Our 1-hour Call on Reproducible Research with R will include three speakers and 20 minutes for Q & A. Ben Marwick will introduce you to a research compendium, which accompanies, enhances, or is a scientific publication providing data, code, and documentation for reproducing a scientific workflow.

Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out.

Despite the hype around “big data”, a more immediate problem facing many scientific analyses is that large-scale databases must be assembled from a collection of small independent and heterogeneous fragments – the outputs of many and isolated scientific studies conducted around the globe. Collecting and compiling these fragments is challenging at both political and technical levels.

So what is Docker? Docker is a relatively new opensource applicationand service, which is seeing interest across a number of areas. Ituses recent Linux kernel features (containers, namespaces) to shieldprocesses.

Science is reportedly in the middle of a reproducibility crisis. Reproducibility seems laudable and is frequently called for (e.g., nature and science). In general the argument is that research that can be independently reproduced is more reliable than research that cannot be independently reproduced.

Reproducible research involves the careful, annotated preservation of data, analysis code, and associated files, such that statistical procedures, output, and published results can be directly and fully replicated. As the push for reproducible research has grown, the R community has responded with an increasingly large set of tools for engaging in reproducible research practices (see, for example, the ReproducibleResearch Task View on CRAN).

Upcoming Book on Open Science with R We’re pleased to announce that the rOpenSci core team has just signed a contract with CRC Press/Taylor and Francis R series to publish a new book on practical ways to implement open science into your own research using R. Given all the talk about the importance of open science, the discussion often lacks practical suggestions on how one might actually incorporate these practices into their