Identifying and analyzing changes in an environment (e.g., production processes, physical systems, components, battlefield, etc.) are essential to predicting their outcomes and addressing them quickly. AI/ML models can integrate sensor inputs from infrastructure and technology, to track changes over time, detect potential weaknesses or issues, and dynamically adjust operations.

Cycle 1 Ideathon Requested Datasets

Cybersecurity and Anomaly Detection

• High-fidelity simulation data generated with domain subject matter experts (SMEs).
• Data related to cybersecurity, IoT, and sensor-based datasets.
• Datasets that address anomalies, including rationale for labeling to understand data principles.

Energy and the Grid

• Weather Data: Needed for seasonal forecasting in energy management.
• Emissions Signatures: Data on vehicle emissions for preventative health monitoring.
• Degradation Data: Information on the degradation of fabricated solar cells exposed to UV radiation.

Manufacturing and Supply Chain Data

• Corrosion Data: Physical inspection data, including thermal inspection for assessing corrosion impact.
• Composite Materials Data: Data from sensors used to detect subsurface delamination in heated areas.
• Device Fabrication Data: Information on the processes and materials used in device manufacturing.
• Supply Chain Data: Data concerning materials used in supply chains.

Data Management and Automation

• Automation of Labeling and Annotation: Interest in automating the time-consuming process of labeling datasets for SMEs.

Human-AI Teaming

• Datasets on how humans interact with AI tools
• Datasets on human decision making with AI tools
• Datasets on how AI interacts with and learns from human users

Physical security data