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README.md

@@ -2,9 +2,8 @@
 license: cc0-1.0
 language:
 - en
-pretty_name: mmla_opc
-task_categories:
-- image-classification
+pretty_name: wildwing_mpala
+task_categories: [image-classification]
 tags:
 - biology
 - image
@@ -16,118 +15,264 @@ size_categories: 10K<n<100K
 ---
 
 
-# Dataset Card for mmla-opc
+# Dataset Card for wildwing-mpala
 
 <!-- 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 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 COCO 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 giraffes, Grevy's zebras, and Plains zebras collected at the Mpala Research Center in Kenya. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.
+
+The annotations indicate the presence of animals in the images in COCO format. The dataset is designed to facilitate research in wildlife monitoring and conservation using autonomous drones.
 
 
 ### Dataset Description
 
 - **Curated by:** Jenna Kline
-- **Homepage:** [mmla](https://github.com/Imageomics/mmla)
-- **Repository:** [https://github.com/Imageomics/mmla](hhttps://github.com/Imageomics/mmla)
-- **Paper:** [WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring](https://doi.org/10.1111/2041-210X.70018)
+- **Homepage:** [mmla](https://imageomics.github.io/mmla/)
+- **Repository:** [https://github.com/imageomics/mmla](https://github.com/imageomics/mmla)
+- **Papers:** 
 
+  - [MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset](https://arxiv.org/abs/2504.07744)
 
-<!-- Provide a longer summary of what this dataset is. -->
-This dataset contains video frames collected from the [WildWing system](https://imageomics.github.io/wildwing/), which is an autonomous drone designed for wildlife monitoring. 
+  - [WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring](https://doi.org/10.1111/2041-210X.70018)
+
+  - [Deep Dive KABR](https://doi.org/10.1007/s11042-024-20512-4)
 
-The dataset includes frames from multiple sessions, over two days of data collection, 2025-01-31 and 2025-02-01, with a total of 5 videos. Each session captures video footage of Plains zebras in their natural habitat at the Ol Pejeta Conservancy in Kenya. 
+  - [KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos](https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf)
 
-The dataset consists of 29,268 frames. Each frame is accompanied by annotations in COCO format, indicating the presence of zebras and their bounding boxes within the images. The annotations were completed manually by the dataset curator using [CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/kabr-tools).
 
 
-The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.
+<!-- Provide a longer summary of what this dataset is. -->
+This dataset contains video frames collected as part of the [Kenyan Animal Behavior Recognition (KABR)](https://kabrdata.xyz/) project at the [Mpala Research Center](https://mpala.org/) in Kenya in January 2023. 
+
+Sessions 1 and 2 are part of the [original KABR data release](https://huggingface.co/datasets/imageomics/KABR), now available in COCO format. Sessions 3, 4 and 5 are part of the extended release. 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 COCO format. The dataset is designed to facilitate research in wildlife monitoring and conservation using advanced imaging technologies.
 
-| Session | Date Collected | Video ID  | Total Frames | Size (pixels) | 
-|---------|---------------|-----------|--------------|---------------|
-| `session_1`       | 2025-01-31    | P0800081  | 5,949        | 3840x2160     |
-| `session_1`       | 2025-01-31    | P0830086  | 2,439        | 3840x2160     |
-| `session_1`       | 2025-01-31    | P0840087  | 4,461        | 4096x2160     |
-| `session_1`       | 2025-01-31    | P0860090  | 1,754        | 3840x2160     |
-| `session_1`       | 2025-01-31    | P0870091  | 2,123        | 4096x2160     |
-| `session_2`       | 2025-02-01    | P0910095  | 5,978        | 4096x2160     |
-| `session_2`       | 2025-02-01    | P0940098  | 6,564        | 4096x2160     |
-| **Total Frames:** |  |  | **29,268** |  |
+The dataset consists of 104,062 frames. Each frame is accompanied by annotations in COCO format, indicating the presence of zebras and giraffes and their bounding boxes within the images. The annotations were completed manually by the dataset curator using [CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/kabr-tools).
 
-This table shows the data collected at Ol Pejeta Conservancy in Laikipia, Kenya, with session information, dates, frame counts, and pixel resolution.
+
+| Session | Date Collected | Total Frames | Species | Video File IDs in Session |
+|---------|---------------|--------------|---------|----------------|
+| `session_1` | 2023-01-12 | 16,891 | Giraffe | DJI_0001, DJI_0002 |
+| `session_2` | 2023-01-17 | 11,165 | Plains zebra | DJI_0005, DJI_0006 |
+| `session_3` | 2023-01-18 | 17,940 | Grevy's zebra | DJI_0068, DJI_0069, DJI_0070, DJI_0071 |
+| `session_4` | 2023-01-20 | 33,960 | Grevy's zebra | DJI_0142, DJI_0143, DJI_0144, DJI_0145, DJI_0146, DJI_0147 |
+| `session_5` | 2023-01-21 | 24,106 | Giraffe, Plains and Grevy's zebras | DJI_0206, DJI_0208, DJI_0210, DJI_0211 |
+| **Total Frames:** | | **104,062** | | |
+
+This table shows the data collected at Mpala Research Center in Laikipia, Kenya, with session information, dates, frame counts, and primary species observed.
 
 The dataset includes frames extracted from drone videos captured during five distinct data collection sessions. Each session represents a separate field excursion lasting approximately one hour, conducted at a specific geographic location. 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/
-        P0800081/
+        DJI_0001/
             partition_1/
-                P0800081_000000.jpg
-                P0800081_000000.txt
-                ...
-                P0800081_007099.txt
+              DJI_0001_000000.jpg
+              DJI_0001_000001.txt
+              ...
+              DJI_0001_004999.txt
             partition_2/
-                P0800081_007100.jpg
-                P0800081_007100.txt
-                ...
-                P0800081_010048.txt
-        P0830086/
-            P0830086_000000.jpg
-            P0830086_000000.txt
+              DJI_0001_005000.jpg
+              DJI_0001_005000.txt
+              ...
+              DJI_0001_008700.txt
+        DJI_0002/
+          DJI_0002_000000.jpg
+          DJI_0002_000001.txt
+          ...
+          DJI_0002_008721.txt
+        metadata.txt
+    session_2/
+        DJI_0005/
+          DJI_0005_001260.jpg
+          DJI_0005_001260.txt
+          ...
+          DJI_0005_008715.txt
+        DJI_0006/
+          partition_1/
+            DJI_0006_000000.jpg
+            DJI_0006_000001.txt
             ...
-            P0830086_002438.txt
-        P0840087/
-            P0840087_000000.jpg
-            P0840087_000000.txt
+            DJI_0006_005351.txt
+          partition_2/
+            DJI_0006_005352.jpg
+            DJI_0006_005352.txt
             ...
-            P0840087_004770.txt
-        P0860090/
-            P0860090_000000.jpg
-            P0860090_000000.txt
+            DJI_0006_008719.txt
+        metadata.txt
+    session_3/
+        DJI_0068/
+          DJI_0068_000780.jpg
+          DJI_0068_000780.txt
+          ...
+          DJI_0068_005790.txt
+        DJI_0069/
+          partition_1/
+            DJI_0069_000000.jpg
+            DJI_0069_000001.txt
             ...
-            P0860090_001753.txt
-        P0870091/
-            P0870091_20250311_000000.jpg
-            P0870091_20250311_000000.txt
+            DJI_0069_004999.txt
+          partition_2/
+            DJI_0069_005000.jpg
+            DJI_0069_005000.txt
             ...
-            P0870091_20250311_003060.txt
-    session_2/
-        P0910095/
+            DJI_0069_005815.txt
+        DJI_0070/
+          partition_1/
+            DJI_0070_000000.jpg
+            DJI_0070_000001.txt
+            ...
+            DJI_0069_004999.txt
+          partition_2/
+            DJI_0070_005000.jpg
+            DJI_0070_005000.txt
+            ...
+            DJI_0070_005812.txt
+        DJI_0071/
+          DJI_0071_000000.jpg
+          DJI_0071_000000.txt
+          ...
+          DJI_0071_001357.txt
+        metadata.txt
+    session_4/
+        DJI_0142/
+            partition_1/
+                DJI_0142_000000.jpg
+                DJI_0142_000000.txt
+                ...
+                DJI_0142_002999.txt
+            partition_2/
+                DJI_0142_003000.jpg
+                DJI_0142_003000.txt
+                ...
+                DJI_0142_005799.txt
+        DJI_0143/
+            partition_1/
+                DJI_0143_000000.jpg
+                DJI_0143_000000.txt
+                ...
+                DJI_0143_002999.txt
+            partition_2/
+                DJI_0143_003000.jpg
+                DJI_0143_003000.txt
+                ...
+                DJI_0143_005816.txt
+        DJI_0144/
+            partition_1/
+                DJI_0144_000000.jpg
+                DJI_0144_000000.txt
+                ...
+                DJI_0144_002999.txt
+            partition_2/
+                DJI_0144_003000.jpg
+                DJI_0144_003000.txt
+                ...
+                DJI_0144_005790.txt
+        DJI_0145/
+            partition_1/
+                DJI_0145_000000.jpg
+                DJI_0145_000000.txt
+                ...
+                DJI_0145_002999.txt
+            partition_2/
+                DJI_0145_003000.jpg
+                DJI_0145_003000.txt
+                ...
+                DJI_0145_005811.txt
+        DJI_0146/
+            partition_1/
+                DJI_0146_000000.jpg
+                DJI_0146_000000.txt
+                ...
+                DJI_0146_002999.txt
+            partition_2/
+                DJI_0146_003000.jpg
+                DJI_0146_003000.txt
+                ...
+                DJI_0146_005809.txt
+        DJI_0147/
             partition_1/
-                P0910095_000000.jpg
-                P0910095_000000.txt
+                DJI_0147_000000.jpg
+                DJI_0147_000000.txt
                 ...
-                P0910095_002999.txt
+                DJI_0147_002999.txt
             partition_2/
-                P0910095_003000.jpg
-                P0910095_003000.txt
+                DJI_0147_003000.jpg
+                DJI_0147_003000.txt
                 ...
-                P0910095_005977.txt
-        P0940098/
+                DJI_0147_005130.txt
+        metadata.txt
+    session_5/
+        DJI_0206/
             partition_1/
-                P0940098_20250311_000000.jpg
-                P0940098_20250311_000000.txt
+                DJI_0206_000000.jpg
+                DJI_0206_000000.txt
                 ...
-                P0940098_20250311_003499.txt
+                DJI_0206_002499.txt
             partition_2/
-                P0940098_20250311_003500.jpg
-                P0940098_20250311_003500.txt
+                DJI_0206_002500.jpg
+                DJI_0206_002500.txt
+                ...
+                DJI_0206_004999.txt
+            partition_3/
+                DJI_0206_005000.jpg
+                DJI_0206_005000.txt
                 ...
-                P0940098_20250311_006563.txt
+                DJI_0206_005802.txt
+        DJI_0208/
+            partition_1/
+                DJI_0208_000000.jpg
+                DJI_0208_000000.txt
+                ...
+                DJI_0208_002999.txt
+            partition_2/
+                DJI_0208_003000.jpg
+                DJI_0208_003000.txt
+                ...
+                DJI_0208_005810.txt
+        DJI_0210/
+            partition_1/
+                DJI_0210_000000.jpg
+                DJI_0210_000000.txt
+                ...
+                DJI_0210_002999.txt
+            partition_2/
+                DJI_0210_003000.jpg
+                DJI_0210_003000.txt
+                ...
+                DJI_0210_005811.txt
+        DJI_0211/
+            partition_1/
+                DJI_0211_000000.jpg
+                DJI_0211_000000.txt
+                ...
+                DJI_0211_002999.txt
+            partition_2/
+                DJI_0211_003000.jpg
+                DJI_0211_003000.txt
+                ...
+                DJI_0211_005809.txt
+        metadata.txt
+
 ```
 
 ### 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 COCO 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>.jpg, each within a folder named for the date of the session. The annotations are in COCO format and are stored in a corresponding .txt file with the same name as the image. 
 
-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: DJI saves video files into 3GB chunks, so each session is divided into multiple video files. 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. The original video files are not included in the dataset.
 
 
 ### Data Fields
 
 **classes.txt**:
   - `0`: zebra
+  - `1`: giraffe
+  - `2`: onager
+  - `3`: dog
 
 **frame_id.txt**:
   - `class`: Class of the object in the image (0 for zebra)
@@ -151,9 +296,10 @@ Give your train-test splits for benchmarking; could be as simple as "split is in
 The dataset was created to facilitate research in wildlife monitoring and conservation using advanced imaging technologies. The goal is to develop and evaluate computer vision models that can accurately detect and classify animals from drone imagery, and their generalizability across different species and environments.
 
 
-<!-- ### Source Data -->
+### 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.) -->
+Please see the original [KABR dataset](https://huggingface.co/datasets/imageomics/KABR) for more information on the source data.
 
 #### Data Collection and Processing
 
@@ -161,9 +307,9 @@ The dataset was created to facilitate research in wildlife monitoring and conser
 This is what _you_ did to it following collection from the original source; it will be overall processing if you collected the data initially.
 -->
 
-The data was collected using the [WildWing](https://github.com/Imageomics/wildwing) system, which autonomously captures video footage of wildlife in their natural habitat. The data collection process involved flying the drone over the [Ol Pejeta Conservancy](https://www.olpejetaconservancy.org/) in Kenya, where Plains zebras were observed. The missions were flown during the [WildDrone](https://wilddrone.eu/) Hackathon in January 2025, with the goal of capturing high-quality video footage for analysis.
+The data was collected manually using a [DJI Air 2S drone](https://www.dji.com/support/product/air-2s). The drone was flown at the [Mpala Research Center](https://mpala.org/) in Laikipia, Kenya, capturing video footage of giraffes, Grevy's zebras, and Plains zebras in their natural habitat.
 
-The videos were annotated manually using the Computer Vision Annotation Tool [CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/kabr-tools) library. These detection annotations and original video files were then processed to extract individual frames, which were saved as JPEG images. The annotations were converted to COCO format, with bounding boxes indicating the presence of zebras in each frame.
+The videos were annotated manually using the Computer Vision Annotation Tool [CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/kabr-tools). These detection annotations and original video files were then processed to extract individual frames, which were saved as JPEG images. The annotations were converted to COCO format, with bounding boxes indicating the presence of zebras in each frame.
 
 <!-- #### Who are the source data producers?
 [More Information Needed] -->
@@ -179,16 +325,25 @@ If the dataset contains annotations which are not part of the initial data colle
 
 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 and kabr-tools were used to annotate the video frames. The annotation process involved manually labeling the presence of zebras in each frame, drawing bounding boxes around them, and converting the annotations to COCO format.
+[CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/kabr-tools) were used to annotate the video frames. The annotation process involved manually labeling the presence of animals in each frame, drawing bounding boxes around them, and converting the annotations to COCO format.
 <!-- This section describes the annotation process such as annotation tools used, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 
 #### Who are the annotators?
-Jenna Kline
+
 <!-- This section describes the people or systems who created the annotations. -->
+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 \
+Alison Zhong (The Ohio State University)
 
 ### Personal and Sensitive Information
-The dataset was cleaned to remove any personal or sensitive information. All images are of Plains zebras in their natural habitat, and no identifiable human subjects are present in the dataset.
+The dataset was cleaned to remove any personal or sensitive information. All images are of Plains zebras, Grevy's zebras, and giraffes in their natural habitat, and no identifiable human subjects are present in the dataset.
 <!-- 
 For instance, if your data includes people or endangered species. -->
 
@@ -216,49 +371,73 @@ This dataset (the compilation) has been marked as dedicated to the public domain
 
 ## Citation
 
+
 **BibTeX:**
 
+
 **Data**
 ```​
-@misc{mmla_opc,
-  author = {Kline, Jenna and 
-            Nguyen Ngoc, Dat and
-            Hine, Duncan and 
-            Rondeau Saint-Jean, Camille and
-            Maalouf, Guy and
-            Juma, Brenda and
-            Kilwaya, Alex and
-            Vuyiya, Brian and
-            Macharia, Irungu and
-            Njoroge, William and
-            Mutisya, Samuel and
-            Guerin, David and
-            Costelloe, Blair and
-            Pastucha, Elzbieta and
-            Hermansen, Jussi and
-            Jensen, Kjeld and
-            Watson, Matt and
-            Richardson, Tom and
-            Pagh Schultz Lundquist, Ulrik  
+@misc{wildwing_mpala,
+  author = {  Jenna Kline,
+              Maksim Kholiavchenko,
+              Alison Zhong,
+              Michelle Ramirez,
+              Samuel Stevens,
+              Nina Van Tiel,
+              Elizabeth Campolongo,
+              Matthew Thompson,
+              Reshma Ramesh Babu,
+              Namrata Banerji,
+              Alec Sheets,
+              Mia Magersupp,
+              Sowbaranika Balasubramaniam,
+              Isla Duporge,
+              Jackson Miliko,
+              Neil Rosser,
+              Tanya Berger-Wolf,
+              Eduardo Bessa,
+              Charles V. Stewart,
+              Daniel I. Rubenstein
           },
-  title = {MMLA Ol Pejeta Conservancy (OPC) Dataset},
+  title = {WildWing Mpala Dataset},
   year = {2025},
-  url = {https://huggingface.co/datasets/imageomics/mmla-opc},
+  url = {https://huggingface.co/datasets/imageomics/wildwing-mpala},
   doi = {<doi once generated>},
   publisher = {Hugging Face}
 }
 ```
 
+**Papers**
+
+```​
+@inproceedings{kholiavchenko2024kabr,
+  title={KABR: In-situ dataset for kenyan animal behavior recognition from drone videos},
+  author={Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and others},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
+  pages={31--40},
+  year={2024}
+}
+```
 
-## Acknowledgements
 
-This work was supported by the [WildDroneEU Project](https://wilddrone.eu). WildDrone is an MSCA Doctoral Network funded by the European Union’s Horizon Europe research and innovation funding programme under the Marie Skłodowska-Curie grant agreement no. 101071224.
+```​
+@article{kline2025wildwing,
+  title={WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring},
+  author={Kline, Jenna and Zhong, Alison and Irizarry, Kevyn and Stewart, Charles V and Stewart, Christopher and Rubenstein, Daniel I and Berger-Wolf, Tanya},
+  journal={Methods in Ecology and Evolution},
+  year={2025},
+  doi={https://doi.org/10.1111/2041-210X.70018}
+  publisher={Wiley Online Library}
+}
+```
 
-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). 
 
-This work was supported by the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment [ICICLE](https://icicle.osu.edu/), which is funded by the US National Science Foundation under grant number OAC-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.
+## 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). 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 AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment [ICICLE](https://icicle.osu.edu/), which is funded by the US National Science Foundation under grant number OAC-2112606.
 
 <!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. -->
 
@@ -267,7 +446,7 @@ Any opinions, findings and conclusions or recommendations expressed in this mate
 <!-- [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
 ## More Information 
-The data was collected under Kenya Civil Aviation Authority (KCAA) permit number KCAA/UAS/OPS/0048/2025. The data collection was conducted in collaboration with the Ol Pejeta Conservancy and the WildDrone Hackathon team in accordance with Research License No. NACOSTI/P/25/415376. 
+The data was gathered at the Mpala Research Center 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.
 
 <!-- [optional] Any other relevant information that doesn't fit elsewhere. -->