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Hundreds of thousands of people in east Africa have been displaced and hundreds have died as a result of torrential rains which ended a drought but saturated soils and engorged rivers, resulting in extreme flooding in 2018. This post will explore these events using the R package smapr, which provides access to global satellite-derived soil moisture data collected by the NASA Soil Moisture Active-Passive (SMAP) mission and abstracts away some of the complexity associated with finding, acquiring, and working with the HDF5 files that contain the observations (shout out to Laura DeCicco and Marco Sciaini for reviewing smapr, and Noam Ross for editing in the rOpenSci onboarding process)....
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research.
...Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out. In the vaccine space (where I work) we analyze collections of high-dimensional immunological data sets from a variety of different technologies (RNA sequencing, cytometry, multiplexed antibody binding, and others)....
In the second post of the series where we obtained data from eBird we determined what birds were observed in the county of Constance, and we complemented this knowledge with some taxonomic and trait information in the fourth post of the series. Now, we could be curious about the occurrence of these birds in scientific work. In this post, we will query the scientific literature and an open scientific data repository for species names: what have these birds been studied for?...
A while ago we onboarded an exciting package, codemetar by Carl Boettiger. codemetar is an R specific information collector and parser for the CodeMeta project. In particular, codemetar can digest metadata about an R package in order to fill the terms recognized by CodeMeta. This means extracting information from DESCRIPTION but also from e.g. continuous integration1 badges in the README! In this note, we’ll take advantage of codemetar::extract_badges function to explore the diversity of badges worn by the READMEs of CRAN packages....
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