Improve dataset card: Add metadata and paper link

README.md

@@ -1,26 +1,36 @@
 ---
 configs:
 - config_name: labeled_all
-  data_files: "labeled_all/all.csv"
-  # data_files:
-  # - split: all
-  #   path: "labeled_all/all.csv"
+  data_files: labeled_all/all.csv
   default: true
-  description: "Labeled spectra data with trait measurements for supervised learning."
+  description: Labeled spectra data with trait measurements for supervised learning.
 - config_name: unlabeled
-  data_files: "unlabeled/*.csv"
-  description: "Unlabeled spectra data for semi-supervised or self-supervised learning."
-
+  data_files: unlabeled/*.csv
+  description: Unlabeled spectra data for semi-supervised or self-supervised learning.
 - config_name: labeled_splits
   data_files:
   - split: train
-    path: "labeled_splits/train.csv"
+    path: labeled_splits/train.csv
   - split: test
-    path: "labeled_splits/test.csv"
-  description: "A stratified splitting of the labeled data."
+    path: labeled_splits/test.csv
+  description: A stratified splitting of the labeled data.
+task_categories:
+- image-feature-extraction
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- hyperspectral
+- vegetation
+- plant-traits
+- remote-sensing
+- climate-change
 ---
+
 # 🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱
 
+[Paper](https://huggingface.co/papers/2507.06806)
+
 GreenHySpectra is a collection of hyperspectral reflectance data of vegetation from different sources. It is intended for Regression machine learning task for plant trait prediction with self and semi-supervised learning.
 
 ## 📁 Configurations
@@ -86,8 +96,4 @@ train_dataset = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', sp
 train_dataset = train_dataset.to_pandas().drop(['Unnamed: 0'], axis=1)
 display(df.head())
 
-```
-
-
-license: cc-by-nc-4.0
----
+```