A Smart Data Hub to integrate subsurface properties into model-based decision-support tools.

Area of Science:

• Geosciences
• Environmental Science
• Data Science

Background:

• Nuclear waste disposal site selection is complex, requiring extensive technical and environmental assessments.
• Existing simulation models lack credible, reproducible data foundations, hindering decision support.
• Current research often lacks datasets with uncertainty information and practical application methods.

Purpose of the Study:

• To introduce a Smart Data Hub addressing limitations in current nuclear waste disposal site selection research.
• To provide a reproducible data foundation for data-integrated simulation models.
• To enhance transparency and usability of data for decision support tools.

Main Methods:

• Compiled a dataset from 50 literature sources on geological information, structural data, and rock properties for German repository sites.
• Developed a functional module for effective data compilation assembly tailored to specific geological structures.
• Integrated uncertainty information into the dataset for enhanced data reliability.

Main Results:

• The Smart Data Hub offers a transparent and uncertainty-informed dataset.
• The functional module enables intelligent assembly of data compilations for specific use cases.
• The solution addresses the need for reproducible data in nuclear waste site selection.

Conclusions:

• The Smart Data Hub provides a reliable, reproducible data compilation for model-based decision support.
• This approach enhances the credibility of simulation models in the nuclear waste disposal site selection process.
• Transparent, uncertainty-informed data coupled with intelligent assembly capabilities are crucial for effective site selection.