We’ve got a big year ahead of us as we work towards expanding our team and organizing various events and activities. We remain committed to supporting and expanding the landscape of open source tools that are available to researchers. While much of our focus has been around making it easier to access various data repositories, we are keen on improving other parts of the research pipeline, including data munging, documentation and sharing.

rOpenSci, whose mission is to develop and maintain sustainable software tools that allow researchers to access, visualize, document, and publish open data on the Web, is pleased to announce that it has been awarded a grant of nearly $2.9 million over three years from The Leona M. and Harry B. Helmsley Charitable Trust. The grant, which was awarded through the Trust’s Biomedical Research Infrastructure Program, will be used to expand rOpenSci’s mission of developing tools and community around open data and reproducible research practices.

A new version of rentrez, our package for the NCBI’s EUtils API, is making it’s way around the CRAN mirrors. This release represents a substantial improvement to rentrez, including a new vignette that documents the whole package. This posts describes some of the new things in rentrez, and gives us a chance to thank some of the people that have contributed to this package’s development. Thanks Thanks to everyone who has filed and issue or written us an email about rentrez, your contributions have been an important part of the package’s development.

We’re happy to announce the launch of a CRAN-style repository for rOpenSci at http://packages.ropensci.org This repository contains the latest nightly builds from the master branch of all rOpenSci packages currently on GitHub. This allows users to install development versions of our software without specialized functions such as install_github(), allows dependencies not hosted on CRAN to still be resolved automatically, and permits the use of update.packages(). Using the repository To use, simply add packages.

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.