Working together to push science forward
Happy rOpenSci users can be found at
Everybody talks about the weather, but nobody does anything about it. - Charles Dudley Warner As a scientist who models plant diseases, I use a lot of weather data. Often this data is not available for areas of interest. Previously, I worked with the International Rice Research Institute (IRRI) and often the countries I was working with did not have weather data available or I was working on a large area covering several countries and needed a single source of data to work from....
camsRad is a lightweight R client for the CAMS Radiation Service, that provides satellite-based time series of solar irradiation for the actual weather conditions as well as for clear-sky conditions. Satellite-based solar irradiation data have been around roughly as long our modern era satellites. But the price tag has been very high, in the range of several thousand euros per site. This has damped research and development of downstream applications. With CAMS Radiation Service coming online in 2016, this changed as the services are provided under the (not yet fully implemented) European Union stand point that data and services produced with public funding shall be provided on free and open grounds....
After 2.5 years of development, version 1.0 of the mongolite package has been released to CRAN. The package is now stable, well documented, and will soon be submitted for peer review to be onboarded in the rOpenSci suite. 🔗 MongoDB in R and mongolite I started working on mongolite in September 2014, and it was first announced at the rOpenSci unconf 2015. At this time, there were already two Mongo clients on CRAN: rmongodb (no longer works) and RMongo (depends on Java)....
I’ve worked for over 12 years in hydrology and natural hazard modelling and one of the things that still fascinates me is the variety of factors that come into play in trying to predict phenomena such as river floods. From local observations of meteorological and hydrological variables and their spatio-temporal patterns to the type and condition of soils and vegetation/land use as well as the geometry and state of river channels and engineering structures affecting the flow....
I’ve recently released the new package ccafs, which provides access to data from Climate Change, Agriculture and Food Security (CCAFS; http://ccafs-climate.org/) General Circulation Models (GCM) data. GCM’s are a particular type of climate model, used for weather forecasting, and climate change forecasting - read more at https://en.wikipedia.org/wiki/General_circulation_model. ccafs falls in the data client camp - its focus is on getting users data - many rOpenSci packages fall into this area. These kinds of packages are important so that scientists don’t have to recreate the wheel themselves every time, but instead use one client that everyone else uses....
Happy rOpenSci users can be found at