Published December 11, 2023 | Version v1

Applying Community Standards for Domain-Relevant Metadata to Enhance Data Product FAIRness

  • 1. ROR icon Analytical Mechanics Associates (United States)
  • 2. ROR icon National Aeronautics and Space Administration
  • 3. ROR icon Langley Research Center
  • 4. Norwegian Institute for Air Research
  • 5. Lingua Logica
  • 6. ROR icon Adnet Systems (United States)

Description

The reusability of data from airborne field campaigns should be supported by detailed metadata relevant to the measurements reported. The Atmospheric Composition Variable Standard Names Convention (ACVSNC) is a community standard which can provide domain-relevant metadata embedded within the data file, e.g., ICARTT data files. The structured format and controlled vocabulary of the ACVSNC is consistent with FAIR principles, enabling data to be Findable, Interoperable, and Reusable. To further make data findable, the data must be human and machine-actionable. One approach to do this is through the I-ADOPT ontology-based framework, which was designed to “harmonize the way observable properties are named and conceptualized across scientific domains”. The Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS) vocabulary has successfully implemented the I-ADOPT approach. We have mapped the ACVSNC into the ACTRIS vocabulary to demonstrate the compatibility of ACVSNC with an ontology-based framework, which suggests the potential contribution to the United Metadata Model for Variables (UMM-Var) that NASA has developed. Mapping between ACTRIS and the ACVSNC has highlighted the ambiguity and subjectivity in assigning ontology components. For example, many variables within the field of atmospheric composition describe a process, not a physical being, which can be difficult to decompose into components. Mapping the ACVSN to ACTRIS has provided insight to improve the implementation of ontological approaches to make accurate variable metadata more machine-actionable and human-readable to a broad spectrum of data users/researchers. This process will involve the synergistic collaboration between data scientists and physical scientists.

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FallAGU2023_MetadataFairness_v4.pdf

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Additional details

Dates

Available
2024-06-27