As sensor data becomes more available and our ability to store and move data increases, our need to manage and extract insights from data increases.

We are turning data into insights – even unassisted action – through artificial intelligence, visualization, and virtual models of physical, biological, chemical and social phenomena.  But collecting, moving, managing, visualizing, analyzing, and interpreting data from disparate systems has significant technical as well as social challenges.

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Current Posts

Extension UAV Initiative Update

The Purdue University Cooperative Extension Service UAV (Unmanned Aerial Vehicle, aka drones) Initiative started in 2017 with Specialists conducting field-level research and expanded in 2018 with funding support from Dr. Jason Henderson to acquire UAVs for Educators across the state. The initial group was comprised of 17 Educators and has since grown to over 20…


Key Terms


Mathematical procedure for estimating unknown values from neighboring known data.

Data Wrangling

The process of acquiring data from multiple sources, cleaning the data (removing/replacing missing/redundant data), combining the data to acquire only required fields and entries, and preparing the data for easy access and analysis.

Digital agriculture

The realm in which our physical and social world is fused through digital devices. Integrated characterization and modeling improve decision making using modern data-intensive technologies that collect, connect, curate, communicate, and compute.

Data Logger

Used to store electronic data sent by a measurement device. A yield monitor is an example of a data logging device.