The challenge of combining 176 x #otherpeoplesdata to create the Biomass And Allometry Database

  Daniel Falster   |   Rich FitzJohn   |   Remko Duursma   |   Diego Barneche   | JUNE 3, 2015

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. The political challenge is to manage the carrots and sticks needed to promote sharing of data within the scientific community.

Introducing Rocker: Docker for R

  Carl Boettiger   |   Dirk Eddelbuettel   | OCTOBER 23, 2014

You only know two things about Docker. First, it uses Linux containers. Second, the Internet won’t shut up about it. – attributed to Solomon Hykes, Docker CEO So what is Docker? Docker is a relatively new open source application and service, which is seeing interest across a number of areas. It uses recent Linux kernel features (containers, namespaces) to shield processes. While its use (superficially) resembles that of virtual machines, it is much more lightweight as it operates at the level of a single process (rather than an emulation of an entire OS layer).

Reproducible research is still a challenge

  Rich FitzJohn   |   Matt Pennell   |   Amy Zanne   |   Will Cornwell   | JUNE 9, 2014

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. It is also worth noting that reproducing research is not solely a checking process, and it can provide useful jumping-off points for future research questions. It is difficult to find a counter-argument to these claims, but arguing that reproducibility is laudable in general glosses over the fact that for each research group it is a significant amount of work to make their research (easily) reproducible for independent scientists.

dvn - Sharing Reproducible Research from R

  Thomas Leeper   | FEBRUARY 20, 2014

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). Most of these tools focus on improving one’s own workflow through closer integration of data analysis and report generation.

Open Science with R

  Karthik Ram   | DECEMBER 2, 2013

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 day to day research workflow.

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