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<p>Global Navigation Satellite System (GNSS) data, while widely accessible, often lacks the necessary structure and metadata for effective reuse, particularly for data-driven research based on machine learning. To address these limitations, we applied the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to the GNSS RINEX observation files hosted in the EUREF Historical Data Center (EUREF-HDC). This was achieved by developing the GNSS-DCAT-AP metadata schema, assigning persistent identifiers (PIDs), which include Digital Object Identifiers (DOIs), to the GNSS datasets, and implementing web services that enable both humans and machines to search, retrieve, and download GNSS data and metadata. The effectiveness of this approach was evaluated using the FAIRsFAIR Data Object Assessment Metrics, which illustrates a significant improvement in FAIR compliance of the GNSS data in the EUREF-HDC. This work demonstrates the feasibility of turning GNSS RINEX data into FAIR Digital Objects and offers a practical roadmap for other geospatial data repositories aiming to align with the FAIR principles<i>.</i></p>

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Enhancing FAIRness of GNSS RINEX data: A metadata-driven approach

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Anna Miglio,
  • András Fábián,
  • Juliette Legrand,
  • Carine Bruyninx,
  • Eric Pottiaux,
  • Fikri Bamahry,
  • Stefanie De Bodt,
  • Paula Oset García

摘要

Global Navigation Satellite System (GNSS) data, while widely accessible, often lacks the necessary structure and metadata for effective reuse, particularly for data-driven research based on machine learning. To address these limitations, we applied the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to the GNSS RINEX observation files hosted in the EUREF Historical Data Center (EUREF-HDC). This was achieved by developing the GNSS-DCAT-AP metadata schema, assigning persistent identifiers (PIDs), which include Digital Object Identifiers (DOIs), to the GNSS datasets, and implementing web services that enable both humans and machines to search, retrieve, and download GNSS data and metadata. The effectiveness of this approach was evaluated using the FAIRsFAIR Data Object Assessment Metrics, which illustrates a significant improvement in FAIR compliance of the GNSS data in the EUREF-HDC. This work demonstrates the feasibility of turning GNSS RINEX data into FAIR Digital Objects and offers a practical roadmap for other geospatial data repositories aiming to align with the FAIR principles.