Working together to push science forward
Happy rOpenSci users can be found at
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....
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....
Happy rOpenSci users can be found at