In light of recent depressing posts on the reproducibility crisis and the natural selection of bad science, I thought it worthwhile to revisit why we actually try to do good science, despite the pressures to compromise, and what qualities good scientists possess. Some time ago, the cell biologist Martin Schwartz wrote an interesting and honest essay on why, for sincere
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In the previous post, we created a branch called bug_fix_#001 and learned to make this our current branch using the git checkout <branch name> command. Once on this branch we made a change to one of our files (simulating a bug fix) and committed the change. By looking at the content of the changed file and our Git log, we
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Every scientist has, at one point or another in their career, plotted results using bar graphs or dot plots with error bars. As pointed in a previous post, these kinds of summary graphs can be misleading, especially since a depressingly large number of scientists plot their error bars as the standard error of the mean (SEM) rather than the standard
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We have seen how Python can be used to process and analyse EMG signals in lessons 1, 2 and 3. When EMG signals are filtered, how does changing filter settings change the appearance of the filtered EMG signal? A low pass filter allows frequencies below the cut-off frequency to pass through (ie. higher frequencies are removed). The filtered EMG signal
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In the previous lesson we learned that our EMG signal had some problems: Baseline EMG values have an offset from zero. Baseline EMG values are noisy. Also, the EMG signal possess both negative and positive values. This means if we wanted to calculate an average or mean EMG, the negative and positive values will cancel out. We can apply 3
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In the previous lesson, we simulated data of two EMG ‘bursts’ during a muscle contraction. The data are shown in Figure 1. Figure 1: Simulated EMG data from 2 muscle contractions The code to generate Figure 1 and the explanations are as follows: 1 2 3 4 5 6 7 8 9 10 11 12 13 14
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