Populate dataset card for MToP pre-run experimental data

This PR populates the dataset card for the MToP pre-run experimental data.

It adds:
- A clear description of the dataset, highlighting its nature as extensive pre-run experimental data from the MToP platform for Evolutionary Multitasking.
- Links to the associated paper, the MToP platform's GitHub repository, and the project page.
- `task_categories: ['other']`, `language: en`, and relevant `tags` (`evolutionary-computation`, `optimization`, `multitask-optimization`, `benchmarking`, `matlab`) to improve discoverability.
- Instructions for downloading the data using `git lfs`.
- The BibTeX citation for the paper.

README.md

@@ -1,3 +1,57 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- other
+language:
+- en
+tags:
+- evolutionary-computation
+- optimization
+- multitask-optimization
+- benchmarking
+- matlab
+---
+
+# MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking (Pre-run Experimental Data)
+
+This repository hosts the extensive pre-run experimental data generated by the **MToP (Multitask Optimization Platform)**, as presented in the paper [MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking](https://huggingface.co/papers/2312.08134).
+
+MToP is the first open-source benchmarking platform for Evolutionary Multitasking (EMT), designed to concurrently address multiple optimization tasks within limited computing resources. This dataset provides comprehensive experimental results from the MToP platform, aiming to enhance reproducibility and reduce computational overhead for researchers working with Multitask Evolutionary Algorithms (MTEAs).
+
+The MToP platform incorporates:
+*   Over 50 Multitask Evolutionary Algorithms (MTEAs) for multitask optimization.
+*   Over 50 adapted single-task evolutionary algorithms for multitask optimization problems.
+*   More than 200 Multitask Optimization (MTO) problem cases, including real-world applications.
+*   Over 150 classical single-task optimization benchmark problems.
+*   Over 20 performance metrics covering single- and multi-objective optimization.
+
+This dataset comprises experimental results of various algorithms on benchmark suites over 30 independent runs under fixed random seeds, facilitating comparative analyses and research in evolutionary multitasking.
+
+## Links
+
+*   **Paper:** [MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking](https://huggingface.co/papers/2312.08134)
+*   **MToP Platform Code (GitHub):** [https://github.com/intLyc/MTO-Platform](https://github.com/intLyc/MTO-Platform)
+*   **Project Page:** [MTO Website](http://www.bdsc.site/websites/MTO/index.html)
+
+## Data Download
+
+You can download the data from this repository directly using `git lfs`:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/intLyc/MToP-MTOData
+```
+
+## Citation
+
+If you find this platform or dataset useful for your research, please cite the original paper:
+
+```bibtex
+@Article{Li2023MToP,
+  title   = {{MToP}: A {MATLAB} Benchmarking Platform for Evolutionary Multitasking},
+  author  = {Yanchi Li and Wenyin Gong and Tingyu Zhang and Fei Ming and Shuijia Li and Qiong Gu and Yew-Soon Ong},
+  journal = {arXiv preprint arXiv:2312.08134},
+  year    = {2023},
+  eprint  = {2312.08134},
+}
+```