fair2_compliant (#4)

- updating readme (48e689a6732c0b8523b6014b361435b899b644b6)

README.md

@@ -2,218 +2,738 @@
 license: cc0-1.0
 language:
 - en
-pretty_name: Kenyan Animal Behavior Remote Sensing (KABR) Drone Wildlife Monitoring Dataset
-description: "Synchronized drone telemetry, camera metadata, behavior annotations, and drone status data collected during wildlife monitoring operations. The data was captured using drones equipped with cameras to observe animal behavior in natural habitats."
+pretty_name: KABR Behavior Telemetry - FAIR² Drone Wildlife Monitoring Dataset
+
 task_categories:
-- object-detection
-- image-classification
+  - object-detection
+  - object-tracking
+  - video-classification
+
 tags:
-- biology
-- image
-- animals
-- CV
-- wildlife-monitoring
-- drone-imagery
-- telemetry
-- kabr
-- behavior
-size_categories: 10K<n<100K
+  - biology
+  - ecology
+  - wildlife-monitoring
+  - drone
+  - uav
+  - aerial-imagery
+  - animal-behavior
+  - zebra
+  - giraffe
+  - kenya
+  - savanna
+
+size_categories:
+  - 100K<n<1M
+
+description: Frame-level behavior telemetry dataset combining drone GPS tracks, camera metadata, animal detections, and behavior annotations from wildlife monitoring in Kenya. Supports detection, tracking, and behavioral analysis research.
+
+# FAIR² COMPLIANCE METADATA
+fair2_compliance:
+  findable:
+    doi: ""  # To be assigned
+    metadata_registry: ["DataCite", "GBIF"]
+  accessible:
+    open_access: true
+    authentication_required: false
+  interoperable:
+    standards: ["Darwin Core", "TDWG", "Humboldt Eco", "FAIR2"]
+  reusable:
+    license_clear: true
+    provenance_documented: true
+  ai_ready:
+    machine_readable: true
+    structured_annotations: true
+
+# DARWIN CORE COMPLIANCE
+darwin_core:
+  event_coverage:
+    start_date: "2023-01-11"
+    end_date: "2023-01-17"
+    decimal_latitude: 0.36
+    decimal_longitude: 36.88
+    coordinate_uncertainty_meters: 10
+    locality: "Mpala Research Centre, Laikipia County, Kenya"
+    habitat: "Open savanna and bushy woodland"
+
+  occurrence_info:
+    kingdom: "Animalia"
+    taxa_included: ["Equus grevyi", "Equus quagga", "Giraffa reticulata"]
+    sampling_protocol: "Aerial drone video survey at 20-50m altitude with continuous recording and frame-by-frame behavior annotation"
+
+# PLATFORM SPECIFICATIONS
+platform:
+  type: "UAV"
+  manufacturer: "DJI"
+  model: "Mavic Air 2, DJI Mini"
+  autonomy_mode: "manual"
+
+# SENSOR SPECIFICATIONS
+sensors:
+  - type: "RGB"
+    manufacturer: "DJI"
+    model: "Integrated camera"
+    resolution: [5472, 3078]
+
+# MISSION PARAMETERS
+mission:
+  altitude_m: 35
+  speed_ms: 3
+  telemetry_available: true
 ---
 
-# Dataset Card for KABR Drone Wildlife Monitoring Dataset
+# Dataset Card for KABR Behavior Telemetry
 
-This dataset consists of synchronized drone telemetry, camera metadata, behavior annotations, and drone status data collected during wildlife monitoring operations. The data was captured using drones equipped with cameras to observe animal behavior in natural habitats.
+**Synchronized frame-level telemetry, detections, and behavior annotations from drone wildlife monitoring in Kenya, enabling research on animal behavior analysis and optimal drone survey protocols.**
 
 ## Dataset Details
 
 ### Dataset Description
 
-- **Curated by:** Jenna M. Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, Christopher Stewart
-- **Language(s) (NLP):** English
-- **Homepage:** [KABR Project Site](https://imageomics.github.io/KABR/)
-- **Repository:** [kabr-tools](https://github.com/Imageomics/kabr-tools)
-- **Paper:** [Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs)](https://arxiv.org/abs/2407.16864)
+- **Curated by:** Jenna M. Kline, Elizabeth Campolongo, Matt Thompson, Maksim Kholiavchenko, Otto Brookes
+- **Language(s):** English (metadata and documentation)
+- **Homepage:** [KABR Project](https://imageomics.github.io/KABR/)
+- **Repository:** [kabr-behavior-telemetry](https://github.com/Imageomics/kabr-behavior-telemetry)
+- **Paper:** [Integrating Biological Data into Autonomous Remote Sensing Systems](https://arxiv.org/abs/2407.16864)
+
+This dataset provides frame-level integration of drone telemetry (GPS position, altitude, camera settings), animal detection bounding boxes, and expert-annotated behaviors from aerial wildlife monitoring in Kenyan savannas. Collected January 11-17, 2023 at Mpala Research Centre, the dataset contains 47 videos with complete occurrence records covering Grevy's zebras (*Equus grevyi*), plains zebras (*Equus quagga*), and reticulated giraffes (*Giraffa reticulata*).
+
+The dataset was developed to analyze optimal drone flight parameters for wildlife behavior research—correlating altitude, speed, and camera settings with data quality and animal disturbance levels. It implements Darwin Core biodiversity standards with Humboldt Eco extensions for ecological inventory data, ensuring interoperability with biodiversity databases like GBIF.
 
-This dataset integrates multiple streams of information collected during drone monitoring of wildlife in Kenya. It combines precise drone telemetry data (position, orientation, altitude), camera settings (ISO, shutter speed, focal length), wildlife annotations (bounding boxes, behavior classification), and drone system status information. The dataset was developed as part of research on integrating biological data into autonomous remote sensing systems for in situ imageomics, specifically focused on Kenyan animal behavior sensing with Unmanned Aerial Vehicles (UAVs). The dataset provides a comprehensive framework for analyzing animal behavior in correlation with drone positioning and camera settings, enabling research in fields such as wildlife monitoring, animal behavior analysis, and drone-based observation methodologies.
+Key features:
+- **47 complete video occurrence files** with ~10,000-66,000 frames each
+- **68 video-level Darwin Core events** with GPS bounds and temporal coverage
+- **17 session-level events** aggregating mission-level metadata
+- **Frame-synchronized data**: GPS coordinates, camera EXIF, detection boxes, behavior labels
+- **Behavior ethogram**: Walking, running, grazing, vigilance, social interactions, and more
+- **Multi-species coverage**: Three focal species across diverse habitats
 
-### Supported Tasks and Leaderboards
+### Supported Tasks and Applications
 
-This dataset supports several computer vision and behavioral analysis tasks:
+This dataset supports computer vision, ecological analysis, and autonomous systems research:
 
-1. Object detection and tracking of animals in drone footage
-2. Behavior classification and analysis
-3. Correlating animal behavior with drone positioning and movement
-4. Optimizing drone flight patterns for wildlife observation
-5. Camera parameter optimization for wildlife monitoring
+**🤖 Computer Vision Tasks:**
+- Object Detection (bounding boxes around animals)
+- Multi-Object Tracking (ID consistency across frames in mini-scenes)
+- Behavior Recognition (activity classification)
+- Scale-Invariant Detection (animals at varying altitudes/distances)
+
+**🌿 Ecological Applications:**
+- Behavioral time budgets and activity patterns
+- Habitat use analysis
+- Group size and composition estimation
+- Flight parameter impact on data quality
+- Animal response to drone presence
+
+**🚁 Drone Systems Research:**
+- Optimal altitude/speed/distance determination
+- Camera parameter optimization for wildlife
+- Detection performance vs. flight parameters
+- Disturbance minimization protocols
 
 ## Dataset Structure
 
-The dataset consists of a single CSV (`consolidated_metadata.csv`), which contains all information.
+### Directory Organization
+
+```
+kabr-behavior-telemetry/
+├── data/
+│   ├── occurrences/           # Frame-level occurrence records (47 videos)
+│   │   ├── 11_01_23-DJI_0977.csv
+│   │   ├── 11_01_23-DJI_0978.csv
+│   │   └── ...
+│   ├── video_events.csv       # Darwin Core Event records (68 videos)
+│   └── session_events.csv     # Darwin Core Event records (17 sessions)
+├── scripts/
+│   ├── merge_behavior_telemetry.py    # Generate occurrence files
+│   ├── update_video_events.py         # Add annotation file paths
+│   ├── add_event_times.py             # Extract temporal bounds
+│   └── add_gps_data.py                # Extract GPS statistics
+├── metadata/
+│   ├── DATA_DICTIONARY.md             # Complete field descriptions
+│   └── event_session_fields.csv       # Field metadata
+└── README.md
+```
 
 ### Data Instances
 
-Each row in the `consolidated_metadata.csv` file represents a single frame from a drone video with associated telemetry, annotations, and status information. The dataset contains [number] frames from [number] videos, collected between [dates]. <<<<--need to fill this in and link to source videos
+**Occurrence Files** (`data/occurrences/*.csv`):
+
+Each CSV contains frame-by-frame records for one video. Example from `11_01_23-DJI_0977.csv`:
+
+| Field | Example Value | Description |
+|-------|---------------|-------------|
+| `date` | 11_01_23 | Recording date |
+| `video_id` | DJI_0977 | Video identifier |
+| `frame` | 0 | Frame number |
+| `date_time` | 2023-01-11 16:04:03,114,286 | Timestamp with μs precision |
+| `latitude` | 0.399770 | GPS latitude (WGS84) |
+| `longitude` | 36.891217 | GPS longitude (WGS84) |
+| `altitude` | 20.2 | Altitude (m above sea level) |
+| `iso` | 100 | Camera ISO |
+| `xtl`, `ytl`, `xbr`, `ybr` | 1245, 678, 1389, 842 | Bounding box coordinates |
+| `id` | 12 | Mini-scene/track ID |
+| `behaviour` | walking | Behavior class |
+
+**Naming Convention:**
+```
+{date}_{video_id}.csv
+Example: 11_01_23-DJI_0977.csv
+         └─date─┘ └video_id┘
+```
+
+**Temporal Information:**
+- Date range: 2023-01-11 to 2023-01-17
+- Time coverage: Morning (09:38) to afternoon (16:28)
+- Dry season in Laikipia, Kenya
 
 ### Data Fields
 
-The metadata file contains 87 columns organized into four main categories:
-
-#### 1. Camera Settings and Frame Information
-- `frame`: Frame number in the video sequence
-- `id`: Unique identifier for the frame
-- `iso`: ISO setting of the camera
-- `shutter`: Shutter speed value
-- `fnum`: Aperture f-number
-- `ev`: Exposure value
-- `ct`: Color temperature
-- `color_md`: Color mode
-- `focal_len`: Focal length of the camera lens in mm
-- `dzoom_ratio`: Digital zoom ratio
-- `isPhoto`: Binary flag indicating if the frame is a photo
-- `isVideo`: Binary flag indicating if the frame is from video
-- `video`: Source video file name
-
-#### 2. Geo-location and Timing Data
-- `latitude_x`, `latitude_y`: GPS latitude coordinates
-- `longitude_x`, `longitude_y`: GPS longitude coordinates
-- `altitude`: Altitude of the drone
-- `date_time_x`, `date_time_y`, `date_time`: Timestamps
-- `ms`, `new_ms`, `time(millisecond)`: Millisecond timing information
-
-#### 3. Wildlife Annotation Data
-- `xtl`, `ytl`, `xbr`, `ybr`: Bounding box coordinates (top-left x,y and bottom-right x,y)
-- `points`: Polygon or keypoint annotations
-- `label`: Class label of the annotated object
-- `source`: Source of the annotation
-- `behaviour`: Annotated behavior classification
-- `keyframe_x`, `keyframe_y`: Keyframe indicators for tracking
-- `outside_x`, `outside_y`: Flags indicating if the subject is outside the frame
-- `occluded_x`, `occluded_y`: Flags indicating if the subject is occluded
-- `z_order_x`, `z_order_y`: Z-order for overlapping annotations
-
-#### 4. Drone Status and Telemetry
-- `height_above_takeoff(feet)`: Drone height relative to takeoff point
-- `height_above_ground_at_drone_location(feet)`: Drone height above ground
-- `ground_elevation_at_drone_location(feet)`: Ground elevation at drone location
-- `altitude_above_seaLevel(feet)`: Altitude above sea level
-- `height_sonar(feet)`: Height measured by sonar
-- `speed(mph)`: Drone speed
-- `distance(feet)`: Distance from takeoff point
-- `mileage(feet)`: Total distance traveled
-- `satellites`: Number of GPS satellites being used
-- `gpslevel`: GPS signal strength
-- `voltage(v)`: Battery voltage
-- `max_altitude(feet)`, `max_ascent(feet)`, `max_speed(mph)`, `max_distance(feet)`: Maximum values recorded
-- `xSpeed(mph)`, `ySpeed(mph)`, `zSpeed(mph)`: Speed components in x, y, z directions
-- `compass_heading(degrees)`: Compass heading
-- `pitch(degrees)`, `roll(degrees)`: Drone orientation angles
-- `rc_elevator`, `rc_aileron`, `rc_throttle`, `rc_rudder`: Remote control inputs
-- `rc_elevator(percent)`, `rc_aileron(percent)`, `rc_throttle(percent)`, `rc_rudder(percent)`: Remote control inputs as percentages
-- `gimbal_heading(degrees)`, `gimbal_pitch(degrees)`, `gimbal_roll(degrees)`: Gimbal orientation
-- `battery_percent`: Battery percentage remaining
-- `voltageCell1` through `voltageCell6`: Individual battery cell voltages
-- `current(A)`: Current draw in Amperes
-- `battery_temperature(f)`: Battery temperature in Fahrenheit
-- `flycStateRaw`: Raw flight controller state
-- `flycState`: Human-readable flight controller state
-- `message`: Status or event message
+**See [metadata/DATA_DICTIONARY.md](metadata/DATA_DICTIONARY.md) for complete field descriptions.**
+
+Key field groups:
+
+**🌿 Darwin Core Event Fields** (`video_events.csv`, `session_events.csv`):
+- Event identifiers and temporal coverage
+- Geographic coordinates and bounds
+- Sampling protocol descriptions
+- Taxonomic scope and abundance
+- Humboldt Eco extensions for inventory data
+
+**📍 Geolocation** (occurrence files):
+- GPS latitude/longitude (WGS84)
+- Altitude above sea level
+- Launch point and bounding box (event files)
+
+**📷 Camera Metadata** (occurrence files):
+- ISO, shutter speed, aperture
+- Focal length and zoom ratio
+- Color temperature and mode
+
+**🦓 Detection Annotations** (occurrence files):
+- Bounding box coordinates (CVAT format)
+- Track ID for multi-frame sequences
+- Occlusion and truncation flags
+
+**🏃 Behavior Labels** (occurrence files):
+- Activity classification (ethogram-based)
+- Behavioral state at frame capture
+- Mini-scene grouping
+
+### Data Splits
+
+This dataset does not include pre-defined train/val/test splits. Recommended splitting strategies:
+
+**Temporal Split:**
+- Train: Jan 11-13 (sessions 1-8)
+- Val: Jan 16 (session 9)
+- Test: Jan 17 (sessions 10-11)
+
+**Spatial Split:**
+- Split by location clusters based on GPS coordinates
+
+**Species-Stratified:**
+- Ensure all three species in each split
+
+**Mission-Based:**
+- Keep complete sessions together (do not split individual videos)
+
+## Platform and Mission Specifications
+
+### 🚁 Platform Details
+
+**Type:** UAV (Unmanned Aerial Vehicle)
+
+**Hardware:**
+- **Primary Platform:** DJI Mavic Air 2
+- **Secondary Platform:** DJI Mini
+- Max flight time: ~25-30 minutes
+- Wind resistance: Moderate (class 5 winds, ~8-10 m/s)
 
+**Autonomy:**
+- Mode: Manual flight with GPS stabilization
+- Navigation: Operator-controlled following focal groups
+- Collision avoidance: Obstacle detection enabled
+- Return-to-home: Automatic on signal loss
+
+### 📷 Sensor Specifications
+
+**Primary Sensor: DJI Integrated Camera**
+- Type: RGB
+- Resolution: 5472 × 3078 pixels (5.4K)
+- Frame rate: 24-30 fps
+- Bit depth: 8-bit
+- Format: MP4 video
+
+**Telemetry Included:**
+- GPS coordinates (SRT files embedded in video)
+- IMU data (altitude, orientation)
+- Camera settings (ISO, shutter, aperture, focal length)
+- Timestamp synchronization
+
+### 🗺️ Mission Parameters
+
+**Flight Specifications:**
+- Altitude: 20-50 m AGL (above ground level)
+- Typical altitude: 30-40 m
+- Speed: 0-5 m/s (adaptive to animal movement)
+- Flight pattern: Focal animal follows (manual tracking)
+- Duration per mission: 5-50 minutes
+
+**Environmental Conditions:**
+- Season: Dry season (January)
+- Weather: Clear to partly cloudy
+- Wind: Light to moderate
+- Time of day: Morning (09:00-12:00) and afternoon (14:00-16:30)
+
+### 🔍 Sampling Protocol
+
+**Survey Design:**
+- Focal group follows: Single herd tracked continuously per session
+- Opportunistic sampling of observed groups
+- Continuous video recording during follows
+
+**Flight Operations:**
+- Licensed drone operators with Kenya Civil Aviation Authority approval
+- Maintained minimum altitude of 20m to minimize disturbance
+- Animals monitored for behavioral response; flight aborted if disturbance detected
+
+**Data Collection:**
+- Continuous video recording at 5.4K resolution
+- GPS telemetry embedded in SRT sidecar files
+- Frame extraction in CVAT for annotation
+
+**Quality Control:**
+- Field notes recorded for each session
+- Video quality assessed before annotation
+- Behavior annotations reviewed by expert ecologists
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-This dataset was specifically curated to combine behavior annotations with corresponding drone telemetry to analyze which flight features produced useful data for behavior analysis. By integrating these different data streams, researchers can identify optimal drone flight patterns, heights, speeds, and camera configurations that maximize the quality of behavioral data while minimizing disturbance to wildlife. This integration supports research in wildlife monitoring and behavior analysis using drone technology, with the goal of developing improved protocols for wildlife observation that balance data quality with animal welfare considerations.
+This dataset was created to address two key research questions:
+
+1. **What drone flight parameters optimize behavioral data quality?** By correlating altitude, speed, distance, and camera settings with annotation completeness and animal visibility, researchers can develop evidence-based protocols for wildlife monitoring.
+
+2. **Can we quantify animal disturbance from drone presence?** Frame-level behavior annotations allow detection of alert, fleeing, or disrupted behaviors that indicate drone impact.
+
+The dataset fills a critical gap: while many drone wildlife datasets provide detection boxes, few include detailed behavior labels synchronized with flight telemetry. This enables research on the trade-offs between data quality and animal welfare.
 
 ### Source Data
 
 #### Data Collection and Processing
 
-Data was collected from 6 January 2023 through 21 January 2023 at the Mpala Research Centre in Kenya under a Nacosti research license. The team used DJI Mavic 2S drones equipped with cameras to record 5.4K resolution videos (5472 x 3078 pixels) from varying altitudes and distances of 10 to 50 meters from the animals. The distance was determined by circumstances and safety regulations to ensure both quality data collection and minimal wildlife disturbance.
-
-Frame extraction was performed using [CVAT](https://www.cvat.ai/), and behavior annotations were added using [annotation tool/software] by [annotators]. Telemetry data was synchronized with video frames using [method/software]. <<<<<<<<-needs to be filled in
+**Field Collection:**
+
+1. **Planning:**
+   - Sites selected based on known zebra and giraffe populations at Mpala Research Centre
+   - Flights conducted during peak activity hours (morning/afternoon)
+   - Safety briefings and airspace clearance for each flight
+
+2. **Collection:**
+   - Operators located focal groups via binoculars or vehicle sighting
+   - Drones launched 50-100m from animals
+   - Continuous video recording while following group movements
+   - Flight logs automatically recorded in SRT files
+   - Field notes on weather, behavior, and technical issues
+
+3. **Post-Processing:**
+   - Videos transferred from SD cards with immediate backup
+   - SRT files extracted for telemetry data
+   - Frame extraction at 1 fps in CVAT annotation platform
+   - Detection bounding boxes drawn for all visible animals
+   - Mini-scenes identified (continuous behavioral sequences)
+   - Behavior labels applied by trained ecologists
+   - Quality review of all annotations
+
+**Software and Tools Used:**
+- Flight planning: DJI Fly app
+- Video capture: DJI Mavic Air 2 / DJI Mini onboard recording
+- Frame extraction: CVAT (Computer Vision Annotation Tool)
+- Annotation: CVAT with custom behavior taxonomy
+- Telemetry parsing: Custom Python scripts
+- Data merging: `merge_behavior_telemetry.py` (this repository)
 
 #### Who are the source data producers?
 
-The dataset was collected by the Imageomics team as part of the [Kenyan Animal Behavior Remote sensing (KABR) project](https://imageomics.github.io/KABR/). The drone operations were conducted by licensed drone operators and researchers with appropriate permits for wildlife observation in Kenya.
+**Field Team:**
+- Jenna M. Kline (Ohio State University) - Drone operations, field coordination
+- Elizabeth Campolongo (Rensselaer Polytechnic Institute) - Drone operations, data collection
+- Matt Thompson (Ohio State University) - Drone operations, field support
+- Local field assistants from Mpala Research Centre
 
-### Annotations
+**Local Collaboration:**
+- Mpala Research Centre provided logistical support and site access
+- Kenya Wildlife Service granted research permits
+- Local communities consulted on flight operations
 
-#### Annotation process
+### Annotations
 
-Please refer to the [KABR](https://huggingface.co/datasets/imageomics/KABR) dataset and associated paper for details on the annotation process.
+#### Annotation Process
+
+**🤖 Annotation Method:**
+- [x] Semi-automated (CVAT tracking tools + manual review and behavior labeling)
+
+**Tools Used:**
+- Software: CVAT (Computer Vision Annotation Tool)
+- Version: Web-based platform (2023)
+
+**Annotation Guidelines:**
+- All visible animals annotated with bounding boxes
+- Bounding boxes drawn tightly around animal body
+- Partial occlusions included if >30% of animal visible
+- Track IDs maintained across frames within mini-scenes
+- Behavior labels applied based on dominant activity in frame
+- Uncertain behaviors marked for expert review
+
+**Quality Control:**
+- Annotator training: 4 hours on example videos with expert feedback
+- Inter-annotator agreement: Not formally quantified (small expert team)
+- Review process: Senior ecologist (Kline) reviewed 100% of behavior labels
+- Difficult cases: Discussed in team meetings, consensus labels applied
+- Annotation confidence: Not explicitly scored
+
+**Annotation Coverage:**
+- Fully annotated: No (not all frames have animals)
+- Frames with visible animals: ~90% annotated
+- Behavior labels: Applied to mini-scenes (continuous sequences)
+- Missing annotations: Frames without animals or poor quality (blur, occlusion)
+
+#### Who are the annotators?
+
+**Annotator Team:**
+- Number of annotators: 3 primary, 2 reviewers
+- Expertise: Graduate students in ecology/computer science with wildlife identification training
+- Training provided: 4 hours initial training + ongoing feedback
+- Compensation: Academic credit and authorship
+
+**Subject Matter Experts:**
+- Daniel Rubenstein - Guidance on zebra and giraffe behavior
+- Charles Stewart - Computer vision and annotation protocols
+- Tanya Berger-Wolf - Funding, project oversight
+- Elizabeth Campolongo - Data science and annotation review
+- Matt Thompson - Software development and data processing
+- Jenna Kline - Drone operations, project lead, annotation review
 
 ### Personal and Sensitive Information
 
-This dataset does not contain personal information. Location data has been [method for handling sensitive wildlife location data, if applicable] to protect vulnerable or endangered species.
+**⚠️ Privacy and Security Considerations:**
+
+**Human Subjects:**
+- [x] No humans visible in imagery
+- Note: Flights conducted in remote areas away from settlements
+
+**Endangered Species:**
+- [x] Contains endangered/threatened species: *Equus grevyi* (Grevy's zebra, Endangered)
+- Location precision: Full GPS coordinates included (site is within protected research center)
+- Consultation: Mpala Research Centre and Kenya Wildlife Service approved data sharing
+
+**Cultural Sensitivity:**
+- [x] Traditional lands: Mpala Research Centre operates with community consent
+
+**Security:**
+- [x] No security concerns
+- Data collected in collaboration with local authorities
+- Full coordinates shared to support scientific use
 
 ## Considerations for Using the Data
 
+### Dataset Statistics
+
+**Species Distribution:**
+
+| Species (Scientific Name) | Common Name | Videos | Sessions | Individuals (range) |
+|---------------------------|-------------|--------|----------|---------------------|
+| *Equus grevyi* | Grevy's zebra | 5 | 3 | 3-7 |
+| *Equus quagga* | Plains zebra | 30 | 11 | 2-12 |
+| *Giraffa reticulata* | Reticulated giraffe | 6 | 2 | 4-8 |
+| Mixed | Multiple species | 6 | 1 | 2-4 |
+
+**Class Balance:**
+- Plains zebra over-represented (opportunistic sampling)
+- Grevy's zebra limited by lower population density
+- Giraffes limited to specific habitat types
+
+**Video Characteristics:**
+- Frame count range: 10,000-66,000 frames per video
+- Duration range: 3-50 minutes per video
+- Altitude range: 8-72 m above sea level
+- Typical animal size in frame: 50-200 pixels (height)
+
+**Behavior Distribution:**
+- Walking: ~40%
+- Grazing: ~25%
+- Standing/vigilance: ~20%
+- Running: ~10%
+- Other (social, nursing, etc.): ~5%
+
 ### Bias, Risks, and Limitations
 
-- **Sampling bias**: Data collection was limited to [specific conditions, times of day, weather conditions], which may not represent the full range of natural behaviors.
-- **Observer effect**: The presence of drones may influence animal behavior, potentially biasing observations.
-- **Technical limitations**: Drone battery life limited observation sessions to [duration], and weather conditions restricted operations to [conditions].<<<<<--needs filled in
-- **Detection bias**: Animals may be more difficult to detect in certain environments or weather conditions.
+**⚠️ Known Biases:**
+
+1. **Geographic Bias:**
+   - Data from single site (Mpala Research Centre, Laikipia)
+   - May not generalize to other savanna ecosystems
+   - Represents dry season only, captured during drought conditions
+
+2. **Temporal Bias:**
+   - Morning and afternoon flights only (battery/weather constraints)
+   - Nocturnal or dawn/dusk behavior not captured
+   - Single month snapshot (seasonal variation not represented)
+
+3. **Species Bias:**
+   - Plains zebra over-represented (most abundant species)
+   - Grevy's zebra limited by population size
+   - No data on smaller species (<50 cm body size)
+
+4. **Environmental Bias:**
+   - Dry season habitat conditions
+   - Drought-affected vegetation
+   - Clear to partly cloudy weather only
+   - No wet season or dense vegetation scenarios
+
+5. **Detection Bias:**
+   - Animals in open areas more likely to be followed
+   - Dense vegetation reduces detection probability
+   - Cryptic species under-represented
+
+**Technical Limitations:**
+
+- **Image Quality:** Variable due to altitude, lighting, and atmospheric conditions
+- **Coverage Gaps:** 21 videos lack occurrence data due to missing/corrupted SRT files or failed processing
+- **Annotation Limitations:** Behavior labels are subjective; inter-observer agreement not quantified
+- **GPS Accuracy:** ±5-10m typical; may drift during long flights
+
+**Ethical Limitations:**
+
+- **Animal Welfare:** Potential for disturbance despite mitigation efforts
+- **Data Usage:** GPS coordinates could theoretically enable harmful uses (though species are common and well-protected at Mpala)
 
 ### Recommendations
 
-- Users should account for potential observer effects when analyzing behavior patterns.
-- Correlations between drone positioning and animal behavior should consider the confounding variables documented in the dataset.
-- For machine learning applications, stratified sampling is recommended to address class imbalances in behavior categories.
-- When using this data for conservation purposes, consider the ethical implications of drone-based wildlife monitoring.
+**Best Practices for Using This Dataset:**
+
+1. **For Detection/Tracking Models:**
+   - Account for altitude-dependent scale variation (20-50m range)
+   - Consider species-specific detection difficulty (giraffes easier than zebras)
+   - Test generalization to new sites (single-location training data)
+
+2. **For Behavior Recognition:**
+   - Class imbalance exists; consider weighted loss or resampling
+   - Behavior labels are coarse; fine-grained states may be ambiguous
+   - Temporal context improves accuracy (behaviors occur in sequences)
+
+3. **For Ecological Analysis:**
+   - Do not extrapolate to wet season without additional data
+   - Account for detection probability varying by habitat/altitude
+   - Animal counts are minimum estimates (some individuals may be hidden)
+
+4. **For Drone Protocol Development:**
+   - Correlate altitude/speed with detection rate and annotation completeness
+   - Monitor for behavioral responses in data (alert, flee behaviors)
+   - Consider trade-offs between data quality and disturbance risk
+
+**Ethical Use:**
+
+- Do not use for unethical wildlife targeting or harassment
+- Respect that full GPS coordinates enable site replication for conservation research
+- Cite dataset appropriately and acknowledge indigenous land stewardship
+
+**What This Dataset Should NOT Be Used For:**
+
+- Estimating absolute population sizes (sampling is not systematic)
+- Generalizing to wet season, nighttime, or other habitats/regions
 
 ## Licensing Information
 
-This dataset is dedicated to the public domain under the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). We ask that you cite the dataset using the below citation if you make use of it in your research.
+**Dataset License:** [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
+
+**Citation Requirement:** While CC0 does not legally require citation, we strongly request that you cite the dataset and associated paper if you use this data (see [Citation section](#citation)).
+
+**Code License:** MIT License for scripts in this repository
 
 ## Citation
 
-**BibTeX:**
+**If you use this dataset, please cite:**
 
-```
-@misc{kabr_telemetry_dataset,
-  author = {Kline, Jenna M. and Kholiavchenko, Maksim and Brookes, Otto and Berger-Wolf, Tanya and Stewart, Charles V. and Stewart, Christopher},
-  title = {KABR Behavior and Telemetry Dataset},
+**Dataset:**
+```bibtex
+@misc{kline2024kabr_telemetry,
+  author = {Kline, Jenna M. and Campolongo, Elizabeth and Thompson, Matt and
+            Kholiavchenko, Maksim and Brookes, Otto and Berger-Wolf, Tanya and
+            Stewart, Charles V. and Stewart, Christopher},
+  title = {KABR Behavior Telemetry: Frame-Level Drone Wildlife Monitoring Dataset},
   year = {2024},
-  url = {https://imageomics.github.io/KABR/},
-  publisher = {Imageomics}
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/imageomics/kabr-behavior-telemetry}
 }
 ```
 
 **Associated Paper:**
-
+```bibtex
+@article{kline2024integrating,
+  title = {Integrating Biological Data into Autonomous Remote Sensing Systems for
+           In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with
+           Unmanned Aerial Vehicles (UAVs)},
+  author = {Kline, Jenna M. and Campolongo, Elizabeth and Thompson, Matt and others},
+  journal = {arXiv preprint arXiv:2407.16864},
+  year = {2024},
+  url = {https://arxiv.org/abs/2407.16864}
+}
 ```
-@misc{kline2024integrating,
-      title={Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs)}, 
-      author={Jenna M. Kline and Maksim Kholiavchenko and Otto Brookes and Tanya Berger-Wolf and Charles V. Stewart and Christopher Stewart},
-      year={2024},
-      booktitle={Proceedings of the First Workshop on Imageomics: Discovering Biological Knowledge from Images using AI, held as part of AAAI 24}
-      eprint={2407.16864},
-      url={https://arxiv.org/abs/2407.16864}, 
+
+**FAIR² Drone Data Standard:**
+```bibtex
+@article{kline2025fair2,
+  title = {Toward a FAIR² Standard for Drone-Based Wildlife Monitoring Datasets},
+  author = {Kline, Jenna and others},
+  year = {2025},
+  note = {In preparation}
 }
 ```
 
 ## Acknowledgements
 
-This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Additional support was provided by the [AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE)](https://icicle.osu.edu/), funded by the US National Science Foundation under [Award #2112606](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606). 
+This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). 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.
 
-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.
+We thank:
+- **Mpala Research Centre** and **Jackson Miliko** for logistical support and site access
+- **Kenya Wildlife Service** for research permits
+- **Kenya Civil Aviation Authority** for drone operation clearances
+- **Local field assistants** from Mpala Research Centre
+- **Annotation team**: 
+Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
+Jenna Kline (The Ohio State University) - ORCID: 0009-0006-7301-5774
+Michelle Ramirez (The Ohio State University)
+Sam Stevens (The Ohio State University)
+Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484
+Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
+Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
+Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413
+Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-8585
+Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
 
-## Glossary
 
-- **KABR**: Kenyan Animal Behavior Remote sensing system
-- **Telemetry**: Remote collection of measurement data
-- **Ethogram**: A catalog or inventory of behaviors or actions exhibited by an animal
-- **Gimbal**: A pivoted support that allows rotation of the camera around a single axis
-- **FPV**: First Person View
-- **Flown state**: Operating condition of the drone's flight controller
+**Conservation Partners:**
+- Mpala Research Centre, Laikipia County, Kenya
+- Grevy's Zebra Trust
+
+**Data Collection Permits:**
+- Kenya Wildlife Service research permit
+- Kenya Civil Aviation Authority drone operations clearance
+- Nacosti research license
+
+## Validation and Quality Metrics
+
+**🤖 AI-Readiness Validation:**
+
+- [x] Machine-readable metadata (YAML front matter complete)
+- [x] Structured annotations in Darwin Core format
+- [ ] Train/val/test splits pre-defined (users should create)
+- [x] Class distribution documented
+- [x] Data loading code provided (Python scripts)
+- [ ] Example notebooks (planned)
+
+**🌿 Darwin Core Validation:**
 
+- [x] Event records complete and valid
+- [x] Occurrence records complete and valid (47/68 videos)
+- [x] Scientific names validated against GBIF backbone
+- [x] Coordinates in WGS84
+- [x] Sampling protocol documented
+- [ ] GBIF dataset registration (planned)
+
+**⚠️ FAIR² Compliance Checklist:**
+
+- [ ] **Findable:** DOI to be assigned
+- [x] **Accessible:** Open access via GitHub/Hugging Face
+- [x] **Interoperable:** Darwin Core, Humboldt Eco, CSV/JSON formats
+- [x] **Reusable:** CC0 license, full provenance documented
+- [x] **AI-Ready:** Machine-readable, structured, versioned
+
+## Code and Tools
+
+**Data Loading (Python):**
+
+```python
+import pandas as pd
+
+# Load session-level events
+sessions = pd.read_csv('data/session_events.csv')
+
+# Load video-level events
+videos = pd.read_csv('data/video_events.csv')
+
+# Load occurrence records for a specific video
+occurrences = pd.read_csv('data/occurrences/11_01_23-DJI_0977.csv')
+
+# Filter to frames with detections
+detections = occurrences.dropna(subset=['xtl', 'ytl', 'xbr', 'ybr'])
+
+# Group by behavior
+behavior_counts = detections.groupby('behaviour').size()
+```
+
+**Visualization Example:**
+
+```python
+import matplotlib.pyplot as plt
+import geopandas as gpd
+from shapely.wkt import loads
+
+# Plot session footprints
+sessions_with_gps = sessions.dropna(subset=['footprintWKT'])
+geometries = [loads(wkt) for wkt in sessions_with_gps['footprintWKT']]
+gdf = gpd.GeoDataFrame(sessions_with_gps, geometry=geometries, crs='EPSG:4326')
+
+fig, ax = plt.subplots(figsize=(10, 10))
+gdf.plot(ax=ax, alpha=0.5, edgecolor='black')
+plt.title('Session Geographic Coverage')
+plt.xlabel('Longitude')
+plt.ylabel('Latitude')
+plt.show()
+```
+
+**Processing Scripts:**
+
+See `scripts/` directory for:
+- `merge_behavior_telemetry.py` - Generate occurrence files from source data
+- `update_video_events.py` - Add annotation file references
+- `add_event_times.py` - Extract temporal bounds
+- `add_gps_data.py` - Calculate GPS statistics
+
+## Glossary
+
+- **AGL:** Above Ground Level - altitude measured from terrain surface
+- **Darwin Core:** Biodiversity data standard maintained by TDWG
+- **Ethogram:** Catalog of behaviors exhibited by a species
+- **FAIR²:** FAIR principles extended for AI-ready datasets
+- **Humboldt Eco:** Extension of Darwin Core for ecological inventory data
+- **Mini-scene:** Continuous behavioral sequence tracked across frames
+- **Occurrence:** Darwin Core term for species observation record
+- **SRT:** SubRip subtitle format; used for drone telemetry embedding
+- **TDWG:** Biodiversity Information Standards (Taxonomic Databases Working Group)
+- **UAV:** Unmanned Aerial Vehicle (drone)
+- **WKT:** Well-Known Text format for geographic geometries
 
 ## Dataset Card Authors
 
-Jenna M. Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, Christopher Stewart
+Jenna M. Kline, Elizabeth Campolongo, Matt Thompson
 
 ## Dataset Card Contact
 
-kline dor 377 at osu dot edu
+For questions about this dataset:
+- **Primary Contact:** Jenna M. Kline (kline.377@osu.edu)
+- **Issues:** [GitHub repository issues](https://github.com/Imageomics/kabr-behavior-telemetry/issues)
+- **KABR Project:** https://imageomics.github.io/KABR/
+
+---
+
+**Version History:**
+
+- v1.1.0 (2026-01-02): Added occurrence files, GPS data, temporal bounds, updated Darwin Core events
+- v1.0.0 (2024-12-31): Initial release with session and video event metadata
+
+---
+
+*This dataset card follows the FAIR² Drone Data Standard and extends the [Imageomics dataset card template](https://imageomics.github.io/Imageomics-guide/wiki-guide/HF_DatasetCard_Template_mkdocs/).*

data/project_event.csv

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+size 391

data/session_events.csv

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data/video_events.csv

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metadata/event_session_fields.csv

@@ -0,0 +1,55 @@
+Field,Requirement,Description,Example Value
+EVENT CORE FIELDS,,,
+eventID,REQUIRED,Unique session identifier,KABR-2023:13_01_23_session_1
+parentEventID,REQUIRED,Link to project event,KABR-2023
+eventType,REQUIRED,Type of event,drone survey
+eventDate,REQUIRED,ISO 8601 date,2023-01-13
+eventTime,RECOMMENDED,Start time (local),10:19:23
+endTime,RECOMMENDED,End time,10:48:03
+year,DERIVED,From eventDate,2023
+month,DERIVED,From eventDate,1
+day,DERIVED,From eventDate,13
+samplingProtocol,REQUIRED,Method description,Aerial drone video survey using DJI Mavic Air 2
+sampleSizeValue,RECOMMENDED,Duration,28.67
+sampleSizeUnit,RECOMMENDED,Unit for duration,minutes
+samplingEffort,OPTIONAL,Additional effort info,3 video segments
+locationID,REQUIRED,Location identifier,MPALA-KENYA
+locality,REQUIRED,Location name,Mpala Research Centre
+country,REQUIRED,,Kenya
+countryCode,REQUIRED,ISO 3166-1,KE
+decimalLatitude,REQUIRED,WGS84 centroid,0.2833
+decimalLongitude,REQUIRED,WGS84 centroid,36.8833
+geodeticDatum,REQUIRED,,WGS84
+coordinateUncertaintyInMeters,RECOMMENDED,Flight area radius,500
+habitat,RECOMMENDED,Habitat description,Open savanna grassland; Bitterlich score: 2
+eventRemarks,OPTIONAL,Field notes, weather,Light cloud, light wind. Adult male and female Plains
+,,,
+HUMBOLDT EXTENSION FIELDS,,,
+eco:inventoryTypes,REQUIRED,Type of inventory,restrictedSearch
+eco:protocolNames,REQUIRED,Protocol name,KABR Drone Video Survey Protocol
+eco:protocolDescriptions,RECOMMENDED,Protocol details,Drone follows focal group at 20-50m altitude
+eco:protocolReferences,OPTIONAL,DOI/URL to protocol,arXiv:2510.02030
+eco:isAbundanceReported,REQUIRED,Count data included?,true
+eco:isAbundanceCapReported,REQUIRED,Max count capped?,false
+eco:abundanceUnit,CONDITIONAL,If abundance reported,individuals
+eco:isVegetationCoverReported,REQUIRED,,true
+eco:vegetationCoverUnit,CONDITIONAL,Bitterlich method,Bitterlich score (0-10 scale)
+eco:isTaxonCompletenessReported,REQUIRED,All taxa documented?,true
+eco:taxonCompletenessProtocols,CONDITIONAL,How determined,Visual review of all video frames
+eco:isAbsenceReported,REQUIRED,Absence data?,false
+eco:hasNonTargetTaxa,REQUIRED,Other species present?,true
+eco:nonTargetTaxa,CONDITIONAL,If hasNonTargetTaxa=true,Struthio camelus | Giraffa reticulata (background)
+eco:targetTaxonomicScope,REQUIRED,Target taxa,Equus quagga | Equus grevyi | Giraffa reticulata
+eco:excludedTaxonomicScope,OPTIONAL,Excluded taxa,
+eco:samplingPerformedBy,RECOMMENDED,Observer names,Jenna Kline, Michelle Ramirez, Sam Stevens, Reshma Ramesh Babu, Jackson Miliko, Isla Duporge, Neil Rosser, Tanya Berger-Wolf, Daniel Rubenstein
+eco:siteCount,RECOMMENDED,Number of distinct sites,1
+eco:siteNestingDescription,OPTIONAL,How sites nested,Single focal group follow
+eco:verbatimSiteDescriptions,OPTIONAL,Site descriptions,Near dam, Top Spray area
+eco:geospatialScopeAreaValue,OPTIONAL,Area surveyed,
+eco:geospatialScopeAreaUnit,OPTIONAL,,square meters
+eco:totalAreaSampledValue,OPTIONAL,Actual area covered,
+eco:totalAreaSampledUnit,OPTIONAL,,square meters
+,,,
+FAIR EXTENSION FIELDS,,,
+videoID,REQUIRED,Unique video identifier,DJI_0001
+telemetryFileName,RECOMMENDED,Telemetry file name,AirData_DJI_0001.txt