Projects

In collaboration with the National Phenology Network, we are creating an analytical pipeline to estimate spatial trends in the time to reach different thresholds of growing degree days in the Northeastern USA.

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.

In collaboration with Margaret Evans's lab and colleagues at the US Forest Service, we are building a suite of tools to facilitate access to the USFS Forest Inventory and Analysis dataset in timeseries form.

Our group is working with colleagues at Indiana University and worldwide to build a global database of plant water potential measurements through the PSInet RCN project.

In a continuation of our Incubator work with Laura Meredith, and with additional support from the R Consortium, we built and released an R package for calculating estimated relative volatility values of organic compounds.

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".

We joined a cross-institutional collaboration to convert pre-existing agricultural datasets to a standardized format and create an Agricultural Research Data Network, to improve the findability and resuability of agricultural data.

Our group built a high-throughput sensor data processing pipeline, including data from drones and other platforms, for agricultural research and beyond.

We worked with a large network of collaborators to help build the world's largest public domain agricultural dataset to facilitate combining remote sensing, phenomic, genomic, and weather data.

We joined a large, multi-institutional collaboration to build accessible tools for running ecosystem models, with applications including models of genetically modified crops and bioenergy crops.

As part of the SENTINEL: SENsing Threats In Natural Environments project, our group conducted biophysical and ecological modeling to predict the functioning, ecological impact, and physiology of plants that have been genetically modified to act as sensors to detect chemical and biological threats.