PROVenance Patterns in Numerical Modelling and Finite Element Simulation Processes of Bio-electric Systems^.

Area of Science:

• Biomedical Engineering
• Computational Science

Background:

• Scientific reproducibility is crucial, especially in biomedical engineering for in-vivo, in-vitro, and in-silico studies.
• Numerical modeling and finite element simulations of bio-electric systems are complex, requiring detailed documentation.

Purpose of the Study:

• To apply the W3C PROV (PROVenance) standard for structuring and documenting bio-electric modeling and simulation processes.
• To enhance the accessibility of these complex processes for researchers and automated analyses.

Main Methods:

• Utilized the PROV data model to represent entities (data, models, software, literature, assumptions, equations) and their relationships.
• Identified and modeled five key process patterns: information extraction, geometrical model generation, model composition, parameterization, and refinement.
• Applied provenance modeling to a case study of electrical stimulation in alloplastic reconstruction plates of the mandible.

Main Results:

• Successfully structured the provenance of a bio-electric modeling and simulation workflow.
• Distinguished various entity types and their roles within the simulation activities.
• Identified five recurring process patterns crucial for understanding the simulation development.

Conclusions:

• The PROV standard provides a robust framework for documenting complex bio-electric modeling and simulation processes.
• This provenance modeling approach is a significant step towards improving the reproducibility and transparency of academic research in biomedical engineering.