The data-powered revolution in agriculture is well underway, and the College of Agriculture at Purdue is driving many innovations that advance our ability to collect research data across a variety of interrelated disciplines.
However, these advances in data collection are outpacing our ability to manage, share, and analyze the mountains of data we’re collecting every day.
The era of big data and data science is bringing new solutions, but also calls for specialized expertise and requires bringing together a diverse set of skills from different teams, making it challenging for faculty to navigate.
Agriculture Research and Graduate Education and AgIT have teamed up to address these challenges by establishing a new Ag Data Services team (ADS).
Ag Data Services Mission
The Ag Data Services team exists to connect researchers across the college with infrastructure, expertise, and collaborators, enhancing their ability to harness data to accelerate discovery and outreach.
The ADS team has a dedicated focus on ag research data, providing the following core services:
Data Strategy and Consultation: help faculty researchers develop data strategies, and identify all the components and providers necessary to map out an end-to-end data flow
Data Pipeline Implementation: work directly with researchers and their teams to implement end-to-end data pipelines, taking advantage of existing resources and infrastructure wherever possible, and extending as needed to address gaps
Data Management: perform data management as a service to help address the need for a more dedicated focus on getting data stored and organized in ways that support future analysis needs
Data Analytics: enable researchers take advantage of advances in big data and data science by helping them apply those methods to their research data, connecting them with other experts and specialists as needed
Training and Education: work together with other providers to organize and deliver training and workshops to help faculty researchers and their grad students leverage the data collection infrastructure, establish effective data management practices, and apply new methods for analysis