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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="Project name: BioTrove">
<meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
<meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG"/>
<meta property="og:url" content="URL OF THE WEBSITE"/>
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<title>BioTrove</title>
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<body>
<!-- <section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Arboretum: A Large Multimodal Dataset for Advancing AI for Biodiversity</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<!-- <span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Chih-Hsuan Yang,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Benjamin Feuer,</a></span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Nirmal Baishnab,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Kelly O. Marshall,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Talukder Zaki Jubery,</a></span>
</div>
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Andre Nakkab,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Md Zahid Hasan,</a></span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Shivani Chiranjeevi,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Zi K. Deng,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Asheesh K Singh,</a></span>
</div>
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Arti Singh,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Soumik Sarkar,</a></span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Nirav Merchant,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Chinmay Hegde,</a></span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Baskar Ganapathysubramanian</a></span>
<div class="is-size-5 publication-authors">
<span class="author-block">Institution Name<br>NeurIPS 2024</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<!-- <span class="link-block">
<a href="https://arxiv.org/pdf/<ARXIV PAPER ID>.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span> -->
<!-- Supplementary PDF link -->
<!-- <span class="link-block">
<a href="static/pdfs/supplementary_material.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Supplementary</span>
</a>
</span> -->
<!-- Github link -->
<!-- <span class="link-block">
<a href="https://github.com/BaskarGroup/Arbor-Clip-data-team.git" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span> -->
<!-- ArXiv abstract Link -->
<!-- <span class="link-block">
<a href="https://arxiv.org/abs/<ARXIV PAPER ID>" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
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</div>
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</div>
</section> -->
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title"> BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://www.linkedin.com/in/chih-hsuan-yang-isu/" target="_blank">Chih-Hsuan Yang*,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/benjaminfeuer/" target="_blank">Benjamin Feuer*,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/talukder-zaki-jubery-23322551/" target="_blank">Talukder Zaki Jubery,</a>
</span>
<span class="author-block">
<a href="https://www.arizona.edu/" target="_blank">Zi K. Deng,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/andre-nakkab-2bb42b170/" target="_blank">Andre Nakkab,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/zahid-isu/" target="_blank">Md Zahid Hasan,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/shivani-chiranjeevi/" target="_blank">Shivani Chiranjeevi,</a>
</span>
<span class="author-block">
<a href="https://km3888.github.io/" target="_blank">Kelly O. Marshall,</a>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/nirmal-baishnab-446332a6/" target="_blank">Nirmal Baishnab,</a>
</span>
<span class="author-block">
<a href="https://www.agron.iastate.edu/people/singh-asheesh/" target="_blank">Asheesh K Singh,</a>
</span>
<span class="author-block">
<a href="https://www.agron.iastate.edu/people/singh-arti-2/" target="_blank">Arti Singh,</a>
</span>
<span class="author-block">
<a href="https://www.engineering.iastate.edu/people/profile/soumiks/" target="_blank">Soumik Sarkar,</a>
</span>
<span class="author-block">
<a href="https://datascience.arizona.edu/person/nirav-merchant" target="_blank">Nirav Merchant,</a>
</span>
<span class="author-block">
<a href="https://chinmayhegde.github.io/" target="_blank">Chinmay Hegde,</a>
</span>
<span class="author-block">
<a href="https://www.engineering.iastate.edu/people/profile/baskarg/" target="_blank">Baskar Ganapathysubramanian</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Iowa State University, New York University, University of Arizona<br><b> NeurIPS 2024 Track on Datasets and Benchmarks (Spotlight)</b>
</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Equal Contribution</small></span>
</div>
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/b92854f80ba4feefb973959b259dbc2c-Paper-Datasets_and_Benchmarks_Track.pdf" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Supplementary PDF link -->
<!-- <span class="link-block">
<a href="static/pdfs/supplementary_material.pdf" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Supplementary</span>
</a>
</span> -->
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/baskargroup/BioTrove" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>GitHub</span>
</a>
</span>
<!-- Hugging Face link -->
<span class="link-block">
<a href="https://huggingface.co/datasets/BGLab/BioTrove" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
🤗
</span>
<span>Dataset card</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/BGLab/BioTrove-CLIP" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
🤗
</span>
<span>Model card</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/spaces/BGLab/BioTrove-CLIP-Demo" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
🤗
</span>
<span>BioTrove-CLIP Demo</span>
</a>
</span>
<!-- ArXiv abstract Link -->
<span class="link-block">
<a href="https://arxiv.org/abs/2406.17720" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</section>
<style>
code {
color:black;
}
</style>
<!-- banner image-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="static/images/Biotrove_top_7_phylum.png" alt="ArborCLIP">
<h2 class="subtitle has-text-centered">
<b>Top Seven Phyla in the BioTrove Dataset.</b> This figure displays the seven most
frequently occurring phyla within BioTrove, which is curated to include data exclusively from the
three primary kingdoms: <i>Animalia</i>, <i>Plantae</i> and <i>Fungi</i>. For each phylum, the five most common
species are shown, including their scientific names, common names, and the number of images per
species. The phyla are ordered by species diversity, with the most diverse phylum on the right and
the least diverse on the left.
</h2>
</div>
</div>
</section>
<!-- End banner image -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
We introduce BIOTROVE, the largest publicly accessible dataset designed to advance AI applications in biodiversity.
Curated from the iNaturalist platform and vetted by domain experts to include only research-grade data, BIOTROVE contains 161.9 million images,
offering unprecedented scale and diversity from three primary kingdoms: <i>Animalia</i> ("animals"), <i>Fungi</i> ("fungi"), and <i>Plantae</i> ("plants"), spanning
approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate
AI model development across diverse species and ecosystems. We demonstrate the value of BIOTROVE by releasing a suite of CLIP models trained using a subset of 40 million
captioned images, known as BIOTROVE-TRAIN. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models,
selected for their critical role in biodiversity and agriculture: <i>Aves</i>("birds"),<i>Arachnida</i>("spiders/ticks/mites"),<i>Insecta</i>("insects"),<i>Plantae</i>("plants"),<i>Fungi</i>("fungi"),
<i>Mollusca</i>("snails") and <i>Reptilia</i>("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across
life stages, rare species, confounding species, and multiple taxonomic levels. We anticipate that BIOTROVE will spur the development of AI models capable of supporting digital tools
for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating
the impacts of climate change. BIOTROVE is publicly available, easily accessible, and ready for immediate use.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Image carousel -->
<!-- <section class="hero is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<img src="static/images/carousel1.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
First image description.
</h2>
</div>
<div class="item">
<img src="static/images/carousel2.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Second image description.
</h2>
</div>
<div class="item">
<img src="static/images/carousel3.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Third image description.
</h2>
</div>
<div class="item">
<img src="static/images/carousel4.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Fourth image description.
</h2>
</div>
</div>
</div>
</div>
</section> -->
<!-- End image carousel -->
<!-- data feature -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Dataset Features</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/category_distribution.png" alt="Category Distribution" style="cursor: pointer; width: 80%; height: auto;">
</div>
<p class="description">
<b\>Distribution of the BioTrove dataset.</b> (a) Size of the top seven Phyla in the
BioTrove dataset. (b) Species counts for the top seven Phyla. (c) The 40 highest occurring
species in entire BioTrove dataset.
</p>
<!-- Second Image -->
<div class="publication-image" id="image-container-2">
<img src="static/images/Treemap_BioTrove_cut.png" alt="Sample Image 2" style="cursor: pointer; width: 80%; height: auto;">
</div>
<p class="description">
<b\>Treemap diagram of the BioTrove dataset</b>, starting from Kingdom. The
nested boxes represent phyla, (taxonomic) classes, orders, and families. Box size represents
the relative number of samples.
</p>
<!-- Third Image -->
<div class="publication-image" id="image-container-3">
<img src="static/images/comparison.png" alt="Sample Image 3" style="cursor: pointer; width: 80%; height: auto;">
</div>
<p class="description">
Comparison of BioTrove dataset with existing biodiversity datasets.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Main Datasets Section -->
<section class="hero is-medium" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h1 class="title is-3">Dataset Benchmarks</h1>
<div class="content">
<p>
<b>BioTrove </b> consists of several benchmark datasets - <b>BioTrove-Train(40M)</b> and <b>New Benchmarks</b>. The main BioTrove is ~162M in sample size.
</p>
</div>
</div>
</div>
</section>
<!-- BioTroveTrain Subsection -->
<section class="hero is-small" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h2 class="title is-4">BioTrove-Train</h3>
<div class="content">
<p>
<b>BioTrove-Train</b> is a curated subset comprising approximately 40M samples and 33K species from the seven - <i>Aves</i>, <i>Arachnida</i>, <i>Insecta</i>, <i>Plantae</i>, <i>Fungi</i>, <i>Mollusca</i>, and <i>Reptilia</i> categories (iNaturalist prior to January 27, 2024).
It contains 30 to maximum of 50,000 samples per species. We conducted semi-global shuffling and divided the data into mini-batches of approximately 50,000 samples each. From these
mini-batches, 95% were randomly selected for training and validation, while the remaining 5% were reserved for testing.
</p>
</div>
<div class="image-container" style="text-align: center;">
<img src="static/images/arboretum40m.png" alt="lifestages" style="width: 80%; height: auto;">
<p class="caption has-text-centered">
Training data sources used in BioTrove-Train and Diversity in Different Taxonomy Levels. We integrate taxonomic labels into the images.
</p>
</div>
</div>
</div>
</section>
<!-- New Benchmark Subsection -->
<section class="hero is-small" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h2 class="title is-4">New Benchmark</h3>
<div class="content">
<p>
From <b>BioTrove</b>, we created three new benchmark datasets for fine-grained image classification. They are- <b>BioTrove-Balanced</b>, <b>BioTrove-Unseen</b> and <b>BioTrove-LifeStages</b>.
</p>
</div>
<div class="image-container" style="text-align: center;">
<img src="static/images/lifestages.png" alt="lifestages" style="width: 80%; height: auto;">
<p class="caption has-text-centered">
(a) Example images from BioTrove-Unseen. (b) BioTrove-LIFE-STAGES with 20 class labels: four life stages
(egg, larva, pupa, and adult) for five distinct insect species
</p>
</div>
</div>
</div>
</section>
<!-- dataprep -->
<section class="hero is-small" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h2 class="title is-3">Data Preparation</h2>
<div class="content">
<p>
Our <a href="https://github.com/baskargroup/BioTrove/blob/main/Arbor-preprocess/README_arbor_process.md" target="_blank" class="underline-link">Github</a> includes the pipeline and <a href="https://pypi.org/project/arbor-process/" target="_blank" class="underline-link">biotrove-process</a> package installation instructions for the data preparation.
The metadata can be downloaded from the HuggingFace dataset cards: <a href="https://huggingface.co/datasets/BGLab/BioTrove-Train" target="_blank" class="underline-link">BioTrove-Train</a> and <a href="https://huggingface.co/datasets/BGLab/BioTrove" target="_blank" class="underline-link">BioTrove (main)</a>. This procedure will generate machine learning-ready image-text pairs from the downloaded metadata in four steps:
</p>
</div>
<div class="image-container" style="text-align: center;">
<img src="static/images/dataprep.png" alt="datprep" style="width: 80%; height: auto;">
</div>
</div>
</div>
</section>
<style>
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text-decoration: underline;
}
</style>
<!-- end of dataprep -->
<!-- ArborCLIP model -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">BioTrove-CLIP Model</h2>
<!-- Explanation text space -->
<div class="content">
<p>
We use <b>BioTrove-Train</b> to train new CLIP-style foundation models, and then evaluate them on zero-shot image classification tasks.
A <code>ViT-B/16</code> backbone initialized from the OpenAI <a href="https://arxiv.org/abs/2103.00020" target="_blank" class="underline-link">CLIP</a> weights,
a <code>ViT-L/14</code> from the <a href="https://github.com/facebookresearch/MetaCLIP" target="_blank" class="underline-link">MetaCLIP</a> and
a <code>ViT-B/16</code> from the <a href="https://arxiv.org/abs/2311.18803" target="_blank" class="underline-link">BioCLIP</a> checkpoint were trained to develop these models.
The BioTrove-CLIP models are designed to analyze and categorize various plant species using advanced machine learning techniques.
It leverages a vast dataset and sophisticated algorithms to achieve high accuracy in species recognition.
</p>
</div>
<!-- Static Image with Caption -->
<div class="image-container" style="text-align: center;">
<img src="static/images/bioTroveCLIPresults.png" alt="ArborCLIP Model" style="width: 80%; height: auto; max-height: 400px;">
<p class="caption has-text-centered"><b>BioTrove-CLIP</b> performances well on various benchmarks. The top three rows
are pre-trained checkpoints: OpenAI-B refers to OpenAI's <code>ViT-B-16</code> model, BioCLIP-B
refers to the BioCLIP <code>ViT-B-16</code> model, and MetaCLIP-L refers to the MetaCLIP-cc <code>ViTL-14</code> model.
The bottom three rows are Biotrove-Clip models fine-tuned on different
checkpoints: <code>BT-CLIP-O</code> (from OpenAI-B), <code>BT-CLIP-B</code> (from BioCLIP-B), and <code>BT-CLIP-M</code>
(from MetaCLIP-L). Benchmark abbreviations: <code>BTU</code> (Biotrove-Unseen, <code>n=300</code>), <code>BTB</code>
(Biotrove-Balanced, <code>n=2253</code>), <code>BCR</code> (BioCLIP-Rare, <code>n=400</code>), <code>F</code> (Fungi, <code>n=25</code>), <code>I2</code> (Insects-2,
<code>n=102</code>), <code>B</code> (Birds-525, <code>n=525</code>), <code>LS</code> (Life-Stages, <code>n=20</code>), and <code>DW</code> (DeepWeeds, <code>n=9</code>).<code>95%</code>
confidence intervals <code>(±CI)</code> are included.</p>
</div>
</div>
</div>
</section>
<!-- Model weights -->
<section class="hero is-medium" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h1 class="title is-4">Model Weights</h1>
<div class="content">
<p>
The <b>BioTrove-CLIP</b> models were developed for the benefit of the AI community as an open-source product.
We released three <b>BioTrove-CLIP</b> trained models based on OpenAI's <a href="https://openai.com/index/clip/" target="_blank" class="underline-link">CLIP</a> model.
The models were trained on <b>BioTrove-Train</b> dataset. Please utilize our HuggingFace <a href="https://huggingface.co/BGLab/BioTrove-CLIP" target="_blank" class="underline-link">Model card</a> to access the model checkpoints.
</p>
</div>
</div>
</div>
</section>
<!-- End Model weights -->
<!-- Acknowledgements -->
<section class="hero is-medium" style="margin-bottom: 1rem;">
<div class="hero-body" style="padding-bottom: 1rem;">
<div class="container">
<h1 class="title is-4">Acknowledgements</h1>
<div class="content">
<p>
This work was supported by the AI Research Institutes program supported by the <b>NSF</b> and <b>USDA-NIFA</b> under
<a href="https://aiira.iastate.edu/" target="_blank" class="underline-link">AI Institute: for Resilient Agriculture (AIIRA)</a>,
<code>Award No.2021-67021-35329</code>. This was also partly supported by the NSF under CPS Frontier grant <code>CNS-1954556</code>. Additionally, we gratefully acknowledge the
support of NYU IT - <a href="https://www.nyu.edu/life/information-technology/research-computing-services/high-performance-computing.html"
target="_blank" class="underline-link"> NYU High Performance Computing</a>, <a href="https://sites.google.com/nyu.edu/nyu-hpc/hpc-systems/greene"
target="_blank" class="underline-link"> NYU Greene</a> resources, services, and staff expertise.
</p>
</p>
</div>
</div>
</div>
</section>
<!-- End -->
<!--Team -->
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<div class="rows">
<div class="rows is-centered ">
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<h2 class="title is-3"><span class="dvima">Team</span></h2>
<div class="columns" style="max-width: 95%; padding-left: 10%;">
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/chih-hsuan-yang-isu/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Bella1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Chih-Hsuan Yang</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/benjaminfeuer/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Ben1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Ben Feuer</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/talukder-zaki-jubery-23322551/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Zaki1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Zaki Jubery</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/zi-deng-15483a149/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Zi1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Zi K. Deng</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/andre-nakkab-2bb42b170/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Andre1.png" alt="" style="width: 92%;" /></span>
<span style="font-weight: bold;">Andre Nakkab</span>
</a>
</div>
</div>
<br />
<div class="columns" style="max-width: 95%; padding-left: 10%;">
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/zahid-isu/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Zahid1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Md Zahid Hasan</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/shivani-chiranjeevi/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Shivani1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Shivani Chiranjeevi</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://km3888.github.io/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Kelly1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Kelly Marshall</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.linkedin.com/in/nirmal-baishnab-446332a6/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Nirmal1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Nirmal Baishnab</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.agron.iastate.edu/people/singh-asheesh/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Asheesh1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Asheesh K Singh</span>
</a>
</div>
</div>
<br />
<div class="columns" style="max-width: 95%; padding-left: 10%;">
<div class="column has-text-centered video-column">
<a href="https://www.agron.iastate.edu/people/singh-arti-2/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Arti1.png" alt="" style="width: 92%;" /></span>
<span style="font-weight: bold;">Arti Singh</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.engineering.iastate.edu/people/profile/soumiks/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Soumik1.png" alt="" style="width: 92%;" /></span>
<span style="font-weight: bold;">Soumik Sarkar</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://datascience.arizona.edu/person/nirav-merchant" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Nirav1.png" alt="" style="width: 90%;" /></span>
<span style="font-weight: bold;">Nirav Merchant</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://chinmayhegde.github.io/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Chinmay1.png" alt="" style="width: 95%;" /></span>
<span style="font-weight: bold;">Chinmay Hegde</span>
</a>
</div>
<div class="column has-text-centered video-column">
<a href="https://www.engineering.iastate.edu/people/profile/baskarg/" target="_blank" style="border-bottom: none;">
<span class="image"><img src="static/images/team/Baskar1.png" alt="" style="width: 88%;" /></span>
<span style="font-weight: bold;">Baskar Ganapathysubramanian</span>
</a>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!--End Team -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>
@inproceedings{yang2024biotrovedataset,
title={BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity},
author={Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery, Zi K. Deng, Andre Nakkab, Md Zahid Hasan, Shivani Chiranjeevi, Kelly Marshall, Nirmal Baishnab, Asheesh K Singh, Arti Singh, Soumik Sarkar, Nirav Merchant, Chinmay Hegde, Baskar Ganapathysubramanian},
booktitle={Thirty-eighth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
primaryClass={cs.CV},
url={https://openreview.net/forum?id=DFDCtGQs7S#discussion}</code></pre>
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