Update dataset card

README.md

@@ -1,37 +1,264 @@
 ---
+annotations_creators:
+  - machine-generated
+language_creators:
+  - found
+language:
+  - en
+license: other
+multilinguality:
+  - monolingual
+size_categories:
+  - 1M<n<10M
+source_datasets:
+  - cogsci13/Amazon-Reviews-2023-Books-Meta
+task_categories:
+  - image-feature-extraction
+  - zero-shot-image-classification
+tags:
+  - iscc
+  - content-identification
+  - similarity-search
+  - deduplication
+  - image
+  - iso-24138
+  - amazon
+  - books
+  - book-covers
+pretty_name: "ISCC Codes for Amazon Book Covers"
 dataset_info:
   features:
-  - name: image_url
-    dtype: string
-  - name: iscc
-    dtype: string
-  - name: iscc_meta
-    dtype: string
-  - name: iscc_semantic
-    dtype: string
-  - name: iscc_content
-    dtype: string
-  - name: iscc_data
-    dtype: string
-  - name: iscc_instance
-    dtype: string
-  - name: source_row_id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: isbn
-    dtype: string
-  - name: publisher
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1815332785
-    num_examples: 3079720
-  download_size: 1295751379
-  dataset_size: 1815332785
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+    - name: image_url
+      dtype: string
+    - name: iscc
+      dtype: string
+    - name: iscc_meta
+      dtype: string
+    - name: iscc_semantic
+      dtype: string
+    - name: iscc_content
+      dtype: string
+    - name: iscc_data
+      dtype: string
+    - name: iscc_instance
+      dtype: string
+    - name: source_row_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: isbn
+      dtype: string
+    - name: publisher
+      dtype: string
 ---
+
+# ISCC Codes for Amazon Book Covers
+
+Amazon book covers enriched with full 256-bit ISCC (International Standard Content Code) identifiers for cover image identification, similarity search, and deduplication research. The `image_url` field links to cover images on Amazon CDN for preview.
+
+## What is ISCC?
+
+The **International Standard Content Code** ([ISO 24138:2024](https://www.iso.org/standard/77899.html)) is a
+content-derived identifier for digital media assets. Unlike traditional identifiers that are assigned
+arbitrarily, ISCC codes are generated algorithmically from the content itself, enabling:
+
+- **Content Identification**: Identify content regardless of format or location
+- **Similarity Search**: Find visually or semantically similar images
+- **Deduplication**: Detect exact and near-duplicate content
+- **Provenance Tracking**: Link derived works to their sources
+
+## ISCC Units
+
+Each record contains five 256-bit ISCC-UNITs that capture different aspects of the content:
+
+| Unit | Field | Description |
+|------|-------|-------------|
+| **Meta-Code** | `iscc_meta` | Similarity based on embedded metadata (filename, title) |
+| **Semantic-Code** | `iscc_semantic` | AI-based visual semantic similarity (what the image depicts) |
+| **Content-Code** | `iscc_content` | Perceptual image similarity (visual appearance) |
+| **Data-Code** | `iscc_data` | Raw binary data similarity (file structure) |
+| **Instance-Code** | `iscc_instance` | Cryptographic hash for exact matching (like SHA-256) |
+
+The `iscc` field contains the composite ISCC-CODE combining all units.
+
+## Dataset Structure
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image_url` | string | Cover image URL on Amazon CDN |
+| `iscc` | string | Full composite ISCC-CODE |
+| `iscc_meta` | string | 256-bit Meta-Code |
+| `iscc_semantic` | string | 256-bit Semantic-Code |
+| `iscc_content` | string | 256-bit Content-Code |
+| `iscc_data` | string | 256-bit Data-Code |
+| `iscc_instance` | string | 256-bit Instance-Code |
+| `source_row_id` | string | Original row identifier (`parent_asin`) in source dataset |
+| `title` | string | Book title |
+| `isbn` | string | ISBN-13 (preferred) or ISBN-10 |
+| `publisher` | string | Publisher name and edition info |
+
+### Data Splits
+
+| Split | Samples |
+|-------|---------|
+| train | 3,079,720 |
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("iscc/iscc-book-covers")
+```
+
+### Viewing a Sample
+
+```python
+sample = ds["train"][0]
+print(f"ISCC: {sample['iscc']}")
+print(f"Title: {sample['title']}")
+```
+
+### Similarity Search Example
+
+```python
+import iscc_core as ic
+
+# Get two ISCC codes to compare
+code1 = ds["train"][0]["iscc_content"]
+code2 = ds["train"][1]["iscc_content"]
+
+# Calculate hamming distance (0 = identical, 256 = maximally different)
+distance = ic.iscc_distance(code1, code2)
+print(f"Hamming distance: {distance}")
+
+# Convert to similarity percentage
+similarity = 1 - (distance / 256)
+print(f"Similarity: {similarity:.1%}")
+```
+
+### Finding Near-Duplicates
+
+```python
+import iscc_core as ic
+
+# Threshold for near-duplicates (adjust based on use case)
+THRESHOLD = 32  # ~87.5% similarity
+
+reference = ds["train"][0]["iscc_content"]
+
+for i, row in enumerate(ds["train"]):
+    distance = ic.iscc_distance(reference, row["iscc_content"])
+    if distance <= THRESHOLD and i > 0:
+        print(f"Near-duplicate found: row {i}, distance={distance}")
+```
+
+### Semantic Similarity Search
+
+```python
+import iscc_core as ic
+
+# Find semantically similar images (same subject/concept)
+reference = ds["train"][0]["iscc_semantic"]
+
+similar = []
+for i, row in enumerate(ds["train"]):
+    distance = ic.iscc_distance(reference, row["iscc_semantic"])
+    if distance <= 64:  # ~75% semantic similarity
+        similar.append((i, distance))
+
+# Sort by similarity
+for idx, dist in sorted(similar, key=lambda x: x[1])[:5]:
+    print(f"Row {idx} (distance={dist})")
+```
+
+## Source Data
+
+This dataset was derived from [cogsci13/Amazon-Reviews-2023-Books-Meta](https://huggingface.co/datasets/cogsci13/Amazon-Reviews-2023-Books-Meta).
+
+### Source Data
+Book metadata from the Amazon Reviews 2023 dataset by McAuley Lab.
+Cover images hosted by Amazon CDN. This derivative dataset contains
+ISCC codes and references to the original images, not the images themselves.
+**License**: Research use only. Refer to original dataset terms.
+
+### Processing
+
+ISCC codes were generated using:
+- [iscc-sdk](https://github.com/iscc/iscc-sdk) - High-level ISCC generation
+- [iscc-sci](https://github.com/iscc/iscc-sci) - Semantic image codes (experimental)
+
+All processing was performed on original resolution images downloaded from Amazon CDN.
+
+## Considerations
+
+### Intended Use
+
+- Content identification and matching research
+- Image similarity search algorithm development
+- Deduplication system benchmarking
+- Visual-semantic retrieval experiments
+- ISCC-based indexing research
+
+### Limitations
+
+- Semantic codes are generated using experimental AI models and may not capture all semantic nuances
+- ISCC codes are sensitive to significant image modifications (heavy cropping, overlays, filters)
+- Image URLs point to Amazon CDN and may become unavailable over time
+
+### Privacy
+
+This dataset contains ISCC codes and image URL references derived from the source dataset. Refer to the original
+dataset documentation for privacy considerations.
+
+## Citation
+
+If you use this dataset, please cite both this dataset and the original source:
+
+**This Dataset:**
+```bibtex
+@dataset{iscc_book_covers,
+  title = {{ISCC Codes for Amazon Book Covers}},
+  author = {{ISCC Foundation}},
+  year = {{2026}},
+  publisher = {{Hugging Face}},
+  url = {{https://huggingface.co/datasets/iscc/iscc-book-covers}}
+}
+```
+
+**Amazon Reviews 2023:**
+```bibtex
+@article{hou2024bridging,
+  title = {{Bridging Language and Items for Retrieval and Recommendation}},
+  author = {{Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}},
+  journal = {{arXiv preprint arXiv:2403.03952}},
+  year = {{2024}}
+}
+```
+
+**ISCC Standard:**
+```bibtex
+@misc{iso24138,
+  title = {{ISO 24138:2024 Information and documentation -- International Standard Content Code (ISCC)}},
+  author = {{International Organization for Standardization}},
+  year = {{2024}},
+  url = {{https://www.iso.org/standard/77899.html}}
+}
+```
+
+## Additional Resources
+
+- [ISCC Foundation](https://iscc.io/) - Standards organization
+- [ISCC Documentation](https://sdk.iscc.codes/) - Technical documentation
+- [ISO 24138:2024](https://www.iso.org/standard/77899.html) - Official standard
+- [iscc-sdk](https://github.com/iscc/iscc-sdk) - Python SDK for ISCC generation
+- [Amazon Reviews 2023](https://amazon-reviews-2023.github.io/)
+
+## Contact
+
+- **Dataset Issues**: [iscc-datasets GitHub](https://github.com/iscc/iscc-datasets/issues)
+- **ISCC Questions**: [ISCC Foundation](https://iscc.io/)