DataDeps.jl: Repeatable Data Setup for Reproducible Data Science | Zendy

Lyndon White | Zendy; Roberto Togneri | Zendy; Wei Liu | Zendy; Mohammed Bennamoun | Zendy

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DataDeps.jl: Repeatable Data Setup for Reproducible Data Science

Author(s) -

Lyndon White,

Roberto Togneri,

Wei Liu,

Mohammed Bennamoun

Publication year - 2019

Publication title -

journal of open research software

Language(s) - English

Resource type - Journals

ISSN - 2049-9647

DOI - 10.5334/jors.244

Subject(s) - computer science , scripting language , replicate , software , programming language , code (set theory) , confusion , repeatability , replication (statistics) , data mining , r package , function (biology) , software engineering , psychology , statistics , chemistry , mathematics , set (abstract data type) , chromatography , evolutionary biology , psychoanalysis , biology

We present DataDeps.jl: a julia package for the reproducible handling of static datasets to enhance the repeatability of scripts used in the data and computational sciences. It is used to automate the data setup part of running software which accompanies a paper to replicate a result. This step is commonly done manually, which expends time and allows for confusion. This functionality is also useful for other packages which require data to function (e.g. a trained machine learning based model). DataDeps.jl simplifies extending research software by automatically managing the dependencies and makes it easier to run another authoru0027s code, thus enhancing the reproducibility of data science research.

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