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<h1>Reproducible computational research in the publication cycle</h1> <p><a href="http://meetingorganizer.copernicus.org/EGU2017/session/25726" rel="nofollow"><strong>EGU 2017 shourt course (SC81)</strong></a></p> <p><strong>Convener:</strong> Daniel Nüst<br> <strong>Co-Conveners:</strong> Edzer Pebesma , Vicky Steeves</p> <p><strong>Date/Time:</strong> Mon, 24 Apr, 13:30–17:00 / Room -2.85</p> <h2>Description</h2> <p>Reproducibility is unquestionably important for science, but introduces challenges in the context of scientific publications based on computations. In this short course we approach these challenges from two perspectives in two time blocks: (1.) a practical introduction to selected tools supporting computational reproducibility, and (2.) talks by stakeholders in the scientific publication process and a panel discussion.</p> <p>This course is set in an increasing athmosphere of openness: open access, open science, open data, and open source. Yet the majority of papers analyzing any kind of data is not accompanied by data, code and documentation which let reviewers or readers easily reproduce the calculations that underly the paper. EGU attendees from all scientific disciplines are welcome to bring their data and get some hands-on experience in the tools that facilitate reproducibility for computations in day-to-day research. A selection of invited speakers will then put these experiences into the broader context of the publication cycle by sharing their experiences, insights, and ideas for the future.</p> <p>The course starts with practical topics followed by presentations because we expect the practical experience will foster the discussion in the second part. A practical mindset will make issues more concrete and give a better understanding of what is important from the different viewpoints on a piece of research.</p> <h2>Schedule</h2> <h3>Hands-on Computational Reproducibility (1.5 hrs)</h3> <p>The first part are hands-on exercises. The attendees are introduced to selected tools to improve the reproducibility of computational research. They then try out these tools either using their own data, or prepared test data.</p> <p><strong>Agenda</strong></p> <ul> <li>Welcome & brief overview of relevant publications, definitions, and terms</li> <li>Docker containers for reproducible research in the geosciences (R, Python)</li> <li>ReproZip for geospatial analyses</li> </ul> <h3>Talks on computational reproducibility in the publication cycle (1.5 hrs)</h3> <p>After the hands-on experiences, the second session opens up to a broader context. Selected speakers provide insight on the topic of reproducing scientific papers from their role within the existing publication process, i.e. as publisher, reviewer, educator, or infrastructure provider. The talks are followed by a discussion between a panel of the presenters and the audience.</p> <p><strong>Confirmed speakers</strong></p> <ul> <li>Edzer Pebesma (geostatistics researcher, also journal editor JStatSoft/Computers & Geosciences)</li> <li>Tobias Weigel (big climate data, data management, researcher and expert perspective)</li> <li>Jens Klump (data + software publication and citation, expert perspective)</li> <li>David Ham (GDM executive editor, journal editor perspective)</li> <li>Xenia van Edig (Copernicus, publisher perspective)</li> <li>Vicky Steeves (librarian perspective)</li> <li>Daniel Nüst (research software engineer perspective)</li> </ul>
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