metadata

task_categories:
  - zero-shot-classification
  - image-classification
language:
  - en
tags:
  - biology
  - image
  - animals
  - species
  - taxonomy
  - morphology
  - rare species
  - endangered species
  - data deficient species
pretty_name: Rare Species Dataset

Dataset Card for Rare Species Dataset

Dataset Description

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Dataset Summary

This dataset uses information from IUCN Redlist to determine species that are rare (eg., endangered, critically endangered, extinct in the wild, data deficient). From this information, data (images and text) are pulled from other datasets (eg., EOL) to generate a dataset consisting of rare species for zero-shot-classification and more refined image classification tasks. There are approximately 25,000 species that fall into these categories, though the image availability for each species is yet to be determined.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

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Data Fields

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Data Splits

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Dataset Creation

Curation Rationale

This dataset was generated with the purpose of providing a test set for the Imageomics BioCLIP model to demonstrate robustness on data with minimal training samples available.

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

The data (images and text) contain a variety of licensing restrictions mostly within the CC family. Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by licenses in the license file).

Citation Information

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Please be sure to also cite the original data source(s) and all constituent parts as appropriate.

Contributions

The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) Institute program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).