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Reproducibility is a cornerstone of scientific research. In this guide we focus on computational reproducibility, which means that another researcher can take your data, code and other computer files central to your research results, run them on their computer, and obtain substantially the same results that you did.
First, reproducibility enables you to show evidence of the correctness of your results. Other researchers can inspect your entire workflow and evaluate all of the decisions you made during your analysis. This makes your research more robust.
Second, making your research reproducible enables others to make use of your methods and results. This can increase the visibility and impact of your work, and increases the pace and efficiency of science.
The idea is to make detailed information about code, software, hardware and implementation openly available so that another researcher could reproduce the results of your analysis. In practical terms, this means: