https://puma.ub.uni-stuttgart.de/group/researchcode/dockerPUMA RSS feed for /group/researchcode/docker2026-04-23T11:17:52+02:00https://puma.ub.uni-stuttgart.de/bibtex/28bf997c8af607c68320a5df06e82c49b/diglezakisdiglezakis2017-09-12T16:24:17+02:00docker forschungsdaten software reproducibility softwareLizenzMotivation <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Carl Boettiger" itemprop="url" href="/person/1ff6c224775947fbda408aea734165c00/author/0"><span itemprop="name">C. Boettiger</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">SIGOPS Oper. Syst. Rev.</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">49 </span></span>(<span itemprop="issueNumber">1</span>): <span itemprop="pagination">71--79</span></em> </span>(<em><span>January 2015<meta content="January 2015" itemprop="datePublished"/></span></em>)</span>Tue Sep 12 16:24:17 CEST 2017New York, NY, USASIGOPS Oper. Syst. Rev.01171--79An Introduction to Docker for Reproducible Research492015docker forschungsdaten software reproducibility softwareLizenzMotivation As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.An introduction to Docker for reproducible research