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.

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.

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. drake resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and organizes the output so you rarely have to think about data files.

Are you passionate about statistical methods and software? If so we would love for you to join our team to dig deep into the world of statistical software packages. You’ll develop standards for evaluating and reviewing statistical tools, publish, and work closely with an international team of experts to set up a new software review system. We are seeking a creative, dedicated, and collaborative software research scientist to support a two-year project in launching a new software peer-review initiative.

The grainchanger package provides functionality for data aggregation to a coarser resolution via moving-window or direct methods. Why do we need new methods for data aggregation? As landscape ecologists and macroecologists, we often need to aggregate data in order to harmonise datasets. In doing so, we often lose a lot of information about the spatial structure and environmental heterogeneity of data measured at finer resolution. The issues around scale disconnects are both conceptual and practical: