All posts (Page 116 of 130)
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. In particular, we welcome
Han Guangchun as a new contributor to rentrez and thank
Matthew O’Meara for posting
an issue that brought the by_id mode for entrez_link (discussed below) to our
attention.
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.ropensci.org to your existing list of R repos, such as:
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. The politics of data sharing have been the primary focus for debate over the last 5 years, but now that many journals and funding agencies are requiring data to be archived at the time of publication, the availability of these data fragments is increasing. But little progress has been made on the technical challenge: how can you combine a collection of independent fragments, each with its own peculiarities, into a single quality database?
...There are many different databases. The most familiar are row-column SQL databases like MySQL, SQLite, or PostgreSQL. Another type of database is the key-value store, which as a concept is very simple: you save a value specified by a key, and you can retrieve a value by its key. One more type is the document database, which instead of storing rows and columns, stores blobs of text or even binary files. The key-value and document types fall under the NoSQL umbrella. As there are mature R clients for many SQL databases, and dplyr is a great generic interface to SQL backends (see dplyr vignettes for an intro), we won’t delve into SQL clients here....
