Last Thursday we held a Community Call discussing how to set up a Package to Foster a Community. This call included speakers Maëlle Salmon, Hugo Gruson and Steffi LaZerte, and was moderated by Stefanie Butland. Summarizing the community call Scientific software development - and with that R packages - is a community effort.
A week ago we held a Community Call discussing rOpenSci Statistical Software Testing and Peer Review.This call included speakers Noam Ross, Mark Padgham, Anna Krystalli, Alex Hayes, and John Sakaluk.
rOpenSci community calls are online events with the fundamental goal of strengthening our community.
Webinars and community calls are a great way to gather many people to discuss a specific topic, without the logistic hurdles of in-person events. But whether online or in-person, to reach the broadest audience, all events should work towards greater accessibility.
In March we held a Community Call discussing the maintenance of R packages.This call included a starting presentation by Julia Silge followed by a discussion featuring panelists with a wide variety of backgrounds: Elin Waring, Erin Grand, Leonardo Collado-Torres and Scott Chamberlain.
For this Community Call, we’re trying something different. We’ll start with a short talk by Julia Silge , then spend most of the time on Q & A with four panelists - Elin Waring , Erin Grand , Leonardo Collado-Torres , and Scott Chamberlain - moderated by Julia. Our panelists bring a wide range of perspectives so there’s something for everyone.
To the uninitiated, software testing may seem variously boring, daunting or bogged down in obscure terminology. However, it has the potential to be enormously useful for people developing software at any level of expertise, and can often be put into practice with relatively little effort. Our 1-hour Call will include two speakers and at least 20 minutes for Q &
Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package.
Our 1-hour Call on Reproducible Research with R will include three speakers and 20 minutes for Q & A. Ben Marwick will introduce you to a research compendium, which accompanies, enhances, or is a scientific publication providing data, code, and documentation for reproducing a scientific workflow.
rOpenSci’s community is increasingly international and multilingual. While we have operated primarily in English, we now receive submissions of packages from authors whose primary language is not. As we expand our community in this way, we want to learn from the experience of other organizations. How can we manage our peer-review process and open-source projects to be welcoming to non-native English speakers?
