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

Transforming diverse data from design, production, and operations into insights is essential in supporting better decisions and more adaptive systems across a manufacturing lifecycle. AI/ML models can connect data streams, identify patterns, extract signals, and automate analyses across complex, multimodal data workflows.

Cycle 2 Ideathon Requested Datasets

Sensor & Time Series Data

• Industrial and manufacturing process monitoring: Manufacturing/SCADA time-series, automated testing logs, and experiment data for tracking operations, quality, and anomalies.
• Critical infrastructure and structural health monitoring: Sensor streams from bridges, pipelines, and other infrastructure, including strain, pressure, temperature, and load-response data.
• Energy and nuclear system monitoring: Nuclear cooling-system strain/temperature cycles and pipeline condition data for detecting leaks, corrosion, fatigue, or abnormal behavior.
• General environmental and sensor-stream analytics: Vibration, flow, humidity, agriculture-environment data, and other continuous sensor streams used for monitoring, prediction, and diagnostics.

Imaging & Visual Data

• Quality-control photographs (boards, solder bonds).
• X-ray inspection images.
• Electron-microscopy (TEM/SEM) microstructure image sets.
• In-situ AM machine images/time series.

Materials Testing & Qualification Data

• Non-destructive evaluation (NDE) measurements.
• Long-term creep and fatigue test records.
• Irradiation-behavior data for nuclear materials.
• Corrosion performance datasets in varied environments.

Simulation & Synthetic Data

• Physics-based synthetic surrogate datasets (modeled leaks, noise).
• Digital-twin simulation inputs (scaled-facility sensor streams).

Documentation & Knowledge Management

• Legacy scanned R&D, operational logs, regulatory documents.
• Fuels & materials properties handbooks (e.g. INL GIV materials properties handbook).
• Industry-specific standards (e.g. NQA-1).
• Domain ontologies and knowledge-graph definitions for specialized expertise.

Operations & Supply-Chain Data

• Supply-chain and raw-material traceability records.
• Power-distribution and microgrid resilience data.

Cybersecurity & Anomaly Detection Data for AM

• Cybersecurity logs, anomaly-detection datasets.
• Adversarial-ML perturbation datasets.

Model Validation & Benchmarking Data

• AI/ML benchmarking test results.
• Model audit-trail and validation records.

Data Formats, Ingestion Tools, & Best Practices

• Master data catalog or directory metadata (dataset/SME index).
• AI-ready formatted datasets (e.g. JSON, structured tables).
• Helper-function code snippets for data ingest.
• Agentic AI with tool-calling to process scanned data into useful formats.

Transforming diverse data across the energy lifecycle into actionable insights is essential for supporting better decisions, improving resilience, and enabling more adaptive energy systems. AI/ML models can connect data streams across energy access, conversion, packaging, distribution, transportation, storage, end use, and end-of-life processes, helping identify patterns, extract signals, optimize operations, and automate analyses across complex, interconnected energy workflows.

Cycle 3 Ideathon Requested Datasets

Grid Operational Data

• Real utility grid topology and operational data, including distribution and transmission system data.
• SCADA, industrial control system, substation, and control-system telemetry data.
• Aggregated customer load data at varying and useful levels of abstraction.
• Dynamic datasets that reflect changing grid conditions.

Grid Planning Data

• Capacity expansion planning datasets from utilities or ISOs.
• Real-world planning constraints, including permitting, political, and NEPA/environmental review constraints.
• Generation and transmission siting constraints.
• Data identifying where new generation or transmission can realistically be added.

Grid Optimization Data

• Curtailment forecasts.
• Utility outage or generation interconnection information.
• OASIS-type data in a more AI-friendly format.
• Data on new generation coming online and impacts of nearby nuclear or gas generation on renewable facility output.
• Grid usage and constraint data affecting generation dispatch.

Synthetic/Augmented Cybersecurity Datasets

• Cyberattack, fault, and anomaly scenarios for grid systems.
• Hardware-in-the-loop testbed and national lab test-range data.
• Synthetic data fused with real utility baseline data.
• Anomaly detection, mitigation, resilience, and forensics data.
• Secure, non-poisoned datasets for training cyber models.

Image, Thermal, and Sensor Data for Asset Inspection

• Thermal imagery of solar assets, RGB imagery of solar equipment and labeled images of equipment faults.
• Data for counting or identifying wires, fuse blocks, combiner boxes, and other components.
• Image datasets suitable for segmentation and retraining vision models.
• Sensor streams paired with ground-truth equipment conditions.

Building, HVAC, and Data Center Operations Data

• HVAC sensor, building energy consumption data, chiller and cooling tower performance data.
• Temperature set points, flow points, valve operations.
• Data center energy and water usage and data for digital twins of data centers.
• Failure-mode data for building systems and data center cooling infrastructure.

Oil, Gas, Methane, and Flaring Data

• Methane and multi-gas sensor data.
• Flaring-sector and flame-out precursor data.
• Atmospheric/environmental data related to methane and flaring.
• Oil and gas delivery/infrastructure monitoring data.

Seismic and Subsurface Data

• Seismic data and reservoir characterization data.
• Subsurface geomechanics/geochemistry data and complex material characterization data.
• Time-resolved subsurface datasets.
• Data for interpreting spatially and temporally variable rock properties.

Battery & Energy Storage Data

• Battery health data, including early indicators of problematic batteries.
• Battery testing data and storage integration data.
• Data for short- and long-term storage behavior.
• Data supporting renewable-storage integration.

Energy-Water Nexus Data

• Water usage data for data centers and water use associated with electricity generation.
• Sustainability, resiliency, and energy-water tradeoff data.
• Data for comparing energy sources such as solar, natural gas, and small modular reactors.
• Environmental impact data.

Microgrid and Wildfire-Related Data

• Microgrid generation and connectivity data.
• Renewable generation data and storage-market integration data.
• Wildfire prediction and preparation data.
• Data supporting microgrid software and agentic AI modules.

Smart Community, IoT, Edge, and Human/Health Performance Data

• Smart home, in-home monitoring and smart community sensor data.
• IoT and edge-computing data.
• Stadium/sports-fan testbed data.
• Human health and performance data, with consent.
• Gas leak triangulation data for smart communities.