Working together to push science forward
Happy rOpenSci users can be found at
Do you have code that accompanies a research project or manuscript? How do you review and archive that code before you submit a paper? Our next Community Call will present different perspectives on this hot topic, with plenty of time for Q&A. What’s the culture of the group around feedback and code collaboration? What are the use cases? What are some practices that can adopted? 🕘 Tuesday, October 16th, 9-10 AM PDT (find your timezone)...
Background Surveys are ubiquitous in the social sciences, and the best of them are meticulously planned out. Statisticians often decide on a sample size based on a theoretical design, and then proceed to inflate this number to account for “sample losses”. This ensures that the desired sample size is achieved, even in the presence of non-response. Factors that reduce the pool of interviews include participant refusals, inability to contact respondents, deaths, and frame inaccuracies....
Hundreds of thousands of people in east Africa have been displaced and hundreds have died as a result of torrential rains which ended a drought but saturated soils and engorged rivers, resulting in extreme flooding in 2018. This post will explore these events using the R package smapr, which provides access to global satellite-derived soil moisture data collected by the NASA Soil Moisture Active-Passive (SMAP) mission and abstracts away some of the complexity associated with finding, acquiring, and working with the HDF5 files that contain the observations (shout out to Laura DeCicco and Marco Sciaini for reviewing smapr, and Noam Ross for editing in the rOpenSci onboarding process)....
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research.
...Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out. In the vaccine space (where I work) we analyze collections of high-dimensional immunological data sets from a variety of different technologies (RNA sequencing, cytometry, multiplexed antibody binding, and others)....
Happy rOpenSci users can be found at