The operations and projects of the Data Sciences Services (DSS) trace back to the 1930's, when the Master Sample of Agriculture was jointly developed by USDA, the U.S. Census Bureau, and the Survey Section of ISU's Statistical Laboratory. The Master Sample of Agriculture is a national area sample of land, which has featured in numerous surveys of American agriculture. It marks the beginning of the USDA-CSSM collaboration that has lasted over eight decades and is going on strong.

Today, DSS focuses on providing a comprehensive suite of statistical, Geographic Information System (GIS), and software services to the legislatively mandated National Resources Inventory (NRI), a longitudinal survey designed to assess conditions and trends for soil, water, and related natural resources on non-federal lands in the United States. 

DSS is comprised of the following groups: Data Analytics, GIS, IT Services, Research and Estimation, Software Development.

A testament to the diverse technical background of its team, DSS provides services in web and mobile device app development, secure data storage, user and access management, and advanced analytics relevant to big data and machine learning. DSS current clients include U.S. federal agencies such as the Department of Agriculture (USDA), the Department of the Interior (DOI), the Department of Justice (DOJ), state agencies such as Iowa Department of Natural Resources (IA-DNR), and non-profit organizations such as Iowa Monarch Conservation Consortium.

See DSS work on the National Resources Inventory

Our Services

Agricultural, conservational, and environmental survey data collection

• Computer-assisted survey information collection systems (CASI)
• Remote sensing and photo-interpretation
• Onsite imaging and location services
• Progress tracking and monitoring
• Quality Assurance

• Secure data storage and management

   

Area frame construction and sample selection

• Stratified two-stage area sample
• Primary sample units (PSU) and secondary sampling units (SSU)
• The Public Land Survey System (PLSS)
• Geographic sampling

Data integration and processing

• Ancillary data from open-source public data
• Administrative records

• GIS technologies

   

Statistical analysis

• Advanced data visualization
• Missing data methods
• Small area estimation
• Level and change estimates
• Weighting and modeling
• Uncertainty assessment