diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b033b8be422de55db1ab55c816c8d41379e7e6c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,81 @@
+---
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- imagenet
+- corruption
+- robustness
+- computer-vision
+- image-classification
+size_categories:
+- 1M<n<10M
+---
+
+# Corruption Dataset: Motion_Blur
+
+## Dataset Description
+
+This dataset contains corrupted versions of ImageNet-1K images using **motion_blur** corruption. It is part of the ImageNet-C benchmark for evaluating model robustness to common image corruptions.
+
+### Dataset Structure
+
+- **Train**: 1,281,167 corrupted images
+- **Validation**: 50,000 corrupted images  
+- **Classes**: 1000 ImageNet-1K classes
+- **Format**: Arrow (Hugging Face Datasets)
+
+### Corruption Type: Motion_Blur
+
+Applies motion blur, simulating camera or object movement.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("MarMaster/corruption-motion_blur")
+
+# Access train and validation splits
+train_dataset = dataset["train"]
+val_dataset = dataset["validation"]
+
+# Example usage
+for example in train_dataset:
+    image = example["image"]
+    class_id = example["class_id"]
+    filename = example["filename"]
+```
+
+## Dataset Statistics
+
+- **Total Images**: 1,331,167
+- **Train Images**: 1,281,167
+- **Validation Images**: 50,000
+- **Classes**: 1000
+- **Image Format**: RGB
+- **Average Image Size**: Variable (ImageNet-1K standard)
+
+## Citation
+
+If you use this dataset, please cite the original ImageNet-C paper:
+
+```bibtex
+@article{hendrycks2019benchmarking,
+  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
+  author={Hendrycks, Dan and Dietterich, Tom},
+  journal={Proceedings of the International Conference on Learning Representations},
+  year={2019}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. The original ImageNet dataset follows its own licensing terms.
+
+## Contact
+
+For questions or issues, please contact: marcin.osial@[your-institution].edu
diff --git a/dataset_info.json b/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..61402f8bbb1a410eb042920e3575d7d6cdba1120
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+++ b/dataset_info.json
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+{
+  "dataset_name": "corruption-motion_blur",
+  "dataset_version": "1.0.0",
+  "description": "ImageNet-Corruption dataset with motion_blur corruption",
+  "license": "mit",
+  "task_categories": [
+    "image-classification"
+  ],
+  "language": [
+    "en"
+  ],
+  "tags": [
+    "imagenet",
+    "corruption",
+    "robustness",
+    "computer-vision"
+  ],
+  "size_categories": "1M<n<10M",
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 0,
+      "num_examples": 1281158
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 0,
+      "num_examples": 50000
+    }
+  },
+  "features": {
+    "image": {
+      "dtype": "image",
+      "_type": "Image"
+    },
+    "class_id": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "split": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "filename": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "download_size": 0,
+  "dataset_size": 0,
+  "created_at": "2025-10-27 21:23:31",
+  "corruption_type": "motion_blur"
+}
\ No newline at end of file
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