ATLAS
Description
Literature review and data compilation is the most time-consuming phase of the mineral resource assessment workflow (https://www.usgs.gov/media/images/usgs-mineral-resource-assessment-workflow). Identifying and extracting datasets referenced in historical documents, journal articles and published databases is currently done using traditional methods of browsing the web, downloading manuscripts and datasets, and extracting relevant data. The AI system will automatically extract metadata about datasets from manuscripts and make it available to users in a catalog. By supporting user queries of the extracted metadata, the system will enable assessment scientists to identify data sources much more quickly. The tool is intended to support other AI tools down the line that facilitate data extraction and synthesis. The system will also be useful for tracking lineage and usage of published datasets, helping to assure data quality and assess impact.
Detailed example
A catalog of metadata about published datasets that supports user queries through agentic AI.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The AI will accelerate the mineral resource assessment process and provide a model for other types of resource assessments. By improving efficiency, accuracy, and transparency of assessment workflows, AI tools can help USGS deliver results more rapidly
Controls / human review
ATO: No; PIA: Not published