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<p><strong>From the introduction chapter of <em>Implementing Reproducible Research</em>:</strong> Related to the need for good research practices to promote reproducibility is the need for software and methodological platforms on which reproducible research can be conducted and distributed. Mikio Braun and Cheng Soon Ong discuss the area of machine learning and place it in the context of open source software and open science. Aspects of the culture of machine learning has lead to many open source software package and hence reproducible methods. Christophe Hurlin and colleagues describe the RunMyCode platform for sharing reproducible research. This chapter addresses a critical need in the area of reproducible research, which is the lack of central infrastructure for distributing results. A key innovation of this platform is the use of the computing cloud to allow research findings to be reproduced through the RunMyCode web interface, or on the user's local system via code and data download.</p> <p>Perhaps the oldest "platform" for distributing research is the journal. Iain Hrynaszkiewicz and colleagues describes some of the infrastructure available for publishing reproducible research. In particular he reviews how journal policies and practices in the growing ?field of open access journals encourage reproducible research. Victoria Stodden provides a primer on the current legal and policy framework for publishing reproducible scientific work. While the publication of traditional articles is rather clearly covered by copyright law, the publication of data and code treads into murkier legal territory. Stodden describes the options available to researchers interested in publishing data and code and summarizes the recommendations of the Reproducible Research Standard.</p> <p><a href="https://openscienceframework.org/project/s9tya/wiki/home/" rel="nofollow">Return to Table of Contents</a></p> <p><a href="https://openscienceframework.org/project/hym6x/files/" rel="nofollow">View Platforms chapters for download</a></p>
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