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Related Experiment Video

Updated: Sep 16, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

SIMPROV: Provenance capturing for simulation studies.

Andreas Ruscheinski¹, Anja Wolpers¹, Philipp Henning¹

¹Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany.

|July 8, 2025

Summary

This summary is machine-generated.

This study introduces SIMPROV, a lightweight method for capturing complete simulation provenance. It enhances model interpretability and reusability across diverse software environments.

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Area of Science:

Computational Biology
Scientific Computing
Data Management

Background:

Improving interpretability and reusability of modeling and simulation studies is crucial.
Provenance, detailing entities, activities, and agents, is key to this goal.
Existing methods capture only partial provenance due to diverse software systems.

Purpose of the Study:

To introduce a lightweight method for recording complete simulation provenance.
To address the challenge of capturing provenance in heterogeneous modeling environments.
To enhance the interpretability and reusability of simulation studies.

Main Methods:

Developed a lightweight method to monitor modelers in their work environment.
Separated concerns between provenance capturers and a provenance builder.
Created a web interface for enhancing and exploring provenance graphs.

Main Results:

Successfully recorded complete provenance for simulation studies.
Demonstrated the practicality of the SIMPROV method.
Showcased the approach through two cell biological case studies.

Conclusions:

The SIMPROV method effectively captures comprehensive simulation provenance.
This approach facilitates enhanced model interpretability and reusability.
SIMPROV offers a practical solution for managing provenance in complex simulation workflows.