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Updated: Dec 9, 2025

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Enhancing Traceability in Clinical Research Data through a Metadata Framework.

Samuel Hume1, Surendra Sarnikar2, Cherie Noteboom3

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This study introduces Trace-XML, a framework enhancing metadata traceability in clinical research. It improves data lineage and reproducibility by identifying and validating traceability gaps across the data lifecycle.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Clinical Research Informatics

Background:

  • Clinical research data lifecycle operates in silos, limiting traceability.
  • Traceability is crucial for regulated and non-regulated studies.
  • Existing tools offer limited metadata traceability and cross-phase querying.

Purpose of the Study:

  • Develop a metadata traceability framework for querying and visualizing traceability.
  • Identify and validate traceability gaps to improve data lineage and reproducibility.

Main Methods:

  • Employed the design science research paradigm to create and evaluate an IT artifact.
  • Developed Trace-XML, a framework extending metadata models and using graph traversal algorithms.
  • Evaluated Trace-XML using analytical and qualitative methods.

Main Results:

  • Trace-XML accurately and completely assesses metadata traceability.
  • Qualitative analysis confirmed Trace-XML's utility for researchers.
  • The framework enables querying, validation, and visualization of traceability metadata.

Conclusions:

  • Trace-XML effectively addresses the problem of limited metadata traceability in clinical research.
  • The framework enhances the ability to create and assess end-to-end study traceability.
  • Trace-XML provides essential features for identifying gaps, validating, and visualizing metadata traceability.