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Making maps of climate change

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....

Style GeoJSON

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....

From occurrence data to interactive maps on the web

We have a number of packages for getting species occurrence data: rgbif and rbison. The power of R is that you can pull down this occurrence data, manipulate the data, do some analyses, and visualize the data - all in one open source framework. However, when dealing with occurrence data on maps, it is often useful to be able to interact with the visualization. Github, a code hosting and collaboration site, now renders a particular type of map file format as an interactive map....

Revisiting our USGS app

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....

What we hope to accomplish with the new funding

At rOpenSci’s virtual HQ we’re busy planning out several exciting projects for the coming year thanks to the generous 180k grant from Sloan. In the interest of maintaining transparency with our community here are additional details of what we hope to accomplish and how we’ll measure our successes. We have also posted a full copy of our proposal over at figshare. 🔗 Objectives for the year a) Focus on identifying shortcomings, strengthening our core products, and working to link existing tools through interoperable data structures and visualization routines....

Working together to push science forward

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