Add description for API, revise some link formulations (#3)

- Add description for API, revise some link formulations (4087fdcd67828983a6313c6acbb48f1baae352fe)


Co-authored-by: Elizabeth Campolongo <[email protected]>

README.md

@@ -3,6 +3,7 @@ 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."
 task_categories:
 - object-detection
 - image-classification
@@ -15,6 +16,7 @@ tags:
 - drone-imagery
 - telemetry
 - kabr
+- behavior
 size_categories: 10K<n<100K
 ---
 
@@ -28,9 +30,9 @@ This dataset consists of synchronized drone telemetry, camera metadata, behavior
 
 - **Curated by:** Jenna M. Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, Christopher Stewart
 - **Language(s) (NLP):** English
-- **Homepage:** https://imageomics.github.io/KABR/
-- **Repository:** https://github.com/Imageomics/kabr-tools
-- **Paper:** https://arxiv.org/abs/2407.16864
+- **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)
 
 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.
 
@@ -46,16 +48,11 @@ This dataset supports several computer vision and behavioral analysis tasks:
 
 ## Dataset Structure
 
-The dataset is organized with the following structure:
-
-```
-/dataset/
-    metadata.csv       # Main metadata file with all information
-```
+The dataset consists of a single CSV (`consolidated_metadata.csv`), which contains all information.
 
 ### Data Instances
 
-Each row in the 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].
+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
 
 ### Data Fields
 
@@ -134,17 +131,17 @@ This dataset was specifically curated to combine behavior annotations with corre
 
 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].
+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
 
 #### 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. The drone operations were conducted by licensed drone operators and researchers with appropriate permits for wildlife observation in Kenya.
+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.
 
 ### Annotations
 
 #### Annotation process
 
-Please refer to the [KABR]((https://huggingface.co/datasets/imageomics/KABR)) dataset and associated paper for details on the annotation process.
+Please refer to the [KABR](https://huggingface.co/datasets/imageomics/KABR) dataset and associated paper for details on the annotation process.
 
 ### Personal and Sensitive Information
 
@@ -156,7 +153,7 @@ This dataset does not contain personal information. Location data has been [meth
 
 - **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].
+- **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.
 
 ### Recommendations
@@ -199,16 +196,9 @@ This dataset is dedicated to the public domain under the [CC0 Public Domain Waiv
 
 ## Acknowledgements
 
-This work was supported by the US National Science Foundation
-through the [Imageomics Institute](https://imageomics.org), 
-which is funded by the 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). 
-and the AI institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment 
-(ICICLE) under [Award #2112606](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606).
-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.
+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). 
 
+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.
 
 ## Glossary