Improve dataset card: add task category, links, and usage

README.md

@@ -1,5 +1,12 @@
+---
+task_categories:
+- time-series-forecasting
+---
+
 # TSCOMP Corpus
 
+[Paper](https://huggingface.co/papers/2605.26562) | [GitHub](https://github.com/SUFE-AILAB/TSCOMP)
+
 The TSCOMP (Time-Series Component-level Benchmarking) Corpus is a curated collection of evaluation results from systematic component-level experiments in deep multivariate time-series forecasting.
 
 ## Overview
@@ -14,13 +21,20 @@ This corpus enables researchers to train **custom meta predictors** — models t
 
 Each subdirectory in the archive corresponds to a downstream dataset (e.g., ECL, ETTh1, Exchange, weather). Within each dataset folder, individual experiment directories encode the full component configuration in their names, and **metrics.npy** contains the evaluation metrics for that run.
 
-## Source
+## Sample Usage
+
+The corpus is designed to be used for meta-learning. You can use the provided code in the GitHub repository to run experiments or extract features:
 
-This corpus is generated from the official TSCOMP project:
+```bash
+# Run meta learning experiments
+python meta/run.py --mode simple --test_dataset ETTh2 --meta_model_type mlp
 
-**https://github.com/SUFE-AILAB/TSCOMP**
+# Extract meta-features for datasets
+python meta/meta_features/get_meta_features_LTF.py --meta_feature_type tabpfn
 
-For more details on the experimental framework and component taxonomy, please refer to the associated paper.
+# Apply meta selection to new datasets
+python meta/run_custom.py --new_dataset my_dataset --checkpoint_path <path> --new_dataset_path <csv_path> --scripts_root <scripts_dir>
+```
 
 ## 📝 Citation