R has a reputation of not playing nice on the web. At rOpenSci, we write R pacakages to bring data from around the web into R on your local machine - so we mostly don’t do any dev for the web. However, the United States Geological Survey (USGS) recenty held an app competition - it was a good opportunity to play with R on the web. We won best overall app as described in an earlier post on this blog.
The USGS recently released a way to search for and get species occurrence records for the USA. The service is called BISON (Biodiversity Information Serving Our Nation). The service has a web interface for human interaction in a browser, and two APIs (application programming interface) to allow machines to interact with their database.
We’ve been busy We have been busy hacking away at code and our website. Here is an update on what we’ve been up to. Packages rplos/alm PLoS provides two different API services: the Search API and ALM API. As their names suggest, the search API lets you search and get text from their papers and associated metadata. The ALM API allows you to get article level metrics data on PLoS papers.
A guest blog post by Steve Moss Why Python? A little background! I started using Python in the summer of 2010. I had applied for the Master of Research postgraduate degree in Computational Biology at the University of York. They teach the programming portion of their course using Python. I thought it might be useful to learn it, before starting, to give me a bit of a head start.
Many US federal agencies are now running app competitions to highlight their web services (see here), and hopefully get people to build cool stuff using government data (see Data.gov for more). See here for a nice list of the US government’s web services. One of these agencies was the United States Geological Survey (USGS). They opened up an app competition and [we won best overall app!
Real use cases from people using our software are awesome. They are important for many reasons: 1) They make the code more useable because we may change code to make the interace and output easier to understand; 2) They may highlight bugs in our code;
Scholarly metadata - the meta-information surrounding articles - can be super useful. Although metadata does not contain the full content of articles, it contains a lot of useful information, including title, authors, abstract, URL to the article, etc. One of the largest sources of metadata is provided via the Open Archives Initiative Protocol for Metadata Harvesting or OAI-PMH.
We have been writing code for R packages for a couple years, so it is time to take a look back at the data. What data you ask? The commits data from GitHub ~ data that records who did what and when. Using the Github commits API we can gather data on who commited code to a Github repository, and when they did it. Then we can visualize this hitorical record.
