Improve dataset card: Add metadata and paper link

This PR significantly improves the dataset card for `GreenHyperSpectra` by:
- **Moving `license` to YAML metadata**: The `cc-by-nc-4.0` license is now correctly placed within the YAML metadata block at the top of the file, adhering to standard Hub practices.
- **Adding `task_categories`**: The `image-feature-extraction` task category has been added, which enhances the dataset's discoverability and proper classification on the Hub.
- **Adding `language`**: The dataset's language (`en`) has been specified in the metadata.
- **Adding `tags`**: Comprehensive tags such as `hyperspectral`, `vegetation`, `plant-traits`, `remote-sensing`, and `climate-change` have been added to provide better categorization and searchability based on the paper's content.
- **Linking to the research paper**: A direct link to the associated Hugging Face paper ([https://huggingface.co/papers/2507.06806](https://huggingface.co/papers/2507.06806)) has been added to the Markdown content, allowing users to easily access the scientific publication.

These changes make the dataset card more informative, discoverable, and compliant with Hugging Face Hub standards.

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
----
+```