All posts (Page 73 of 122)
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). These data often arise from clinical trials and can involve tens to hundreds of subjects. The data are analyzed by teams of researchers with a diverse variety of goals.
...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? Read on if you want to learn how to use R packages allowing to do so!...
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....
You might have read my blog post analyzing the social weather of rOpenSci onboarding, based on a text analysis of GitHub issues. I extracted text out of Markdown-formatted threads with regular expressions. I basically hammered away at the issues using tools I was familiar with until it worked! Now I know there’s a much better and cleaner way, that I’ll present in this note. Read on if you want to extract insights about text, code, links, etc. from R Markdown reports, Hugo website sources, GitHub issues… without writing messy and smelly code!...
