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Data Provenance Standards and Recommendations for FAIR Data.

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This review examines five data provenance models, proposing six essential properties for robust data management. These properties support FAIR data principles for research and health information.

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Area of Science:

  • Computer Science
  • Information Science
  • Data Management

Background:

  • Data provenance is crucial for understanding data origins and ensuring data integrity.
  • Existing data provenance models vary in their characteristics and capabilities.
  • The need for standardized provenance mechanisms supporting data sharing and reuse is increasing.

Purpose of the Study:

  • To review and analyze the main characteristics of five widely used data provenance models.
  • To propose a set of six essential provenance properties for effective data provenance mechanisms.
  • To ensure these properties support the findable, accessible, interoperable, and reusable (FAIR) principles for research and health data.

Main Methods:

  • Literature review of five prominent data provenance models.
  • Comparative analysis of their key features and functionalities.
  • Development of a proposed set of six core provenance properties.

Main Results:

  • Identification of the strengths and weaknesses of the reviewed data provenance models.
  • A defined set of six critical properties for evaluating and developing new provenance models.
  • Demonstration of how these properties align with and enhance FAIR data principles.

Conclusions:

  • A standardized set of provenance properties is essential for consistent and reliable data provenance.
  • Implementing these properties will facilitate better data management and adherence to FAIR principles.
  • This framework supports the trustworthy use of research and health data.