Update README.md

README.md

@@ -4,14 +4,11 @@
 **License:** CC-BY-4.0  
 **Paper:** https://arxiv.org/abs/2511.06943, accepted in AAAI
 
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-
 ## Introduction
 
 PlantTraitNet is a curated multimodal dataset for training and evaluating global plant trait inference models using citizen science images paired with trait distributions from TRY.  
 Each sample contains an RGB observation, species information, geolocation, and quantified trait ranges supporting uncertainty-aware modeling.
 
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 ## Dataset Splits
 
@@ -23,8 +20,6 @@ Each sample contains an RGB observation, species information, geolocation, and q
 | `benchmark` | Benchmark set (benchmark + val) for global evaluation vs sPlotOpen | Cross-dataset benchmarking |
 | `traitmap` | Global `.tif` layers (TRY community-weighted means + sPlotOpen) | Global mapping + inference |
 
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-
 ## Column Schema
 
 | Column | Type | Meaning |
@@ -40,17 +35,15 @@ Each sample contains an RGB observation, species information, geolocation, and q
 
 > `<trait>` is replaced by each trait included in the dataset.
 
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-
-## 🔧 Usage
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("username/PlantTraitNet")
-
-train = ds["train"]
-val = ds["val"]
-reference = ds["reference"]
-benchmark = ds["benchmark"]
-traitmap = ds["traitmap"]
+## Citation
+Coming soon: Citation of published verison
+```bibtex
+@misc{sharma2025planttraitnetuncertaintyawaremultimodalframework,
+      title={PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data}, 
+      author={Ayushi Sharma and Johanna Trost and Daniel Lusk and Johannes Dollinger and Julian Schrader and Christian Rossi and Javier Lopatin and Etienne Laliberté and Simon Haberstroh and Jana Eichel and Daniel Mederer and Jose Miguel Cerda-Paredes and Shyam S. Phartyal and Lisa-Maricia Schwarz and Anja Linstädter and Maria Conceição Caldeira and Teja Kattenborn},
+      year={2025},
+      eprint={2511.06943},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2511.06943}, 
+}