What We Do
Data Management and Discovery
Data Management: We help scientists organize and publish data: from writing a data management plan to sharing data in archives as well as custom databases and APIs. We work with the Data Science Institute, the UA libraries, CyVerse, and other data repositories and can help you find the best way to share and get credit for your data; we also work with communities working on standardizing data formats, vocabularies, and protocols to make it easier to create and use FAIR data.
Data Discovery and Use: We also help users find and use available data from public databases, prior publications, and old spreadsheets. We have experience with data synthesis methods including meta-analysis and model-data fusion.
Analysis and Visualization: We can help with statistical analysis, visualization to understand and communicate your research. We can help you with experimental design and analysis including advanced statistical and computing methods, and we can help design figures and dashboards to facilitate interpretation and communication of results.
Pipelines: We have experience developing custom data processing and analysis pipelines for data, models, and reports. We can help speed up your analysis and scale it up using high performance computers at UArizona and beyond. We can also help make your workflows more automated and reproducible.
Crop & Ecosystem Simulation Models
We have a variety of models appropriate for different applications, from predicting the yield, carbon, and water balance of crop monocultures to understanding plant communities and biogeochemical cycling. We can help parameterize, calibrate, improve and run models including the Ecosystem Demography Model, BioCro, Sipnet, and more. We have also applied and built software (pecanproject.org) to automate common analyses used in modeling - including sensitivity analysis, uncertainty propagation, forecasting, data assimilation, and assessing model skill.
Training and Workshops
We teach the skills that we have learned. A core part of our vision is to elevate scientific computing skills of our institution and collaborators while working with others as well as through workshops. Most of our lessons are centered on agricultural and ecosystem sciences and range from foundations of data science to advanced statistical methods and more.