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How to make a software more robust

Scientific quality and reproducibility rely on the traceability of the experimental data, statistical methods and bioinformatics tools used to generate results. Being unable to replicate and validate scientific results is unfortunately very common. This reproducibility crisis as named by Monya Baker considerably slows down the research progress and affects all of the fields including chemistry, biology and medicine.   Best practices are crucially needed today to improve reproducibility of data analysis and hence to make software robust enough to be run by any user.   Indeed, most of the software tools used to produce scientific results and publications are prototypes and lack robustness. Usually designed and run by a single person in a specific computing environment, codes may be very …

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