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Area of Science:

  • Data Science
  • Information Science
  • Computer Science

Background:

  • Big data and FAIR data principles necessitate machine-interpretable data formats.
  • Human readability is a key, yet often unquantified, property in data format standardization.
  • Existing standards like STAR, W3C PROV, and XML development highlight the need for human readability.

Purpose of the Study:

  • To define and measure human readability in structured data archival formats.
  • To compare the human readability of data represented in different formats, such as JSON and XML.
  • To evaluate if current data standards achieve both machine interpretability and human readability.

Main Methods:

  • Reviewing aspects of written text that influence readability.
  • Adapting educational readability metrics for structured data.
  • Applying the proposed metric to compare data in various formats (e.g., JSON, XML).

Main Results:

  • A proposed metric for estimating the relative human readability of structured data.
  • Comparative analysis of data formats based on the new readability metric.
  • Insights into the trade-offs between machine interpretability and human readability in data standards.

Conclusions:

  • Human readability in data formats can be estimated and compared using a proposed metric.
  • The study provides a framework for evaluating data format design beyond mere machine interpretability.
  • Further research can refine the metric and apply it to a wider range of data standards.