Datasets:

nasa-impact
/

EO-via-NLP

license: cc-by-4.0
language:
  - en
tags:
  - earth-science
  - named-entity-recognition
  - natural-language-processing
  - agentic-ai
  - multimodal
  - scientific-workflows
pretty_name: Open Earth Science (OES) via NLP Dataset

Dataset Summary

Toward Open Earth Science as Fast and Accessible as Natural Language

This dataset was curated to accompany the EO-via-NLP code and the following paper:

Ellis, M., Gurung, I., Ramasubramanian, M., & Ramachandran, R. (2025).
Toward Open Earth Science as Fast and Accessible as Natural Language.
arXiv:2505.15690

Supported Tasks

This dataset was primarily designed for:

Named Entity Recognition (NER) in earth science contexts.

Languages

English (en)

Intended Use

This dataset is intended for:

Research in agentic AI systems for Earth science,
Development of natural language interfaces for software and scientific workflows,
Benchmarking multimodal and task-oriented models in geoscience contexts.

How to Use

See the EO-via-NLP repository.

To simply load it:

from datasets import load_dataset

dataset = load_dataset("nasa-impact/EO-via-NLP")

Dataset Creation

This dataset was curated by the authors of the accompanying paper and EO-via-NLP codebase. All annotations and selections were created by humans.

License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

You are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially

Attribution is required. Please cite the following work when using this dataset:

Citation

BibTeX

@article{ellis2025oes,
  title={Toward Open Earth Science as Fast and Accessible as Natural Language},
  author={Ellis, Marquita and Gurung, Iksha and Ramasubramanian, Muthukumaran and Ramachandran, Rahul},
  journal={arXiv preprint arXiv:2505.15690},
  year={2025},
  month={May},
  doi={10.48550/arXiv.2505.15690},
  url={https://arxiv.org/abs/2505.15690}
}

Please open an issue or discussion on the Hugging Face dataset page, or contact the authors via the corresponding author listed in the paper.

README Attribution

This README was drafted with assistance from Copilot, an AI system based on GPT-4, to ensure clarity, consistency, and alignment with Hugging Face dataset standards.