Dr. Julia Stewart Lowndes [@juliesquid on Twitter] is the Science Program Lead for the Ocean Health Index and works at the National Center for Ecological Analysis and Synthesis. She and Sean Kross discussed how data science, open science, and community can help reproducibility in research. [This interview occurred at the 2017 rOpenSci unconference] SK: I’m Sean Kross, I’m the CTO of the Johns Hopkins Data Science Lab.

For a fifth year running, we are excited to announce the rOpenSci unconference, our annual event loosely modeled on Foo Camp. rOpenSci unconferences have a rich history. You can get a feel for them by reading collected stories about people and projects from unconf17. We’re organizing unconf18 to bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profits to get together for a couple of days to hack on various projects and generally enrich our community.

The drake R package is a pipeline toolkit. It manages data science workflows, saves time, and adds more confidence to reproducibility. I hope it will impact the landscapes of reproducible research and high-performance computing, but I originally created it for different reasons. This post is the prequel to drake‘s inception. There was struggle, and drake was the answer. Dissertation frustration Sisyphus My dissertation project was intense. The final computational challenge was to analyze multiple genomics datasets using an emerging method and its competitors.

We’re very pleased to be introducing someone who needs no introduction in the R community. Join us in welcoming Maëlle Salmon to rOpenSci as a Research Software Engineer (part time, working from Nancy, France). We’d like to formally introduce her here and share a bit about the kinds of things she’ll be working on. Maëlle did a B.Sc. in Biology with an emphasis on maths and quantitative work, two Masters degrees - one in Ecology and one in Public Health - and a Ph.

One of the best things about learning R is that no matter your skill level, there is always someone who can benefit from your experience. Topics in R ranging from complicated machine learning approaches to calculating a mean all find their relevant audiences. This is particularly true when writing R packages. With an ever evolving R package development landscape (R, GitHub, external data, CRAN, continuous integration, users), there is a strong possibility that you will be taken into regions of the R world that you never knew existed.