Formatting and related reference clarification (mainly links)

README.md

@@ -2,7 +2,7 @@
 license: cc0-1.0
 language:
 - en
-pretty_name: mmla_opc
+pretty_name: MMLA Ol Pejeta Conservancy
 task_categories:
 - image-classification
 tags:
@@ -13,24 +13,25 @@ tags:
 - drone
 - zebra
 size_categories: 10K<n<100K
+description: "Annotated video frames of Plains zebras collected at the Ol Pejeta Conservancy (OPC) in Kenya using the semi-autonomous WildWing system. includes frames from various sessions, with annotations indicating the presence of zebras in the images in YOLO format. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery to facilitate research in wildlife monitoring and conservation using advanced imaging technologies."
 ---
 
 
-# Dataset Card for mmla-opc
+# Dataset Card for MMLA Ol Pejeta Conservancy
 
 <!-- Provide a quick summary of what the dataset is or can be used for. --> 
 
 ## Dataset Details
-This is a dataset containing annotated video frames of Plains zebras collected at the Ol Pejeta Conservancy (OPC) in Kenya using the semi-autonomous WildWing system. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery. The dataset includes frames from various sessions, 
-with annotations indicating the presence of zebras in the images in YOLO format. The dataset is designed to facilitate research in wildlife monitoring and conservation using advanced imaging technologies.
+This is a dataset containing annotated video frames of Plains zebras collected at the [Ol Pejeta Conservancy (OPC)](https://www.olpejetaconservancy.org/) in Kenya using the semi-autonomous [WildWing system](https://imageomics.github.io/wildwing/). The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery. It includes frames from various sessions, 
+with annotations indicating the presence of zebras in the images in YOLO format, and is designed to facilitate research in wildlife monitoring and conservation using advanced imaging technologies.
 
 
 ### Dataset Description
 
 
 - **Curated by:** Jenna Kline
-- **Homepage:** [mmla project](https://github.com/Imageomics/mmla)
-- **Repository:** [https://github.com/Imageomics/mmla](https://github.com/Imageomics/mmla)
+- **Homepage:** [MMLA project](https://github.com/Imageomics/mmla)
+- **Repository:** [Imageomics/mmla](https://github.com/Imageomics/mmla)
 - **Paper:** [MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset](https://arxiv.org/abs/2504.07744)
 
 
@@ -65,7 +66,7 @@ The dataset includes frames extracted from drone videos captured during five dis
 Multiple sessions may occur on the same day but in different locations or targeting different animal groups. During each session, multiple drone videos were recorded to capture animals in their natural habitat under varying environmental conditions.
 
 ## Dataset Structure
-```​
+```
 /dataset/
     classes.txt
     session_1/
@@ -128,9 +129,9 @@ Multiple sessions may occur on the same day but in different locations or target
 ```
 
 ### Data Instances
-All images are names <video_id>_<frame_number>.jpg, each within a folder named for the date of the session. The annotations are in YOLO format and are stored in a corresponding .txt file with the same name as the image. 2025-01-31 and 2025-02-01 are the two days of data collection, with a total of 7 sessions. 2025-01-31 has 5 sessions and 2025-02-01 has 2 sessions. 
+All images are named `<video_id>_<frame_number>.jpg`, under the particular session and full video to which they belong; these can be matched to dates based on the table above. The annotations are in YOLO format and are stored in a corresponding `.txt` file with the same name as the image. 2025-01-31 and 2025-02-01 are the two days of data collection, with a total of 7 sessions. 2025-01-31 has 5 sessions and 2025-02-01 has 2 sessions. 
 
-Note on data partitions: HuggingFace limits folders to 10,000 files per folder, so each video file is further divided into partitions of 10,000 files. The partition folders are named `partition_1`, `partition_2`, etc.
+Note on data partitions: Hugging Face limits folders to 10,000 files per folder, so each video file is further divided into partitions of 10,000 files. The partition folders are named `partition_1`, `partition_2`, etc.
 
 
 ### Data Fields
@@ -142,7 +143,7 @@ Note on data partitions: HuggingFace limits folders to 10,000 files per folder,
   - `3`: dog
 
 Note: only zebras appear in this dataset; other class labels are included to be consistent across MMLA data collected at other locations, 
-see the data from [Mpala Research Center](https://huggingface.co/datasets/imageomics/mmla_mpala) and [The Wilds](https://huggingface.co/datasets/imageomics/mmla_wilds) datasets.
+see the [MMLA data from Mpala Research Center](https://huggingface.co/datasets/imageomics/mmla_mpala) and [The Wilds MMLA dataset](https://huggingface.co/datasets/imageomics/mmla_wilds).
 
 
 **frame_id.txt**:
@@ -152,11 +153,10 @@ see the data from [Mpala Research Center](https://huggingface.co/datasets/imageo
   - `width`: Width of the bounding box (normalized to [0, 1])
   - `height`: Height of the bounding box (normalized to [0, 1])
 
-<!-- ### Data Splits
-[More Information Needed] -->
-<!--
-Give your train-test splits for benchmarking; could be as simple as "split is indicated by the `split` column in the metadata file: `train`, `val`, or `test`." Or perhaps this is just the training dataset and other datasets were used for testing (you may indicate which were used).
--->
+### Data Splits
+
+This dataset was used in conjunction with the other two [MMLA datasets](https://huggingface.co/collections/imageomics/mmla) for both training and testing the [MMLA YOLO model](https://huggingface.co/imageomics/mmla#training-details).
+
 
 ## Dataset Creation
 
@@ -227,7 +227,7 @@ This section is meant to convey recommendations with respect to the bias, risk,
 
 ## Licensing Information
 
-This dataset has been marked as dedicated to the public domain by applying the [CC0-1.0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). 
+This dataset has been marked as dedicated to the public domain by applying the [CC0-1.0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). We ask that you cite the dataset and paper using the below citations if you make use of it in your research.
 
 
 ## Citation
@@ -235,7 +235,7 @@ This dataset has been marked as dedicated to the public domain by applying the [
 **BibTeX:**
 
 **Data**
-```​
+```
 @misc{mmla_opc,
   author = {Kline, Jenna and 
             Nguyen Ngoc, Dat and
@@ -266,7 +266,7 @@ This dataset has been marked as dedicated to the public domain by applying the [
 ```
 
 **Paper**
-```​
+```
 @misc{kline2025mmla,
       title={MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset}, 
       author={Jenna Kline and Samuel Stevens and Guy Maalouf and Camille Rondeau Saint-Jean and Dat Nguyen Ngoc and Majid Mirmehdi and David Guerin and Tilo Burghardt and Elzbieta Pastucha and Blair Costelloe and Matthew Watson and Thomas Richardson and Ulrik Pagh Schultz Lundquist},