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Related Experiment Video

Updated: Aug 5, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Data Provenance in Biomedical Research: Scoping Review.

Marco Johns1, Thierry Meurers1, Felix N Wirth1

  • 1Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.

Journal of Medical Internet Research
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Data provenance in biomedical research lacks standardization, hindering reproducibility. Developing a common framework and reference models is crucial for wider adoption and improved scientific practice.

Keywords:
biomedical researchcomparisondata provenancescoping reviewsystematization

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Scientific Reproducibility

Background:

  • Data provenance, detailing data origin and processing, is vital for enhancing reproducibility and quality in biomedical research.
  • Despite growing interest and adoption in other fields, data provenance technologies remain underutilized in biomedical research.

Purpose of the Study:

  • To systematically review and map the landscape of data provenance methods in biomedical research.
  • To describe and compare existing provenance technologies, identifying functionalities and design patterns.
  • To pinpoint research gaps and opportunities for developing more widely adoptable provenance solutions.

Main Methods:

  • A scoping review following PRISMA-ScR guidelines, searching PubMed, IEEE Xplore, and Web of Science.
  • Inclusion of original articles on software-based provenance management in scientific research (2010-2021).
  • Data extraction across publication metadata, application scope, provenance aspects, data representation, and functionalities.

Main Results:

  • 44 articles published between 2010-2021 were analyzed, revealing significant heterogeneity in provenance solutions.
  • Identified relationships between motivations for provenance use, feature sets, and implementation details.
  • A key gap identified: limited focus on provenance data analysis and adoption of standards like PROV.

Conclusions:

  • The heterogeneity highlights a lack of unified understanding and common framework for biomedical data provenance.
  • Developing a shared framework, reference models, and benchmarking datasets can promote more integrated provenance solutions.
  • Addressing identified gaps can foster wider adoption and improve scientific practice in the biomedical field.