Bewaard in:
| Hoofdauteur: | |
|---|---|
| Formaat: | Recurso digital |
| Taal: | Engels |
| Gepubliceerd in: |
Zenodo
2025
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| Onderwerpen: | |
| Online toegang: | https://doi.org/10.5281/zenodo.18410481 |
| Tags: |
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Inhoudsopgave:
- <p>A comprehensive benchmark dataset and codebase for evaluating machine learning models in marine science applications. This package includes:</p> <p>- 9 marine datasets (159,851 total samples) covering biotoxin detection, oceanographic measurements, satellite data, and phytoplankton analysis<br>- 37 pre-trained models across 5 algorithms (Random Forest, XGBoost, SVR, LSTM, Transformer)<br>- Complete reproducible pipeline with cross-platform scripts<br>- Publication-ready figures and tables<br>- Comprehensive documentation and methodology</p> <p>The benchmark evaluates both traditional machine learning and deep learning approaches on diverse marine science tasks, providing standardized evaluation metrics and reproducible results for the research community.</p> <p>Key Features:<br>- Cross-sectional and time-series marine datasets<br>- Traditional ML vs Deep Learning comparison<br>- Comprehensive data validation and sanity checks<br>- Publication-ready visualizations<br>- Complete reproducibility package<br>- Multi-platform support (Windows/Linux/Mac)</p> <p>This work supports reproducible research in marine machine learning and provides a foundation for future developments in the field.</p>