In science, results that are not reproducible by peer scientists are valueless and of no significance. Among other practices, recording the provenance of data facilitates to reproduce results in data science and users can be confident in quality of the data. The talk shows how to record and to analyse provenance using the provenance model PROV for Python data analytics processes.


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    Titel :

    Provenance for Reproducible Data Science


    Beteiligte:

    Kongress:

    2017 ; Redmond, USA


    Erscheinungsdatum :

    2017-07-06


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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