Climate-Invariant Machine Learning
<p>The "Climate-Invariant Machine Learning" manuscript's accompanying data is organized into two folders:</p> <ul> <li>"CIML_Fig_Data_v2.zip" contains the data necessary to reproduce all the manuscript's figures by running the Jupyter notebook <a href="https://github.com/tbeucler/CBRAIN-CAM/blob/master/notebooks/tbeucler_devlog/090_Climate_Invariant_Paper_Figures_v2.ipynb">at this link</a> and to train climate-invariant models by running the Jupyter notebook <a href="https://colab.research.google.com/github/tbeucler/CBRAIN-CAM/blob/master/Climate_Invariant_Guide.ipynb">at this link</a>.</li> <li>"CIML_SPCAM5_Initialization" contains the data necessary to intialize and re-run the three SPCAM5, Earth-like simulations used in the manuscript.</li> </ul> <p>See SI A of the manuscript and the notebooks for more details.</p> <p>This is a pre-release: The release will be final if the manuscript if accepted for publication after peer-review.</p>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4