Working together to push science forward
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We recently had a paper come out in a special issue on article-level metrics in the journal Information Standards Quarterly. Our paper basically compared article-level metrics provided by different aggregators. The other papers covered various article-level metrics topics from folks at PLOS, Mendeley, and more. Get our paper. To get data from the article-level metrics providers we used one R package we created to get DOIs for PLOS articles (rplos) and three R packages we created to get metrics: alm, rImpactStory, and rAltmetric....
It’s the last week in July and this means that ecologists across North America (and elsewhere) are busy returning from the field and preparing their presentations and posters in anticipation of the annual Ecological Society of America meeting. The entire rOpenSci dev team will be in attendance this year and we have several workshops, talks, and events planned out. The topics range from half-day workshops on open data, data visualization, reproducible research, to an entire symposium on open science....
One of our primary goals at ROpenSci is to wrap as many science API’s as possible. While each package can be used as a standalone interface, there’s lots of ways our packages can overlap and complement each other. Sure He-Man usually rode Battle Cat, but there’s no reason he couldn’t ride a my little pony sometimes too. That’s the case with our packages for GBIF and the worldbank climate data api....
A recent video on the PBS Ideas Channel posited that the discovery of climate change is humanities greatest scientific achievement. It took synthesizing generations of data from thousands of scientists, hundreds of thousands (if not more) of hours of computer time to run models at institutions all over the world. But how can the individual researcher get their hands of some this data? Right now the World Bank provides access to global circulation model (GCM) output from between 1900 and 2100 in 20 year intervals via their climate data api....
Previously on this blog and on my own personal blog, I have discussed how easy it is to create interactive maps on Github using a combination of R, git and Github. This is done using a file format called geojson, a file format based on JSON (JavaScript Object Notation) in which you can specify geographic data along with any other metadata. In my previous post on this blog about geojson, I described how you could get data from the USGS BISON API using our rbison package, then make a geojson file, then push to Github....
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