Update README.md

README.md

@@ -70,7 +70,32 @@ The dataset contains the raw images and two metadata files representing the data
 
 #### Categories
 Both JSON files share a consistent mapping of 23 categories: 
-`0: grevy's zebra`, `1: plains zebra`, `2: giraffe_reticulated`, `3: ignore`, `4: bird`, `5: antelope`, `6: impala`, `7: goat`, `8: water_buffalo`, `9: person`, `10: gazelle`, `11: lion`, `12: donkey`, `13: domesticated_cow`, `14: rhino_white`, `15: elephant_savanna`, `16: horse`, `17: ostrich`, `18: warthog`, `19: car`, `20: rhino_black`, `21: camel`, `22: ____`
+
+* `0: grevy's zebra`
+* `1: plains zebra`
+* `2: giraffe_reticulated`
+* `3: ignore`
+* `4: bird`
+* `5: antelope`
+* `6: impala`
+* `7: goat`
+* `8: water_buffalo`
+* `9: person`
+* `10: gazelle`
+* `11: lion`
+* `12: donkey`
+* `13: domesticated_cow`
+* `14: rhino_white`
+* `15: elephant_savanna`
+* `16: horse`
+* `17: ostrich`
+* `18: warthog`
+* `19: car`
+* `20: rhino_black`
+* `21: camel`
+* `22: ____`
+
+
 
 #### `initial_metadata.json`
 Contains metadata of the image files before running the pipeline:
@@ -90,9 +115,9 @@ Contains cleaned image metadata and a rich `annotations` array generated by the
 - **Annotations:**
   - **`image_uuid`**: Links the annotation to the parent image.
   - **`bbox`**: Absolute bounding box `[x, y, width, height]` of the detected animal.
-  - **`region_bbox`**: An expanded/localized bounding box for identifying specific physical traits (e.g., flanks).
+  - **`region_bbox`**: A localized bounding box of census annotation region.
   - **`category_id`**: Integer corresponding to the species category list.
-  - **`viewpoint`**: String denoting orientation (e.g., `right`, `left`).
+  - **`viewpoint`**: String denoting viewpoint orientation (e.g., `right`, `left`).
   - **`CA_score`**: Float representing the Identifiable Annotation (IA) confidence score.
   - **`annotations_census`**: Boolean indicating if the annotation is valid for population counting.
   - **`annot_uuid`**: Unique identifier for the specific detection.
@@ -119,4 +144,24 @@ This dataset is the direct output of the GGR (Great Grevy's Rally) Pipeline, a 9
 
 - **Viewpoint Filtering:** Because animal Re-ID relies heavily on flank stripe patterns, the pipeline aggressively filters for `left` and `right` viewpoints. Forward or backward-facing animals are detected but may be excluded from Re-ID tracking.
 - **Geographic Focus:** The GPS coordinates center heavily on specific Kenyan ecosystems. Models trained heavily on this dataset may experience domain shift when deployed in different biomes or lighting conditions.
-- **Class Imbalance:** Due to the targeted nature of the Great Grevy's Rally, equid classes heavily outnumber categories like `lion` or `rhino_black`.
+- **Class Imbalance:** Due to the targeted nature of the Great Grevy's Rally, equid classes heavily outnumber categories like `lion` or `rhino_black`.
+
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@misc{gzcd2026,
+  author = {Ankit K. Upadhyay and Ekaterina Nepovinnykh and S M Rayeed and Aidan Westphal and Lawrence Miao and Julian Bain and Jaeseok Kang and Tuomas Eerola and Heikki Kälviäinen and Charles V. Stewart},
+  title = {GZCD: Grevy's Zebra Census Dataset},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{[https://huggingface.co/datasets/imageomics/GZCD](https://huggingface.co/datasets/imageomics/GZCD)}}
+}
+```
+
+## Dataset Card Authors
+
+S M Rayeed
+Elizabeth G. Campolongo