Abstract
General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. Abstract—Computational methods have become a prime branch of modern science. Unfortunately, retractions of papers in high-ranked journals due to erroneous computations as well as a general lack of reproducibility of results have led to a so-called credibility crisis. The answer from the scientific com-munity has been an increased focus on implementing reproducible research in the computational sciences. Researchers and scientists have addressed this increasingly important problem by proposing best practices as well as making available tools for aiding in implementing them. We discuss and give an example of how to implement such best practices using scientific Python packages. Our focus is on how to store the relevant metadata along with the results of a com-putational experiment. We propose the use of JSON and the HDF5 database and detail a reference implementation in the Magni Python package. Further, we discuss the focuses and purposes of the broad range of available tools for making scientific computations reproducible. We pinpoint the particular use cases that we believe are better solved by storing metadata along with results the same HDF5 database. Storing metadata along with results is important in implementing reproducible research and it is readily achievable using scientific Python packages.
Cite
CITATION STYLE
Oxvig, C., Arildsen, T., & Larsen, T. (2016). Storing Reproducible Results from Computational Experiments using Scientific Python Packages. In Proceedings of the 15th Python in Science Conference (pp. 45–50). SciPy. https://doi.org/10.25080/majora-629e541a-006
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