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Got bugs? Here’s how to catch the errors in your scientific software

Science is becoming increasingly computational. Experimental data must be logged, cleaned, checked and analysed. Data analysis often involves iterative trial and error using ‘scripting’ programming languages such as Python and R. The outputs of such programs are then included in papers, presentations and grant applications. A typical piece of professional software contains up to 50…

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