Clarifying updates, reformatting (#2)

- Clarifying updates, reformatting (723401e3c33526a91286eacc5c15e9ee1654047b)
- clarify detection variable definitions (54459ff36a31fe23f3c8163068e7b79b969b7f30)


Co-authored-by: Elizabeth Campolongo <egrace479@users.noreply.huggingface.co>

README.md

@@ -20,12 +20,15 @@ tags:
 - Mpala Research Centre
 language:
 - en
-pretty_name: kabr-worked-examples
+pretty_name: "KABR Worked Examples"
 size_categories:
 - 1K<n<10K
 ---
 
-# Dataset Card for kabr-worked-example
+# Dataset Card for KABR Worked Examples
+
+This dataset is comprised of manually annotated bounding box detections, mini-scenes, behavior annotations, and associated telemetry 
+for three drone video sessions that were used for [kabr-tools](https://github.com/Imageomics/kabr-tools) case studies. Drone video was collected at [Mpala Research Centre](https://mpala.org/) in January 2023; please see the [full video dataset](https://huggingface.co/datasets/imageomics/kabr-full-video) for more information on original video context.
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -39,8 +42,7 @@ size_categories:
   - [A. Detections (CVAT "tracks" XML)](#a-detections-cvat-tracks-xml)
   - [B. Behavior CSV (auto labels; one file per source video)](#b-behavior-csv-auto-labels-one-file-per-source-video)
   - [C. Mini-scene metadata JSON (per source video)](#c-mini-scene-metadata-json-per-source-video)
-  - [D. Actions folder (per-clip predictions; if present)](#d-actions-folder-per-clip-predictions-if-present)
-  - [E. Telemetry CSV (Airdata export)](#e-telemetry-csv-airdata-export)
+  - [D. Telemetry CSV (Airdata export)](#e-telemetry-csv-airdata-export)
 - [Dataset Creation](#dataset-creation)
   - [Curation Rationale](#curation-rationale)
   - [Source Data](#source-data)
@@ -57,335 +59,66 @@ size_categories:
 
 
 ## Dataset Details
-Manually annotated bounding box detections, mini-scenes, behavior annotations, and associated telemetry 
-for three sessions used for kabr-tools case studies.
 
 ### Dataset Description
 
 - **Curated by:** Alison Zhong and Jenna Kline
-- **Homepage:** 
+- **Homepage:** https://imageomics.github.io/KABR
 - **Repository:** https://github.com/Imageomics/kabr-tools
 - **Paper:** kabr-tools (manuscript in preparation)
 
-Annotations were created to evaluate the kabr-tools pipeline and conduct case studies on Grevy's landscape of fear and inter-species spatial distribution. Annotations include manual detections and tracks, mini-scenes cut from source videos, behavior annotations from an X3D action recognition model, and associated drone telemetry data. The detections contains bounding box coordinates, image file names, and class labels for each annotated animal. Annotations were created using CVAT to manually draw bounding boxes around animals in a selection of raw drone videos. The annotations were then exported as xml files and used to create the provided mini-scenes. The KABR X3D model was used to label the mini-scenes with predicted behaviors. Telemetry data was exported from Airdata.
+Annotations were created to evaluate the [kabr-tools pipeline](https://github.com/Imageomics/kabr-tools) and conduct case studies on Grevy's landscape of fear and inter-species spatial distribution. Annotations include manual detections and tracks, mini-scenes cut from source videos, behavior annotations from an X3D action recognition model, and associated drone telemetry data. The detections contain bounding box coordinates, image file names, and class labels for each annotated animal. Annotations were created using [CVAT](https://www.cvat.ai/) to manually draw bounding boxes around animals in a selection of raw drone videos. The annotations were then exported as xml files and used to create the provided mini-scenes. The [KABR X3D model](https://huggingface.co/imageomics/x3d-kabr-kinetics) was used to label the mini-scenes with predicted behaviors. Telemetry data was exported from [Airdata](https://airdata.com/).
 
 ### Session Summary
 
-| Session | Date Collected | Demographic Information and Habitat | Video File IDs in Session |
-|---------|---------------|--------------|---------|
-| `1` | 2023-01-18 | 2 Adult male Grevy's zebra in an open plain| DJI_0068, DJI_0069, DJI_0070, DJI_0071 |
-| `2` | 2023-01-20 |  5 Grevy's zebra in a semi-open habitat along a roadway| DJI_0142, DJI_0143, DJI_0144, DJI_0145, DJI_0146, DJI_0147 |
-| `3` | 2023-01-21 |  Mixed herd of 3 reticulated giraffe, 2 plains zebras and 11 Grevy's zebras in a closed habitat with dense vegetation near Mo Kenya| DJI_0206, DJI_0208, DJI_0210, DJI_0211 |
+| Session | Date Collected | Demographic Information and Habitat | Video File IDs in Session | Session Source Videos (link) |
+|---------|---------------|--------------|---------|---------|
+| `ex-1` | 2023-01-18 | 2 Adult male Grevy's zebras in an open plain| `DJI_0068`, `DJI_0069`, `DJI_0070`, `DJI_0071` | [imageomics/kabr-full-video/18_01_2023_session_7/](https://huggingface.co/datasets/imageomics/kabr-full-video/tree/main/18_01_2023_session_7) |
+| `ex-2` | 2023-01-20 |  5 Grevy's zebras in a semi-open habitat along a roadway| `DJI_0142`, `DJI_0143`, `DJI_0144`, `DJI_0145`, `DJI_0146`, `DJI_0147` | [imageomics/kabr-full-video/20_01_2023_session_3/](https://huggingface.co/datasets/imageomics/kabr-full-video/tree/main/20_01_2023_session_3) |
+| `ex-3` | 2023-01-21 |  Mixed herd of 3 reticulated giraffes, 2 plains zebras and 11 Grevy's zebras in a closed habitat with dense vegetation near Mo Kenya| `DJI_0206`, `DJI_0208`, `DJI_0210`, `DJI_0211` | [imageomics/kabr-full-video/21_01_2023_session_5/](https://huggingface.co/datasets/imageomics/kabr-full-video/tree/main/21_01_2023_session_5) |
+
+**Note:** Session numbers (as used in identifiers) are based on _all_ KABR video sessions, while we focus in this dataset on Sessions 7, 5, and 3, which we label as Sessions ex-1, ex-2, and ex-3, respectively.
 
 ## Dataset Structure
 ```text
 
-├── behavior
+├── behavior/
 │   ├── 18_01_2023_session_7-DJI_0068.csv
 │   ├── 18_01_2023_session_7-DJI_0069.csv
-│   ├── 18_01_2023_session_7-DJI_0070.csv
-│   ├── 18_01_2023_session_7-DJI_0071.csv
-│   ├── 20_01_2023_session_3-DJI_0142.csv
-│   ├── 20_01_2023_session_3-DJI_0143.csv
-│   ├── 20_01_2023_session_3-DJI_0144.csv
-│   ├── 20_01_2023_session_3-DJI_0145.csv
-│   ├── 20_01_2023_session_3-DJI_0146.csv
-│   ├── 20_01_2023_session_3-DJI_0147.csv
-│   ├── 21_01_2023_session_5-DJI_0206.csv
-│   ├── 21_01_2023_session_5-DJI_0208.csv
-│   ├── 21_01_2023_session_5-DJI_0210.csv
+│   ├── ...
 │   ├── 21_01_2023_session_5-DJI_0211.csv
 │   └── 21_01_2023_session_5-DJI_0212.csv
-├── detections
+├── detections/
 │   ├── 18_01_2023_session_7-DJI_0068.xml
 │   ├── 18_01_2023_session_7-DJI_0069.xml
-│   ├── 18_01_2023_session_7-DJI_0070.xml
-│   ├── 18_01_2023_session_7-DJI_0071.xml
-│   ├── 20_01_2023_session_3-DJI_0142.xml
-│   ├── 20_01_2023_session_3-DJI_0143.xml
-│   ├── 20_01_2023_session_3-DJI_0144.xml
-│   ├── 20_01_2023_session_3-DJI_0145.xml
-│   ├── 20_01_2023_session_3-DJI_0146.xml
-│   ├── 20_01_2023_session_3-DJI_0147.xml
-│   ├── 21_01_2023_session_5-DJI_0206.xml
-│   ├── 21_01_2023_session_5-DJI_0208.xml
-│   ├── 21_01_2023_session_5-DJI_0210.xml
+│   ├── ...
 │   ├── 21_01_2023_session_5-DJI_0211.xml
 │   └── 21_01_2023_session_5-DJI_0212.xml
-├── mini_scenes
-│   ├── 18_01_2023_session_7-DJI_0068
+├── mini_scenes/
+│   ├── 18_01_2023_session_7-DJI_0068/
 │   │   ├── 0.mp4
 │   │   ├── 1.mp4
-│   │   ├── DJI_0068.mp4
-│   │   └── metadata
+│   │   └── metadata/
 │   │       ├── DJI_0068.jpg
 │   │       ├── DJI_0068_metadata.json
 │   │       └── DJI_0068_tracks.xml
-│   ├── 18_01_2023_session_7-DJI_0069
+│   ├── 18_01_2023_session_7-DJI_0069/
 │   │   ├── 0.mp4
 │   │   ├── 1.mp4
-│   │   ├── DJI_0069.mp4
-│   │   └── metadata
+│   │   └── metadata/
 │   │       ├── DJI_0069.jpg
 │   │       ├── DJI_0069_metadata.json
 │   │       └── DJI_0069_tracks.xml
-│   ├── 18_01_2023_session_7-DJI_0070
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── DJI_0070.mp4
-│   │   └── metadata
-│   │       ├── DJI_0070.jpg
-│   │       ├── DJI_0070_metadata.json
-│   │       └── DJI_0070_tracks.xml
-│   ├── 18_01_2023_session_7-DJI_0071
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── DJI_0071.mp4
-│   │   └── metadata
-│   │       ├── DJI_0071.jpg
-│   │       ├── DJI_0071_metadata.json
-│   │       └── DJI_0071_tracks.xml
-│   ├── 20_01_2023_session_3-DJI_0142
-│   │   ├── 0.mp4
-│   │   ├── 10.mp4
-│   │   ├── 11.mp4
-│   │   ├── 1.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── 6.mp4
-│   │   ├── 7.mp4
-│   │   ├── 8.mp4
-│   │   ├── 9.mp4
-│   │   ├── actions
-│   │   ├── DJI_0142.mp4
-│   │   └── metadata
-│   │       ├── DJI_0142.jpg
-│   │       ├── DJI_0142_metadata.json
-│   │       └── DJI_0142_tracks.xml
-│   ├── 20_01_2023_session_3-DJI_0143
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── actions
-│   │   ├── DJI_0143.mp4
-│   │   └── metadata
-│   │       ├── DJI_0143.jpg
-│   │       ├── DJI_0143_metadata.json
-│   │       └── DJI_0143_tracks.xml
-│   ├── 20_01_2023_session_3-DJI_0144
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── actions
-│   │   ├── DJI_0144.mp4
-│   │   └── metadata
-│   │       ├── DJI_0144.jpg
-│   │       ├── DJI_0144_metadata.json
-│   │       └── DJI_0144_tracks.xml
-│   ├── 20_01_2023_session_3-DJI_0145
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── actions
-│   │   ├── DJI_0145.mp4
-│   │   └── metadata
-│   │       ├── DJI_0145.jpg
-│   │       ├── DJI_0145_metadata.json
-│   │       └── DJI_0145_tracks.xml
-│   ├── 20_01_2023_session_3-DJI_0146
-│   │   ├── 0.mp4
-│   │   ├── 1.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── actions
-│   │   ├── DJI_0146.mp4
-│   │   └── metadata
-│   │       ├── DJI_0146.jpg
-│   │       ├── DJI_0146_metadata.json
-│   │       └── DJI_0146_tracks.xml
-│   ├── 21_01_2023_session_5-DJI_0206
-│   │   ├── 0.mp4
-│   │   ├── 10.mp4
-│   │   ├── 11.mp4
-│   │   ├── 12.mp4
-│   │   ├── 13.mp4
-│   │   ├── 14.mp4
-│   │   ├── 15.mp4
-│   │   ├── 16.mp4
-│   │   ├── 17.mp4
-│   │   ├── 18.mp4
-│   │   ├── 19.mp4
-│   │   ├── 1.mp4
-│   │   ├── 20.mp4
-│   │   ├── 21.mp4
-│   │   ├── 22.mp4
-│   │   ├── 23.mp4
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-│   │   ├── 26.mp4
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-│   │   ├── 2.mp4
-│   │   ├── 30.mp4
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-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── 6.mp4
-│   │   ├── 7.mp4
-│   │   ├── 8.mp4
-│   │   ├── 9.mp4
-│   │   ├── DJI_0206.mp4
-│   │   └── metadata
-│   │       ├── DJI_0206.jpg
-│   │       ├── DJI_0206_metadata.json
-│   │       └── DJI_0206_tracks.xml
-│   ├── 21_01_2023_session_5-DJI_0208
+│   ├── ...
+│   ├── 21_01_2023_session_5-DJI_0211/
 │   │   ├── 0.mp4
-│   │   ├── 10.mp4
-│   │   ├── 11.mp4
-│   │   ├── 12.mp4
-│   │   ├── 13.mp4
-│   │   ├── 14.mp4
-│   │   ├── 15.mp4
-│   │   ├── 16.mp4
-│   │   ├── 17.mp4
-│   │   ├── 18.mp4
-│   │   ├── 19.mp4
-│   │   ├── 1.mp4
-│   │   ├── 20.mp4
-│   │   ├── 21.mp4
-│   │   ├── 22.mp4
-│   │   ├── 23.mp4
-│   │   ├── 24.mp4
-│   │   ├── 25.mp4
-│   │   ├── 26.mp4
-│   │   ├── 27.mp4
-│   │   ├── 28.mp4
-│   │   ├── 29.mp4
-│   │   ├── 2.mp4
-│   │   ├── 30.mp4
-│   │   ├── 31.mp4
-│   │   ├── 32.mp4
+│   │   ├── ...
 │   │   ├── 33.mp4
-│   │   ├── 34.mp4
-│   │   ├── 35.mp4
-│   │   ├── 36.mp4
-│   │   ├── 37.mp4
-│   │   ├── 38.mp4
-│   │   ├── 39.mp4
-│   │   ├── 3.mp4
-│   │   ├── 40.mp4
-│   │   ├── 41.mp4
-│   │   ├── 42.mp4
-│   │   ├── 43.mp4
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-│   │   ├── 47.mp4
-│   │   ├── 48.mp4
-│   │   ├── 49.mp4
-│   │   ├── 4.mp4
-│   │   ├── 50.mp4
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-│   │   ├── 55.mp4
-│   │   ├── 5.mp4
-│   │   ├── 6.mp4
-│   │   ├── 7.mp4
-│   │   ├── 8.mp4
-│   │   ├── 9.mp4
-│   │   ├── DJI_0208.mp4
-│   │   └── metadata
-│   │       ├── DJI_0208.jpg
-│   │       ├── DJI_0208_metadata.json
-│   │       └── DJI_0208_tracks.xml
-│   ├── 21_01_2023_session_5-DJI_0210
-│   │   ├── 0.mp4
-│   │   ├── 10.mp4
-│   │   ├── 11.mp4
-│   │   ├── 12.mp4
-│   │   ├── 13.mp4
-│   │   ├── 14.mp4
-│   │   ├── 15.mp4
-│   │   ├── 16.mp4
-│   │   ├── 17.mp4
-│   │   ├── 18.mp4
-│   │   ├── 19.mp4
-│   │   ├── 1.mp4
-│   │   ├── 20.mp4
-│   │   ├── 21.mp4
-│   │   ├── 22.mp4
-│   │   ├── 23.mp4
-│   │   ├── 24.mp4
-│   │   ├── 2.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── 6.mp4
-│   │   ├── 7.mp4
-│   │   ├── 8.mp4
-│   │   ├── 9.mp4
-│   │   ├── DJI_0210.mp4
-│   │   └── metadata
-│   │       ├── DJI_0210.jpg
-│   │       ├── DJI_0210_metadata.json
-│   │       └── DJI_0210_tracks.xml
-│   ├── 21_01_2023_session_5-DJI_0211
-│   │   ├── 0.mp4
-│   │   ├── 10.mp4
-│   │   ├── 11.mp4
-│   │   ├── 12.mp4
-│   │   ├── 13.mp4
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-│   │   ├── 19.mp4
-│   │   ├── 1.mp4
-│   │   ├── 20.mp4
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-│   │   ├── 28.mp4
-│   │   ├── 29.mp4
-│   │   ├── 2.mp4
-│   │   ├── 30.mp4
-│   │   ├── 31.mp4
-│   │   ├── 32.mp4
-│   │   ├── 33.mp4
-│   │   ├── 3.mp4
-│   │   ├── 4.mp4
-│   │   ├── 5.mp4
-│   │   ├── 6.mp4
-│   │   ├── 7.mp4
-│   │   ├── 8.mp4
-│   │   ├── 9.mp4
-│   │   ├── DJI_0211.mp4
-│   │   └── metadata
+│   │   └── metadata/
 │   │       ├── DJI_0211.jpg
 │   │       ├── DJI_0211_metadata.json
 │   │       └── DJI_0211_tracks.xml
-│   └── 21_01_2023_session_5-DJI_0212
+│   └── 21_01_2023_session_5-DJI_0212/
 │       ├── 0.mp4
 │       ├── 10.mp4
 │       ├── 11.mp4
@@ -401,38 +134,39 @@ Annotations were created to evaluate the kabr-tools pipeline and conduct case st
 │       ├── 7.mp4
 │       ├── 8.mp4
 │       ├── 9.mp4
-│       ├── DJI_0212.mp4
-│       └── metadata
+│       └── metadata/
 │           ├── DJI_0212.jpg
 │           ├── DJI_0212_metadata.json
 │           └── DJI_0212_tracks.xml
 ├── README.md
-└── telemetry
-    ├── Jan-18th-2023-12-47PM-Flight-Airdata.csv
-    ├── Jan-20th-2023-12-58PM-Flight-Airdata.csv
-    └── Jan-21st-2023-02-49PM-Flight-Airdata.csv
+└── telemetry/
+    ├── 18_01_2023-session_7-Flight_Airdata.csv
+    ├── 20_01_2023-session_3-Flight_Airdata.csv
+    └── 21_01_2023-session_5-Flight_Airdata.csv
 
 ```
 
+**Note:** Each video has an associated `video_id`, which is defined as `<DD>_01_2023_session_<session-number>-DJI_<video-number>` (ex: `21_01_2023_session_5-DJI_0212`). This ID is used to identify and link all (meta)data associated with that video.
+
 ## What each file/folder is for
 
 | Path / Pattern | Purpose |
 |---|---|
-| `detections/*.xml` | **Manual detections/tracks** per source video (CVAT “tracks” XML). One `<track>` per animal across frames; used to cut mini-scenes. |
+| `behavior/<video_id>.csv` | **Per-video roll-ups** of X3D behavior predictions. CSV containing one row per mini-scene clip with label + references (video, track, frame). |
+| `detections/<video_id>.xml` | **Manual detections/tracks** per source video (CVAT “tracks” XML). One `<track>` per animal across frames; used to cut mini-scenes. |
 | `mini_scenes/<video_id>/DJI_XXXX.mp4` | The **source video** referenced by detections for that `<video_id>`. |
 | `mini_scenes/<video_id>/<k>.mp4` | **Mini-scenes** (short clips) cut from the source video based on detection tracks (`0.mp4`, `1.mp4`, …). |
 | `mini_scenes/<video_id>/metadata/DJI_XXXX_tracks.xml` | Copy of the **CVAT tracks** used to generate the mini-scenes (provenance). |
 | `mini_scenes/<video_id>/metadata/DJI_XXXX_metadata.json` | **Video-level metadata** (session/date, FPS, resolution, timing, etc.). |
 | `mini_scenes/<video_id>/metadata/DJI_XXXX.jpg` | **Thumbnail/keyframe** for quick preview. |
-| `mini_scenes/<video_id>/actions/` | **Per-clip auto behavior labels** from the X3D action model (CSV or JSON; presence varies by video). |
-| `behavior/*.csv` | **Per-video roll-ups** of X3D behavior predictions. One row per mini-scene clip with label + references (and optional timing/confidence). |
-| `telemetry/*Flight-Airdata.csv` | **Drone flight logs** (Airdata export) for the corresponding sessions (timing, altitude, battery, etc.). |
+| `mini_scenes/<video_id>/actions/` | **Per-clip auto behavior labels** from the [X3D action model](https://huggingface.co/imageomics/x3d-kabr-kinetics) (CSV or JSON; presence varies by video). |
+| `telemetry/<DD>_01_2023-session_<session-number>-Flight_Airdata.csv` | **Drone flight logs** ([Airdata](https://airdata.com/) export) for the corresponding sessions (timing, altitude, battery, etc.). |
 | `README.md` | Repository-level notes and usage tips. |
 
 
 ## Data instances
 
-- **Detection instance (XML):** one `<track>` spans frames; each `<box>` is a frame-level bounding box with coordinates and flags.  
+- **Detection instance (XML):** one `<track>` spans all frames of a video; each `<box>` is a frame-level bounding box with coordinates and flags.  
 - **Mini-scene instance (MP4):** a short clip indexed by file name (`k.mp4`) under `mini_scenes/<video_id>/`.  
 - **Behavior instance (CSV row):** one mini-scene with **X3D-predicted behavior** and references to the clip (plus optional confidence/timing).  
 - **Telemetry instance (CSV row):** one flight-log record from Airdata with timestamped vehicle context.  
@@ -444,30 +178,30 @@ Annotations were created to evaluate the kabr-tools pipeline and conduct case st
 
 | Element / Attribute               | Type              | Example       | Meaning |
 | --- | --- | --- | --- |
-| `/annotations/version`           | string            | `1.1`         | Annotation file/schema version. |
-| `/annotations/track@id`          | integer           | `0`           | Unique id for a tracked object. |
+| `/annotations/version`           | string            | `1.1`         | Annotation file (XML) version. |
+| `/annotations/track@id`          | integer           | `0`           | Unique id for a tracked object _within_ the video. |
 | `/annotations/track@label`       | string            | `Grevy`       | Class/species label. |
-| `/annotations/track@source`      | string            | `manual`      | How the annotation was created. |
+| `/annotations/track@source`      | string            | `manual`      | How the annotation was created. These are all `manual`. |
 | `/annotations/track/box@frame`   | int (0-based)     | `0,1,2,…`     | Frame index. |
 | `/annotations/track/box@outside` | enum {`0`,`1`}    | `0`           | `0` present; `1` not visible. |
-| `/annotations/track/box@occluded`| enum {`0`,`1`}    | `0`           | Occlusion flag. |
-| `/annotations/track/box@keyframe`| enum {`0`,`1`}    | `1`           | Keyframe marker. |
-| `/annotations/track/box@xtl`     | float (px)        | `2342.00`     | X of top-left. |
-| `/annotations/track/box@ytl`     | float (px)        | `2427.00`     | Y of top-left. |
-| `/annotations/track/box@xbr`     | float (px)        | `2530.00`     | X of bottom-right. |
-| `/annotations/track/box@ybr`     | float (px)        | `2623.00`     | Y of bottom-right. |
+| `/annotations/track/box@occluded`| enum {`0`,`1`}    | `0`           | Occlusion flag (`1` indicates the subject is occluded). |
+| `/annotations/track/box@keyframe`| enum {`0`,`1`}    | `1`           | Keyframe marker. Every 10th frame is considered a "keyframe" (CVAT default setting). |
+| `/annotations/track/box@xtl`     | float (px)        | `2342.00`     | X coordinate of top-left corner. |
+| `/annotations/track/box@ytl`     | float (px)        | `2427.00`     | Y coordinate of top-left corner. |
+| `/annotations/track/box@xbr`     | float (px)        | `2530.00`     | X coordinate of bottom-right corner. |
+| `/annotations/track/box@ybr`     | float (px)        | `2623.00`     | Y coordinate of bottom-right corner. |
 | `/annotations/track/box@z_order` | integer           | `0`           | Drawing order. |
 
 
 ### B. Behavior CSV (auto labels; one file per source video)
 
-> **Note:** Column names may vary slightly by export; use the header in each CSV as ground truth.
+**Note:** Column names may vary slightly by export; use the header in each CSV as ground truth.
 
 | Column (typical) | Example | Meaning |
 |---|---|---|
 | `clip_path` or `clip_id` | `mini_scenes/21_01_2023_session_5-DJI_0208/33.mp4` | Relative path to the mini-scene clip. |
 | `source_video` | `DJI_0208.mp4` | Name of the parent/source video. |
-| `video_id` | `21_01_2023_session_5-DJI_0208` | Folder/video identifier. |
+| `video_id` | `21_01_2023_session_5-DJI_0208` | Folder/video identifier. This ID is used to identify and link all (meta)data associated with that source video. |
 | `clip_index` | `33` | Index of the clip within the video folder. |
 | `behavior` | `walking` | X3D-predicted action/behavior label. |
 | `confidence` | `0.92` | Model confidence/probability (if provided). |
@@ -475,7 +209,7 @@ Annotations were created to evaluate the kabr-tools pipeline and conduct case st
 | `end_frame` | `1450` | Last frame of the segment (if provided). |
 | `start_time` | `00:00:41.2` | Segment start time (if provided). |
 | `end_time` | `00:00:48.8` | Segment end time (if provided). |
-| `species` | `Grevy` | Species label (if propagated/available). |
+| `species` | `Grevy` | Species label (if propagated/available). Only three potential labels: `Grevy`, `Plain Zebra`, or `Giraffe`. |
 | `notes` | `—` | Free-text notes or flags (optional). |
 | `model` | `x3d` | Model identifier used to label. |
 | `model_version` | `x3d_m` | Specific checkpoint/version tag (optional). |
@@ -483,76 +217,66 @@ Annotations were created to evaluate the kabr-tools pipeline and conduct case st
 
 ### C. Mini-scene metadata JSON (per source video)
 
-> **Typical keys** (presence may vary):
+**Typical keys** (presence may vary):
 
 | Key | Example | Meaning |
 |---|---|---|
-| `video_id` | `21_01_2023_session_5-DJI_0208` | Folder/video identifier. |
+| `video_id` | `21_01_2023_session_5-DJI_0208` | Folder/video identifier. This ID is used to identify and link all (meta)data associated with that source video. |
 | `source_video` | `DJI_0208.mp4` | Original MP4 filename. |
-| `session_date` | `2023-01-21` | Capture date. |
+| `session_date` | `2023-01-21` | Capture date (`YYYY-MM-DD`). |
 | `session_id` | `session_5` | Field session tag. |
-| `fps` | `29.97` | Frames per second. |
-| `resolution` | `[3840, 2160]` | Width × height (px). |
+| `fps` | `29.97` | Frames per second of recording. |
+| `resolution` | `[3840, 2160]` | Width × height (px) (in list format). |
 | `duration_s` | `123.45` | Video duration (seconds). |
-| `timezone` | `Africa/Nairobi` | Local timezone of recording. |
+| `timezone` | `Africa/Nairobi` | Local timezone of recording (UTC+3). |
 | `generator` | `mini_scene_cutter@<git-sha>` | Tool/commit that wrote the metadata. |
 | `tracks_xml` | `DJI_0208_tracks.xml` | Provenance link to the CVAT tracks file. |
 
 
-### D. Actions folder (per-clip predictions; if present)
-
-- **CSV format (typical columns):** `clip_index`, `clip_path`, `behavior`, `confidence`, `model`, `model_version`.  
-- **JSON format (typical fields):** object per clip with `index`, `path`, `label`, `score`, `model`, `model_version`.  
-
+### D. Telemetry CSV (Airdata export)
 
-### E. Telemetry CSV (Airdata export)
-
-> Columns depend on Airdata export settings; common fields include:
+Columns depend on [Airdata](https://airdata.com/) export settings; common fields include:
 
 | Column (common) | Example | Meaning |
 |---|---|---|
 | `UTC Timestamp` | `2023-01-21 12:49:07` | Log timestamp (UTC). |
-| `Latitude` / `Longitude` | `0.28123`, `37.12345` | Aircraft location. |
-| `Altitude (m)` | `68.2` | Altitude above takeoff or MSL (per export). |
-| `AGL (m)` | `47.9` | Above-ground level (if provided). |
-| `Speed (m/s)` | `9.4` | Horizontal speed. |
+| `Latitude` , `Longitude` | `0.28123`, `37.12345` | Aircraft location in decimal degrees. |
+| `Altitude (m)` | `68.2` | Altitude (meters) above takeoff or MSL (per export). |
+| `AGL (m)` | `47.9` | Above-ground level (in meters, if provided). |
+| `Speed (m/s)` | `9.4` | Horizontal speed (meters per second). |
 | `Heading (deg)` | `135` | Yaw/heading. |
 | `Battery (%)` | `54` | Remaining battery percentage. |
-| `Flight Mode` | `P-GPS` | Autopilot mode. |
-| `Distance (m)` | `122.5` | Distance from home point. |
+| `FlyState` | `P-GPS` | This indicates high-level drone status, such as `Motors_Started`, `Assisted_Takeoff`, `P-GPS` (positioning-gps mode), `Landing`. |
+| `Distance (m)` | `122.5` | Distance from home point (in meters). Specifically, Distance = current GPS - home point GPS. |
 
 
 
 ## Dataset Creation
 
 ### Curation Rationale
-Created to evaluate kabr-tools pipeline and conduct case studies on Grevy's landscape of fear and inter-species spatial distribution.
+Created to evaluate [kabr-tools](https://github.com/Imageomics/kabr-tools) pipeline and conduct case studies on Grevy's landscape of fear and inter-species spatial distribution.
 
 ### Source Data
 
 <!-- This section describes the source data (e.g., news text and headlines, social media posts, translated sentences, ...). As well as an original source it was created from (e.g., sampling from Zenodo records, compiling images from different aggregators, etc.) -->
 
 #### Data Collection and Processing
-Data collected at Mpala Research Centre, Kenya, in January 2023. The data was collected using a DJI Air 2S drone and manually annotated using CVAT. The annotations were exported as xml files.
+Data collected at [Mpala Research Centre](https://mpala.org/), Kenya, in January 2023. The data was collected using a DJI Air 2S drone and manually annotated using [CVAT](https://www.cvat.ai/). The annotations were exported as XML files.
 
 #### Who are the source data producers?
-See citation for kabr-full-video dataset - https://huggingface.co/datasets/imageomics/KABR-full-videos 
+[Imageomics/KABR-full-video dataset authors](https://huggingface.co/datasets/imageomics/kabr-full-video/blob/main/README.md#authors).
 
 
 ### Annotations
-<!-- 
-If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. 
-
-Ex: We standardized the taxonomic labels provided by the various data sources to conform to a uniform 7-rank Linnean structure. (Then, under annotation process, describe how this was done: Our sources used different names for the same kingdom (both _Animalia_ and _Metazoa_), so we chose one for all (_Animalia_). -->
 
 #### Annotation process
-CVAT was used to manually annotate the bounding boxes around animals in the videos. The annotations were then exported as xml files to create mini-scenes using tracks_extractor.py. The mini-scenes were then labeled with predicted behaviors using the KABR X3D action recognition model using the miniscene2behavior.py.
+A local instance of [CVAT](https://www.cvat.ai/) was used to manually annotate the bounding boxes around animals in the videos. The annotations were then exported as XML files to create mini-scenes using [`tracks_extractor.py`](https://github.com/Imageomics/kabr-tools/blob/master/src/kabr_tools/tracks_extractor.py). The mini-scenes were then labeled with predicted behaviors using the [KABR X3D action recognition model](https://huggingface.co/imageomics/x3d-kabr-kinetics) using the [`miniscene2behavior.py`](https://github.com/Imageomics/kabr-tools/blob/master/src/kabr_tools/miniscene2behavior.py).
 
 #### Who are the annotators?
 Alison Zhong and Jenna Kline
 
 ### Personal and Sensitive Information
-People trimmed from the videos before annotation. 
+Videos were trimmed (as needed) to remove people before annotation. 
 Endangered species are included in the dataset, but no personal or sensitive information is included.
 
 
@@ -560,25 +284,25 @@ Endangered species are included in the dataset, but no personal or sensitive inf
 
 ### Intended Use Cases
 
-This dataset serves as a **worked example** for the kabr-tools pipeline and is specifically designed for:
-- **Pipeline demonstration**: Showing complete end-to-end processing from raw videos to behavioral annotations
-- **Method validation**: Evaluating automated detection and behavior recognition against manual annotations  
-- **Case study research**: Supporting specific research questions on Grevy's zebra landscape of fear and inter-species spatial distribution
-- **Educational purposes**: Teaching researchers how to use the kabr-tools pipeline with real data
-- **Reproducibility**: Providing a reference implementation with known inputs and outputs
+This dataset serves as a **worked example** for the [kabr-tools](https://github.com/Imageomics/kabr-tools) pipeline and is specifically designed for:
+- **Pipeline demonstration**: Showing complete end-to-end processing from raw videos to behavioral annotations.
+- **Method validation**: Evaluating automated detection and behavior recognition against manual annotations.
+- **Case study research**: Supporting specific research questions on Grevy's zebra landscape of fear and inter-species spatial distribution.
+- **Educational purposes**: Teaching researchers how to use the kabr-tools pipeline with real data.
+- **Reproducibility**: Providing a reference implementation with known inputs and outputs.
 
 ### Important Data Considerations
 
 **Limited scope**: This is a **demonstration dataset** with only 3 sessions and 15 video files, designed to illustrate methodology rather than provide comprehensive coverage.
 
-**Session heterogeneity**: Each session represents distinctly different scenarios:
-- **Session 1**: Minimal complexity (2 male Grevy's zebras, open habitat)
-- **Session 2**: Moderate complexity (5 Grevy's zebras, semi-open roadway habitat)  
-- **Session 3**: High complexity (mixed species, dense vegetation, 16 total animals)
+**Session heterogeneity**: Each example session represents distinctly different scenarios:
+- **Session ex-1**: Minimal complexity (2 male Grevy's zebras, open habitat)
+- **Session ex-2**: Moderate complexity (5 Grevy's zebras, semi-open roadway habitat)  
+- **Session ex-3**: High complexity (mixed species, dense vegetation, 16 total animals)
 
 **Processing completeness**: Not all videos have complete processing outputs - some lack `actions/` folders, reflecting real-world pipeline execution variability.
 
-**Annotation methodology**: Manual detections serve as ground truth, while behavior labels are X3D model predictions, not expert-validated behaviors.
+**Annotation methodology**: Manual detections serve as ground truth, while behavior labels are [X3D model](https://huggingface.co/imageomics/x3d-kabr-kinetics) predictions, not expert-validated behaviors.
 
 ### Bias, Risks, and Limitations
 
@@ -589,12 +313,12 @@ This dataset serves as a **worked example** for the kabr-tools pipeline and is s
 
 **Species representation bias**:
 - Heavily weighted toward Grevy's zebras (endangered species focus)
-- Giraffes only present in one session (Session 3)
+- Giraffes only present in one session (Session ex-3)
 - Plains zebras only in mixed-species context
 - May not represent typical behavioral patterns for each species
 
 **Habitat and temporal constraints**:
-- Single location (Mpala Research Centre, Kenya) 
+- Single location ([Mpala Research Centre](https://mpala.org/), Kenya) 
 - 3-day collection window (January 18-21, 2023)
 - Limited environmental and seasonal variability
 - Habitat types may not represent species' full range
@@ -607,7 +331,7 @@ This dataset serves as a **worked example** for the kabr-tools pipeline and is s
 
 **Methodological constraints**:
 - Manual annotations by only 2 annotators (potential inter-annotator variability)
-- CVAT tracking may have limitations in dense vegetation (Session 3)
+- CVAT tracking may have limitations in dense vegetation (Session ex-3)
 - Behavior model trained on different dataset, may not generalize perfectly
 
 ### Recommendations
@@ -615,16 +339,16 @@ This dataset serves as a **worked example** for the kabr-tools pipeline and is s
 **For pipeline evaluation and development**:
 - Use manual detections in `detections/*.xml` as ground truth for automated detection validation
 - Compare processing outputs across sessions to understand pipeline performance in different scenarios
-- Use Session 1 (simple) for initial testing, Session 3 (complex) for stress testing
+- Use Session ex-1 (simple) for initial testing, Session ex-3 (complex) for stress testing
 - Validate timestamp alignment between telemetry and video data before spatial analysis
 
 **For case study research**:
-- **Landscape of fear studies**: Focus on Grevy's zebra data from Sessions 1 and 2; use telemetry data to correlate spatial position with behaviors
-- **Inter-species analysis**: Use Session 3 mixed-species data; consider habitat complexity when interpreting interactions
+- **Landscape of fear studies**: Focus on Grevy's zebra data from Sessions ex-1 and ex-2; use telemetry data to correlate spatial position with behaviors
+- **Inter-species analysis**: Use Session ex-3 mixed-species data; consider habitat complexity when interpreting interactions
 - Account for small sample sizes in statistical analyses and interpretation
 
 **For educational use**:
-- Start with Session 1 data for learning pipeline basics
+- Start with Session ex-1 data for learning pipeline basics
 - Progress through sessions in order of increasing complexity
 - Use metadata files to understand processing provenance
 - Examine both successful and incomplete processing examples
@@ -643,8 +367,8 @@ This dataset serves as a **worked example** for the kabr-tools pipeline and is s
 
 ## References
 
-- Original KABR dataset: [https://huggingface.co/datasets/imageomics/KABR](https://huggingface.co/datasets/imageomics/KABR)
-- KABR raw videos: [https://huggingface.co/datasets/imageomics/KABR-full-videos](https://huggingface.co/datasets/imageomics/KABR-full-videos)
+- Original KABR mini-scene dataset: [https://huggingface.co/datasets/imageomics/KABR](https://huggingface.co/datasets/imageomics/KABR)
+- KABR raw videos (not processed for KABR mini-scene dataset): [https://huggingface.co/datasets/imageomics/KABR-full-videos](https://huggingface.co/datasets/imageomics/KABR-full-videos)
 - kabr-tools repository: [https://github.com/Imageomics/kabr-tools](https://github.com/Imageomics/kabr-tools)
 - Mpala Research Centre: [https://mpala.org/](https://mpala.org/)
 
@@ -661,9 +385,10 @@ This dataset is dedicated to the public domain for the benefit of scientific pur
 @misc{KABR_worked_example,
   author = {Zhong, Alison and Kline, Jenna and Kholiavchenko, Maksim and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Rosser, Neil and Stewart, Charles and Berger-Wolf, Tanya and Rubenstein, Daniel},
   title = {KABR Worked Example: Manually Annotated Detections and Behavioral Analysis for Kenyan Wildlife Pipeline Demonstration},
-  year = {2023},
+  year = {2025},
   url = {https://huggingface.co/datasets/imageomics/kabr-worked-example},
-  publisher = {Hugging Face}
+  publisher = {Hugging Face},
+  doi = { }
 }
 ```
 
@@ -682,15 +407,14 @@ This dataset is dedicated to the public domain for the benefit of scientific pur
 ```
 @article{kabr_tools_manuscript,
   title={kabr-tools: An Open-Source Pipeline for Automated Wildlife Behavior Analysis from Drone Videos},
-  author={Zhong, Alison and Kline, Jenna and [additional authors]},
+  author={Jenna Kline and Maksim Kholiavchenko and Samuel Stevens and Nina van Tiel and Namrata Banerji and Matthew Thompson and Elizabeth Campolongo and Michelle Ramirez and Alec Sheets and Alison Zhong and Sowbaranika Balasubramaniam and Isla Duporge and Jackson Miliko and Neil Rosser and Tanya Berger-Wolf and Charles V. Stewart and Daniel I. Rubenstein},
   journal={[Journal name]},
   year={[Year]},
   note={Manuscript in preparation}
 }
 ```
 
-Please also cite the original data sources:
-- Original KABR dataset: https://huggingface.co/datasets/imageomics/KABR
+Please also cite the original data source:
 - KABR raw videos: https://huggingface.co/datasets/imageomics/KABR-full-videos
 
 ## Contributions
@@ -699,7 +423,7 @@ This work was supported by the [Imageomics Institute](https://imageomics.org), w
 
 Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
-The data was collected at the [Mpala Research Centre](https://mpala.org/) in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
+The [raw data](https://huggingface.co/datasets/imageomics/KABR-full-videos) fed into the [KABR tools pipeline](https://github.com/Imageomics/kabr-tools) to produce this worked example was collected at the [Mpala Research Centre](https://mpala.org/) in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
 
 ### Dataset Creation Contributors
 
@@ -714,9 +438,11 @@ The data was collected at the [Mpala Research Centre](https://mpala.org/) in Ken
 
 **Mini-scene**: Short video clips (typically 5-10 seconds) extracted from source videos, centered on individual animals based on tracking annotations.
 
+**Mo Kenya**: A big hill to the north of Mpala.
+
 **CVAT**: Computer Vision Annotation Tool - open-source software used for manual video annotation and object tracking.
 
-**X3D**: 3D CNN architecture used for video-based action recognition, adapted for animal behavior classification in the KABR project.
+**X3D**: 3D CNN architecture used for video-based action recognition, adapted for animal behavior classification in the KABR project. Model: [Imageomics/X3D-KABR-Kinetics](https://huggingface.co/imageomics/x3d-kabr-kinetics).
 
 **Track**: A sequence of bounding boxes following a single animal across multiple video frames.
 
@@ -728,7 +454,7 @@ The data was collected at the [Mpala Research Centre](https://mpala.org/) in Ken
 
 For detailed usage instructions and code examples, see the [kabr-tools repository](https://github.com/Imageomics/kabr-tools). 
 
-For questions about the broader KABR project and related datasets, visit the [Imageomics Institute website](https://imageomics.org).
+For questions about the broader KABR project and related datasets, visit the [Imageomics Institute website](https://imageomics.org) and see the [KABR Collection](https://huggingface.co/collections/imageomics/kabr-664dff304d29e6cd7b8e1a00).
 
 This dataset is part of a larger effort to develop automated methods for wildlife monitoring and conservation using computer vision and machine learning techniques.
 
@@ -738,4 +464,4 @@ Jenna Kline
 
 ## Dataset Card Contact
 
-kline dot 377 at osu dot edu
+kline dot 377 at osu dot edu