A customizable data management framework for high-repetition-rate high-energy-density science.

Area of Science:

• High-Energy-Density (HED) Physics
• Computational Physics
• Data Science

Background:

• The high-energy-density (HED) physics community is transitioning to high-repetition-rate (HRR) operational paradigms.
• Effective data management, including synchronization and real-time archival, is crucial for leveraging HRR HED facilities.
• Current data organization methods may hinder the efficiency required for advanced HED research.

Purpose of the Study:

• To develop a generalized database framework for managing data from HRR HED facilities.
• To implement a novel data organizational strategy for improved archival and retrieval efficiency.
• To lay the groundwork for machine-actionable data solutions and community-driven data standards in HED science.

Main Methods:

• Development of a generalized NoSQL database framework, specifically the MongoDB repository for information and archiving.
• Implementation of a data organizational strategy shifting from a shot-based to a diagnostic-based approach.
• Focus on real-time data tagging, synchronization, and robust archival of subsystem components (laser, targetry, diagnostics).

Main Results:

• Establishment of the MongoDB repository as a foundational element for HRR HED data management.
• Demonstration of increased archival and retrieval efficiency through the diagnostic-based data organization.
• Successful integration of synchronized subsystems and real-time data handling.

Conclusions:

• The developed framework and organizational strategy represent a significant step toward efficient data management in HRR HED science.
• The diagnostic-based approach enhances data accessibility and supports optimization applications.
• This work encourages community engagement to define essential data standards for the future of HED research.