Improve dataset card: Add paper, code, description, tasks, and tags (Update license to MIT)

This PR significantly improves the dataset card for the CISIR datasets.

It adds:
- A link to the paper: https://huggingface.co/papers/2509.16339
- A link to the associated GitHub repository for the code: https://github.com/Machine-Earning/CISIR
- A detailed description of the dataset collection, including the abstract, overview, key contributions, and individual dataset descriptions, sourced from the paper and its GitHub README.
- Relevant `task_categories` (`tabular-regression`) and `tags` (e.g., `regression`, `tabular-data`, `imbalanced-learning`, `sep-forecasting`, `solar-physics`, `machine-learning`, `deep-learning`) for better discoverability.
- The BibTeX citation for proper attribution.
- A direct link for data download.

The `license` in the metadata has also been corrected from `apache-2.0` to `mit`, as indicated in the project's GitHub repository.

README.md

@@ -1,3 +1,67 @@
----
-license: apache-2.0
----
+---
+license: mit
+task_categories:
+- tabular-regression
+tags:
+- regression
+- tabular-data
+- imbalanced-learning
+- sep-forecasting
+- solar-physics
+- machine-learning
+- deep-learning
+---
+
+# Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
+
+This repository contains the datasets used in the paper [Highly Imbalanced Regression with Tabular Data in SEP and Other Applications](https://huggingface.co/papers/2509.16339).
+
+Code: [https://github.com/Machine-Earning/CISIR](https://github.com/Machine-Earning/CISIR)
+
+## Abstract
+
+We investigate imbalanced regression with tabular data that have an imbalance ratio larger than 1,000 ("highly imbalanced"). Accurately estimating the target values of rare instances is important in applications such as forecasting the intensity of rare harmful Solar Energetic Particle (SEP) events. For regression, the MSE loss does not consider the correlation between predicted and actual values. Typical inverse importance functions allow only convex functions. Uniform sampling might yield mini-batches that do not have rare instances. We propose CISIR that incorporates correlation, Monotonically Decreasing Involution (MDI) importance, and stratified sampling. Based on five datasets, our experimental results indicate that CISIR can achieve lower error and higher correlation than some recent methods. Also, adding our correlation component to other recent methods can improve their performance. Lastly, MDI importance can outperform other importance functions.
+
+## Overview
+
+This dataset collection supports the official implementation of **CISIR** (Correlation, Involution, Stratified Importance Regression), a novel method for highly imbalanced regression with tabular data. The method is particularly applicable to SEP (Solar Energetic Particle) forecasting research for NASA and other domains requiring accurate prediction of rare, high-impact events.
+
+### Key Contributions
+
+- **Correlation-aware loss function** that considers the correlation between predicted and actual values
+- **Monotonically Decreasing Involution (MDI) importance** weighting that outperforms traditional convex importance functions
+- **Stratified sampling strategy** that ensures rare instances are included in mini-batches
+- **Comprehensive evaluation** on five highly imbalanced datasets with imbalance ratios > 1,000
+
+## Datasets
+
+We evaluate our method on five highly imbalanced datasets. All datasets exhibit high imbalance ratios (ρ > 1,000), making them ideal for evaluating highly imbalanced regression methods.
+
+Download all datasets: [https://huggingface.co/datasets/Machine-Earning/CISIR-datasets/resolve/main/CISIR-data.zip](https://huggingface.co/datasets/Machine-Earning/CISIR-datasets/resolve/main/CISIR-data.zip)
+
+### SEP Datasets
+- **SEP-EC**: Forecasts the change (delta) in proton intensity based on features from electron intensity and CMEs (Coronal Mass Ejections)
+- **SEP-C**: Forecasts peak proton intensity based on CME characteristics
+
+### Other Datasets
+- **SARCOS**: Estimates the torque vector based on joint-state inputs for a 7-DOF robot arm
+- **Blog Feedback (BF)**: Forecasts the number of comments based on textual, temporal, and engagement features  
+- **Online News Popularity (ONP)**: Estimates the number of shares of an article based on content, topic, and sentiment attributes
+
+## Citation
+
+If you find these datasets useful in your research, please consider citing the associated paper:
+
+```bibtex
+@inproceedings{moukpe2024cisir,
+  title={Highly Imbalanced Regression with Tabular Data in SEP and Other Applications},
+  author={Moukpe, Josias K. and Chan, Philip K. and Zhang, Ming},
+  booktitle={Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA)},
+  year={2024},
+  organization={IEEE}
+}
+```
+
+## License
+
+This project and its datasets are licensed under the MIT License.