Update README.md
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README.md
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@@ -30,10 +30,10 @@ and environmental datasets with the LUCAS soil survey as the backbone.
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The released dataset contains:
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- 72,
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- 1,
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- 68 integrated ESDAC source datasets
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- Numerical
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LUCAS-MEGA is designed for representation learning in soil science. It provides a unified sample-feature space where
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models can learn relationships across soil, climate, terrain, land-use, hydrological, and environmental variables.
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The fusion dataset is the released LUCAS-MEGA dataset. It integrates standardized source datasets into a unified
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sample-feature representation. Each sample is a soil observation, and each feature is a fused soil or environmental
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variable. Features may be
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to dense asset files.
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### Released Files
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```
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The standardized representation contains cleaned and normalized individual source datasets. These datasets have been
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converted into common formats but are not yet fused into the final LUCAS-MEGA sample-feature
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It includes:
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python viewer.py
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```
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<img src="resources/preview.png" alt="preview" style="width:
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---
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The released dataset contains:
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- 72,000+ soil samples
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- 1,000+ fused soil and environmental features
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- 68 integrated ESDAC source datasets
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- Numerical (scalar- and vector-valued), categorical, textual, and visual data
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LUCAS-MEGA is designed for representation learning in soil science. It provides a unified sample-feature space where
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models can learn relationships across soil, climate, terrain, land-use, hydrological, and environmental variables.
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|
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| 71 |
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The fusion dataset is the released LUCAS-MEGA dataset. It integrates standardized source datasets into a unified
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sample-feature representation. Each sample is a soil observation, and each feature is a fused soil or environmental
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variable. Features may be numerical values, categorical labels, text fields, vector-valued measurements, images, or links
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to dense asset files.
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### Released Files
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```
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The standardized representation contains cleaned and normalized individual source datasets. These datasets have been
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converted into common formats but are not yet fused into the final LUCAS-MEGA sample-feature tabular transformation.
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It includes:
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python viewer.py
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```
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<img src="resources/preview.png" alt="preview" style="width:60%;">
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---
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