dk23mer1r12-nitw/ML-dataset-for-Materials-letters: Research Dataset & Code Package v1.1.0 material letters
This release contains the complete Jupyter Notebook–based analysis framework for TPMS scaffold evaluation, porosity–velocity–pressure drop prediction, and machine learning modelling. It includes data preprocessing scripts, ML training pipelines, residual plot generation, permeability estimation, and figure exports used for implant and scaffold simulations. This version represents the first stable, citable release for research and publication purposes.
Resource information
- Format
- zip
- Original repository
- 17669814
- DOI
- 10.5281/zenodo.17669814
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
This release contains the complete Jupyter Notebook–based analysis framework for TPMS scaffold evaluation, porosity–velocity–pressure drop prediction, and machine learning modelling. It includes data preprocessing scripts, ML training pipelines, residual plot generation, permeability estimation, and figure exports used for implant and scaffold simulations. This version represents the first stable, citable release for research and publication purposes.