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A collaborative semantic-based provenance management platform for reproducibility.

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  • 1Michael Stifel Center Jena, Jena, Germany.

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Summary
This summary is machine-generated.

Scientific data management is crucial for reproducible research. CAESAR, a new framework, links experimental data, computational steps, and results for enhanced scientific reproducibility and reuse.

Keywords:
Jupyter NotebooksOntologyProvenanceReproducibilityResearch data management platformScientific experimentsSemantic WebVisualization

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Area of Science:

  • Scientific data management
  • Computational science
  • Microscopy research

Background:

  • Reproducibility in science requires accessible data and experimental steps.
  • Current systems lack integration between computational and non-computational experiment components.
  • Tracking provenance is key for understanding and reusing scientific experiments.

Purpose of the Study:

  • To present a novel solution for end-to-end provenance management in scientific experiments.
  • To develop a framework for capturing, managing, querying, and visualizing the complete experimental path.
  • To enhance the findability, accessibility, interoperability, and reusability (FAIR) of scientific data and results.

Main Methods:

  • Developed CAESAR (Collaborative Environment for Scientific Analysis with Reproducibility) framework.
  • Integrated the REPRODUCE-ME provenance model, extending semantic web standards.
  • Created ProvBook, a Jupyter Notebook extension for computational reproducibility.

Main Results:

  • CAESAR enables interoperable management of computational and non-computational experimental data and steps.
  • The REPRODUCE-ME model provides a comprehensive representation of experimental provenance.
  • ProvBook enhances the reproducibility of computational aspects of experiments.
  • The framework was successfully applied and evaluated in microscopy research.

Conclusions:

  • CAESAR offers a comprehensive solution for scientific experiment provenance management.
  • The framework facilitates improved understandability, reproducibility, and reusability of scientific research.
  • The integration of computational and non-computational provenance is vital for robust scientific outcomes.