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As announced in February, we now have an online book containing all things related to rOpenSci software review. Our goal is to update it approximately quarterly - it’s time to present the third version. You can read the changelog or this blog post to find out what’s new in our dev guide 0.3.0! 🔗 Updates to our policies and guidance 🔗 Scope We’ve introduced an important change for anyone thinking of submitting a package....
Image processing is one of the core focus areas of rOpenSci. Over the last few months we have released several major upgrades to core packages in our imaging suite, including magick, tesseract, and av. This post highlights a few cool new features. 🔗 Magick 2.2 The magick package is one of the most powerful packages for image processing in R. It interfaces to the ImageMagick C++ API and can takes advantage of several other R packages providing imaging functionality in R....
citecorp is a new (hit CRAN in late August) R package for working with data from the OpenCitations Corpus (OCC). OpenCitations, run by David Shotton and Silvio Peroni, houses the OCC, an open repository of scholarly citation data under the very open CC0 license. The I4OC (Initiative for Open Citations) is a collaboration between many parties, with the aim of promoting “unrestricted availability of scholarly citation data”. Citation data is available through Crossref, and available in R via our packages rcrossref, fulltext and crminer....
The UCSC Xena platform provides an unprecedented resource for public omics data from big projects like The Cancer Genome Atlas (TCGA), however, it is hard for users to incorporate multiple datasets or data types, integrate the selected data with popular analysis tools or homebrewed code, and reproduce analysis procedures. To address this issue, we developed an R package UCSCXenaTools for enabling data retrieval, analysis integration and reproducible research for omics data from the UCSC Xena platform1....
🔗 Teaching collaborative software development In the University of British Columbia’s Master of Data Science program one of the courses we teach is called Collaborative Software Development, DSCI 524. In this course we focus on teaching how to exploit practices from collaborative software development techniques in data scientific workflows. This includes appropriate use of the software life cycle, unit testing and continuous integration, as well as packaging code for use by others....
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