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  license: cc0-1.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: cc0-1.0
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+ task_categories:
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+ - video-classification
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+ tags:
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+ - zebra
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+ - giraffe
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+ - plains zebra
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+ - Grevy's zebra
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+ - video
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+ - animal behavior
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+ - behavior recognition
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+ - annotation
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+ - annotated video
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+ - conservation
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+ - drone
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+ - UAV
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+ - imbalanced
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+ - Kenya
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+ - Mpala Research Centre
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+ pretty_name: >-
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+ KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone
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+ Videos
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+ size_categories:
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+ - 1M<n<10M
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  ---
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+ # Dataset Card for KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone Videos
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://dirtmaxim.github.io/kabr/
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+ - **Repository:** https://github.com/dirtmaxim/kabr-tools
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+ - **Paper:** [Coming Soon]
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+ - **Leaderboard:**
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+ - **Point of Contact:**
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+
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+ ### Dataset Summary
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+
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+ We present a novel high-quality dataset for animal behavior recognition from drone videos.
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+ The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras.
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+ The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances.
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+ In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process.
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+ Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.
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+ The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors.
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+
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+ <!--This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).-->
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [Include Benchmarks Here]
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+
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+ ### Languages
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+
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+ [More Information Needed]
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+
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+ ## Dataset Structure
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+
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+ The KABR dataset follows the Charades format:
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+
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+ ```
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+ KABR
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+ /images
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+ /video_1
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+ /image_1.jpg
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+ /image_2.jpg
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+ ...
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+ /image_n.jpg
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+ /video_2
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+ /image_1.jpg
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+ /image_2.jpg
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+ ...
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+ /image_n.jpg
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+ ...
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+ /video_n
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+ /image_1.jpg
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+ /image_2.jpg
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+ /image_3.jpg
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+ ...
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+ /image_n.jpg
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+ /annotation
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+ /classes.json
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+ /train.csv
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+ /val.csv
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+ ```
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+
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+ The dataset can be directly loaded and processed by the [SlowFast](https://github.com/facebookresearch/SlowFast) framework.
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+
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+ **Informational Files**
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+ * `KABR/configs`: examples of SlowFast framework configs.
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+ * `KABR/annotation/distribution.xlsx`: distribution of classes for all videos.
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+
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+ **Scripts:**
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+ * `image2video.py`: Encode image sequences into the original video.
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+ * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `video/G0067.mp4`.
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+ * `image2visual.py`: Encode image sequences into the original video with corresponding annotations.
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+ * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `visual/G0067.mp4`.
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+
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+ ### Data Instances
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+
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+ [More Information Needed]
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+ **Naming:** Within the image folder, the `video_n` folders are named as follows (X indicates a number):
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+ * G0XXX.X - Giraffes
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+ * ZP0XXX.X - Plains Zebras
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+ * ZG0XXX.X - Grevy's Zebras
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+ * Within each of these folders the images are simply `X.jpg`.
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+
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+ ### Data Fields
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+
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+ [More Information Needed]
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+
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+ ### Data Splits
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+
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+ Training and validation sets are indicated by their respecive CSV files, located within the `annotation` folder.
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ We present a novel high-quality dataset for animal behavior recognition from drone videos.
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+ The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras.
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+ The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances.
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+ In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process.
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+ Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.
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+ The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors.
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+ We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models.
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+ Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies.
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+
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+ [To be added:]
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+ We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models.
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+ The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area.
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ Data was collected from 6 January 2023 through 21 January 2023 at the [Mpala Research Centre](https://mpala.org/) in Kenya.
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process.
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+ Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ [More Information Needed]
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+
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+ ### Discussion of Biases
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+
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+ [More Information Needed]
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+
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+ ### Other Known Limitations
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+
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+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed]
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+
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+ ### Licensing Information
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+
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+ [More Information Needed]
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+
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+ ### Citation Information
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+
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+ [More Information Needed]
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+
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+ ### Contributions
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+
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+ <!---location for authors instead of under curators?--->
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+ * Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
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+ * Jenna Kline (The Ohio State University)
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+ * Michelle Ramirez (The Ohio State University)
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+ * Sam Stevens (The Ohio State University)
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+ * Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484
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+ * Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
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+ * Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
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+ * Elizabeth Campolongo (Imageomics Institute) - ORCID: 0000-0003-0846-2413
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+ * Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
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+ * Jackson Miliko (Mpala Research Centre)
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+ * Eduardo Bessa (Universidade de Brasília) - ORCID: 0000-0003-0606-5860
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+ * Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412
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+ * Daniel Rubenstein (Princeton University) - ORCID: 0000-0001-9049-5219
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+ * Charles Stewart (Rensselaer Polytechnic Institute)