"Reproducible research for big data in practice": call for abstracts EGU GA 2017 session09 Nov 2016
We are happy to announce that a session convened by o2r team member Edzer Pebesma along with co-conveners Yolanda Gil, Kerstin Lehnert, Jens Klump, Martin Hammitzsch, and Daniel Nüst was accepted at next year’s European Geosciences Union General Assembly.
The call for abstracts is now open. The abstract submission deadline is 11 Jan 2017, 13:00 CET. So there is plenty of time to contribute, prepare an abstract and share your experience of reproducible research.
From the session description:
This session will showcase papers that focus on big data analysis and take reproducibility and openness into account. It is open to members of all programme groups and scientific disciplines to present how they conduct data-based research in a reproducible way. They are welcome to share practical advice, lessons learned, practical challenges of reproducibility, and report on the application of tools and software that support computational reproducibility.
The session is co-organized as part of the Interdisplinary Event “Big Data in the Geosciences” (IE 3.3), and the division on Earth & Space Science Informatics (ESSI ESSI4.11). “Using computers” is the unifying feature of many a researcher in the scientific divisions, so we look forward to meet a diverse group of people next year in Vienna. In the session description the conveners point out that…
[c]omputational reproducibility is especially important in the context of big data. Readers of articles must be able to trust the applied methods and computations because [..] data are also unique, observed by a single entity, or synthetic and simulated. Contributions based on small datasets are of special interest to demonstrate the variety in big data. Topics may include, but are not limited to, reproducibility reports and packages for previously published computational research, practical evaluations of reproducibility solutions for a specific research use case, best practices towards reproducibility in a specific domain such as publishing guidelines for data and code, or experiences from teaching methods for computational reproducibility.