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High-energy-density (HED) physics is adopting high-repetition-rate (HRR) operations. A new diagnostic-based data management framework using MongoDB enhances archival efficiency for HRR HED science.

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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.