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Reproducibility
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`Reproducibility`__ is the ability of an entire analysis and computation of a problem under study to be duplicated. It is one of
the main principles of the computational research enterprise. As the scientific method is being transformed by massive
computations and data manipulations, it becomes essential to establish
procedures to ensure and facilitate reproducibility. Replication can however be complicated due to the emergence of new
computational tools and technologies, massive amounts of data,
interdisciplinary approaches, and the complexity of the problems such
as `Grand Challenges`__, including problems related to climate modeling, modern materials,
and medicine. Establishing feasible practices of reproducibility is
currently a central focus of researchers in the field of computational
science.

A good practice of reproducibility is necessary, for instance, to
allow previously developed methodologies to be effectively and
efficiently applied to new data and problems. Releasing and making the data and developed software `accessible` to the
wider scientific community would allow reuse of developed codes and
data for new projects. Nowadays, most peer-reviewed journals allow
articles to be supplemented with online material (such as code and
raw data), and some journals
have initiated further efforts for making data and code more
integrated with publications. Importantly, the establishment of such
rules and practices will increase `transparency` and reproducibility of
scientific works. See `here`__ and the references therein if you are
interested in learning more about thi topic.



In this class we will concentrate on `version control`__ as one easy
way to greatly improve the reproducibility of our scientific works.

__ http://en.wikipedia.org/wiki/Reproducibility
__ https://en.wikipedia.org/wiki/Grand_Challenges
__ http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285
__ http://math.unm.edu/~motamed/Teaching/Fall20/HPSC/vc.html