Quarterly OKR template

	A	B	C	D
1	Attribute	Text	Key words and phrases	Notes

2	Name	Digital Innovations for Agriculture Group (DIAG)	digital, innovation, agriculture
3	Tagline	Using data to transform agriculture	data, transform, agriculture
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5	Vision	Our software will be used and collaboratively developed by researchers at major land grant universities, global ag research centers (e.g. CG), and industry. We will enable scientists to spend less time engineering bespoke pipelines and collecting redundant data so that they can spend more time developing algorithms, augmenting existing data with strategic data collection, and analyzing data.	vision, software, collaboratively, universities, research centers, industry, enable, effective research
6	Mission	The mission of the UA ag data group is to provide scientists and engineers with open software, data, and computing that will allow more efficient discovery and invention so that we can engineer crops and manage sustainable agricultural landscapes that produce food, energy, and ecosystem services.	mission, agriculture, data, group, science, engineering, open, software, data, computing, efficient, discovery, invention, crops, sustainable, produce, food, ecosystem
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8	We do: Data Curation	We build software and processes to support data curation and harmonization. We have extensive experience with combining existing databases and extracting results and metadata from publications. We help scientists organize and publish data: from writing a data management plan to publishing data to developing custom databases. We work with CyVerse, the UA libraries, and other data repositories and can help you find the best way to share and get credit for your data.	build software, build processes, support, data curation, data harmonization, experience, combine databases, extract results, extract metadata, help, organize, publish, data management plan, custom databases, CyVerse, UA, data repositories, share, credit
9	We do: Custom Pipelines	We have experience developing custom data processing and analysis pipelines. Our projects include aggregation and processing of weather data, using crop and ecosystem simulation models for inference and prediction, analysis of remote sensing data and more.	experience, custom, data processing, data analysis, pipelines, data aggregation, weather data, crop models, ecosystem simulation models, inference, prediction, remote sensing data
10	We do: 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 are also familiar with common analyses used in modeling - including sensitivity analysis, uncertainty propagation, forecasting, data assimilation, and assessing model skill.	various models, crop monocultures, prediction, crop yield, carbon balance, water balance, understanding plant communities, biogeochemical cycling, help, parameterize, calibrate, improve, run models, Ecosystem Demography Model, BioCro, Sipnet, common analyses, sensitivity analysis, uncertaintly propagation, forecasting, data assimilation, assessing model skill
11	We do: Training	Much of what we do, we teach. Most of our lessons are centered on agricultural systems and they range from data science basics to remote sensing and crop and ecosystem modeling.	do, teach, lessons, agricultural systems, data science basics, remote sensing, crop modeling, ecosystem modeling
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