that path often
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I am used to confused interactions, likely because I get along better with dogs and kids than I do with adults. But that is not what Nieuwenhuis et al. had in mind when they surveyed 513 articles relating to behavior, cognitive function or brain imaging from leading journals (Nature, Science, Nature Neuroscience, Neuron, Journal of Neurosience). What they were looking
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For better or worse, our mind plays tricks on us: we are prone to cognitive biases and logical fallacies. These biases don’t impact how do the laundry or prepare supper, although I may convince myself that I always intended to make a galette despite having the cookbook open at the soufflé page. Unfortunately, these same biases and fallacies can wreck
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Scientific impact is a difficult thing to measure. Nevertheless, a variety of indexes and metrics have been created that attempt to quantify it. Many of these measures are based on citations: the number of times a scientific paper is referenced by another scientific paper. Unfortunately, it is not entirely clear what these citation-based metrics actually reflect, nor is it clear
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In my last two posts, I talked about my experience with errors in science. Errors are part of life, and they are part of science. Without errors we can’t learn. However, if we hide away our data, our analysis and, if relevant, our code, errors will not be noticed. They and the data and results they generate will become part
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Science and computers go hand in hand. But so do humans and errors. That is why in my previous post I talked about my experience of how science, a human endeavour, does not always apply due diligence when it comes to ensuring errors are avoided. I don’t think this is intentional, but rather a mix of ignorance, bias, and lack
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