A case study explaining how a predictive, data-driven machine-learning model was developed to detect unauthorized cash benefit withdrawals more quickly and accurately in California.
A statewide framework to improve data literacy among Oregon public sector employees by identifying core competencies, learning goals, and implementation strategies across various roles and skill levels.
To assist states in closing digital skill gaps and preparing for digital equity planning, this brief offers key questions and resources for state leaders to consider.
This report analyzes the rise of digital driver’s licenses (DDLs) and warns that, without strong safeguards, they could threaten privacy, civil liberties, and equitable access to identification.
This case study explores how the City of Akron developed a community tree map to engage residents in urban forestry efforts, enabling them to identify, grow, and care for trees in their neighborhoods.
This playbook shares best practices and innovations related to data and analytical approaches for improving grant outcomes. Equitable data practices improve the effectiveness and efficiency of federal program dollars through better resource allocation and more informed decision-making.
The report highlights that many eligible low-income children are not receiving WIC benefits during the COVID-19 pandemic, with participation rates varying significantly by state and lagging behind programs like Medicaid and SNAP.
This toolkit is designed to assist state and local TANF agencies in accessing, linking, and analyzing employment data from unemployment insurance (UI) systems.
