Reproducible Reports

As a continuation of our Incubator project with Jeremy Weiss, we released a new version of the {azmetr} R package, which facilitates accessing AZMet data and automated QA/QC checks to alert managers to data issues.

We are working with David More and Yang Li of the School of Natural Resources & the Environment to build an analytical pipeline characterizing uncertainty in estimates of above-ground biomass, to help stakeholders make informed decisions about which data products to use for their needs.

We created a QA/QC dashboard to quickly spot potential errors in weather data collected by AZMet

Our group curated phenotypic and meteorological data for use as part of the NSF Ideas Lab Project "Converging Genomics, Phenomics, And Environments Using Interpretable Machine Learning Models".