rOpenSci Dev Guide 0.3.0: Updates
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.
Using rOpenSci Software Peer Review Guidelines for Teaching
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.
Introducing Open Forensic Science in R
The free online book Open Forensic Science in R was created to foster open science practices in the forensic science community. It is comprised of eight chapters: an introduction and seven chapters covering different areas of forensic science: the validation of DNA interpretation systems, firearms analysis of bullets and casings, latent fingerprints, shoe outsole impressions, trace glass evidence, and decision-making in forensic identification tasks. The chapters of Open Forensic Science in R have the same five sections: Introduction, Data, R Package(s), Drawing Conclusions, and Case Study.
2 Months in 2 Minutes - rOpenSci News, August 2019
rOpenSci HQ rOpenSci received a $678K award from the Sloan Foundation to expand Software Peer Review. We are hiring for a new position in statistical software testing and peer review. Join our next Community Call on Reproducible Workflows at Scale with drake September 24th. Videos, speakers’ slides, resources and collaborative notes from our Community Calls on Involving Multilingual Communities and Reproducible Research with R are posted. Software Peer Review 5 community-contributed packages passed software peer review.
Synthesizing population time-series data from the USA Long Term Ecological Research Network
Introduction The availability of large quantities of freely available data is revolutionizing the world of ecological research. Open data maximizes the opportunities to perform comparative analyses and meta-analyses. Such synthesis efforts will increasingly exploit “population data”, which we define here as time series of population abundance. Such population data plays a central role in testing ecological theory and guiding management decisions. One of the richest sources of open access population data is the USA Long Term Ecological Research (LTER) Network.
