swell-ml-reproducibility

Reproducibility package for domain-informed machine learning model predicting swell potential of expansive soils.

https://github.com/hssa83/swell-ml-reproducibility

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (1.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Reproducibility package for domain-informed machine learning model predicting swell potential of expansive soils.

Basic Info
  • Host: GitHub
  • Owner: hssa83
  • License: mit
  • Default Branch: main
  • Size: 5.86 KB
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  • Releases: 1
Created 9 months ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.md

DOI

swell-ml-reproducibility

Reproducibility package for domain-informed machine learning model predicting swell potential of expansive soils.

Owner

  • Login: hssa83
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this code or data, please cite it as below."
title: "A Domain-Informed Data Cleaning Framework for Geotechnical Machine Learning"
authors:
  - family-names: Alharbi
    given-names: Hani S.
    affiliation: Shaqra University
    orcid: https://orcid.org/0009-0003-6066-6648  
date-released: 2025-06-17
version: 1.0-paper
doi: 10.5281/zenodo.15685091
url: https://doi.org/10.5281/zenodo.15685091
license: MIT

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