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<p>Earth and environmental research (E&amp;E) involves managing complex, large-scale data streams that require robust processing, thorough documentation, and secure long-term preservation. The O2A (Observations to Analysis and Archives) Data Flow Framework presents a modular solution linking raw observations to standardized workflows and archival systems, facilitating the transformation of diverse sensor outputs into curated and accessible datasets. Its integrated modules enable efficient instrument metadata management, automated data ingestion, near real-time monitoring, collaborative analysis, and FAIR-compliant data publication. The framework has been successfully implemented in prominent initiatives such as MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) and the German Marine Research Alliance (DAM), providing a scalable and interoperable foundation for contemporary data management and promoting reproducibility as well as cross-institutional collaboration.</p>

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The O2A data flow framework: from observations to analysis and archives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Roland Koppe,
  • Sonja Hänzelmann,
  • Daniel Damaske,
  • Sebastian Immoor,
  • Janine Felden,
  • Angela Schäfer,
  • Gauvain Wiemer,
  • Frank Oliver Glöckner

摘要

Earth and environmental research (E&E) involves managing complex, large-scale data streams that require robust processing, thorough documentation, and secure long-term preservation. The O2A (Observations to Analysis and Archives) Data Flow Framework presents a modular solution linking raw observations to standardized workflows and archival systems, facilitating the transformation of diverse sensor outputs into curated and accessible datasets. Its integrated modules enable efficient instrument metadata management, automated data ingestion, near real-time monitoring, collaborative analysis, and FAIR-compliant data publication. The framework has been successfully implemented in prominent initiatives such as MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) and the German Marine Research Alliance (DAM), providing a scalable and interoperable foundation for contemporary data management and promoting reproducibility as well as cross-institutional collaboration.