It all started January 26th this year when I signed up to volunteer as a reviewer for R packages submitted to rOpenSci. My main motivation for wanting to volunteer was to learn something new and to contribute to the R open source community. If you are wondering why the people behind rOpenSci are doing this, you can read How rOpenSci uses Code Review to Promote Reproducible Science. Three months later I was contacted by Maëlle Salmon asking whether I was interested in reviewing the R package patentsview for rOpenSci.

At rOpenSci, we create and curate software to help scientists with the data life cycle. These tools access, download, manage, and archive scientific data in open, reproducible ways. Early on, we realized this could only be a community effort. The variety of scientific data and workflows could only be tackled by drawing on contributions of scientists with field-specific expertise. With the community approach came challenges. How could we ensure the quality of code written by scientists without formal training in software development practices?

Are you thinking about submitting a package to rOpenSci’s open peer software review? Considering volunteering to review for the first time? Maybe you’re an experienced package author or reviewer and have ideas about how we can improve. Join our Community Call on Wednesday, September 13th. We want to get your feedback and we’d love to answer your questions! Agenda Welcome (Stefanie Butland, rOpenSci Community Manager, 5 min) guest: Noam Ross, editor (15 min) Noam will give an overview of the rOpenSci software review and onboarding, highlighting the role editors play and how decisions are made about policies and changes to the process.

As you might remember from my blog post about ropenaq, I work as a data manager and statistician for an epidemiology project called CHAI for Cardio-vascular health effects of air pollution in Telangana, India. One of our interests in CHAI is determining exposure, and sources of exposure, to PM2.5 which are very small particles in the air that have diverse adverse health effects. You can find more details about CHAI in our recently published protocol paper.

Take a look at the data This is a phrase that comes up when you first get a dataset. It is also ambiguous. Does it mean to do some exploratory modelling? Or make some histograms, scatterplots, and boxplots? Is it both? Starting down either path, you often encounter the non-trivial growing pains of working with a new dataset. The mix ups of data types - height in cm coded as a factor, categories are numerics with decimals, strings are datetimes, and somehow datetime is one long number.