Add dataset card and paper link

README.md

@@ -0,0 +1,46 @@
+---
+task_categories:
+- time-series-forecasting
+tags:
+- climate
+- time-series
+- satellite-imagery
+license: unknown
+---
+
+# ClimateBench-M: A Multi-modal Climate Data Benchmark 
+
+ClimateBench-M is a multi-modal climate benchmark designed to support the development of artificial general intelligence (AGI) in climate applications. It aligns data across three critical modalities at a unified spatio-temporal resolution:
+
+1.  Time-series climate variables from ERA5.
+2.  Extreme weather event records from NOAA.
+3.  Satellite imagery from NASA HLS.
+
+*   [**ClimateBench-M-TS**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-TS): Climate time series with extreme events aligned and labeled.
+*   [**ClimateBench-M-IMG**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-IMG): Satellite imagery data
+*   [**📖 Paper**](https://arxiv.org/abs/2404.00225)
+
+If you find this repository useful in your research, please consider citing the following paper:
+
+```
+@inproceedings{DBLP:conf/kdd/ZhengJLTH24,
+  author       = {Lecheng Zheng and
+                  Baoyu Jing and
+                  Zihao Li and
+                  Hanghang Tong and
+                  Jingrui He},
+  editor       = {Ricardo Baeza{-}Yates and
+                  Francesco Bonchi},
+  title        = {Heterogeneous Contrastive Learning for Foundation Models and Beyond},
+  booktitle    = {Proceedings of the 30th {ACM} {SIGKDD} Conference on Knowledge Discovery
+                  and Data Mining, {KDD} 2024, Barcelona, Spain, August 25-29, 2024},
+  pages        = {6666--6676},
+  publisher    = {{ACM}},
+  year         = {2024},
+  url          = {https://doi.org/10.1145/3637528.3671454},
+  doi          = {10.1145/3637528.3671454},
+  timestamp    = {Sun, 08 Sep 2024 16:05:58 +0200},
+  biburl       = {https://dblp.org/rec/conf/kdd/ZhengJLTH24.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```