---
pretty_name: HALvest-Geometric

license: cc-by-4.0

configs:
- config_name: en
  data_files: "en/*.gz"
- config_name: fr
  data_files: "fr/*.gz"

language:
- en
- fr

size_categories:
  - 100K<n<1M

task_categories:
- text-generation
- fill-mask

task_ids:
- language-modeling
- masked-language-modeling

tags:
- academia
- research
- graph

annotations_creators:
- no-annotation

multilinguality:
- multilingual

source_datasets:
- HALvest
---


<div align="center">
    <h1> HALvest-Geometric </h1>
    <h3> Citation Network of Open Scientific Papers Harvested from HAL </h3>
</div>

---


## Dataset Description

- **Repository:** [GitHub](https://github.com/Madjakul/HALvesting-Geometric)


## Dataset Summary

### overview:

French and English fulltexts from open papers found on [Hyper Articles en Ligne (HAL)](https://hal.science/) and its citation network.

You can download the dataset using Hugging Face datasets:
```py
from datasets import load_dataset

ds = load_dataset("Madjakul/HALvest-Geometric", "en")
```


### Details

#### Nodes

* Papers: 18,662,037
* Authors: 238,397
* Affiliations: 96,105
* Domains: 16

#### Edges

- Paper <-> Domain: 136,700
- Paper <-> Paper: 22,363,817
- Author <-> Paper: 238,397
- Author <-> Affiliation: 426,030


### Languages

ISO-639|Language|# Documents|# mT5 Tokens
-------|--------|-----------|--------
en|English|442,892|7,606,895,258
fr|French|193,437|8,728,722,255


## Considerations for Using the Data

The corpus is extracted from the [HAL's open archive](https://hal.science/) which distributes scientific publications following open access principles. The corpus is made up of both creative commons licensed and copyrighted documents (distribution authorized on HAL by the publisher). This must be considered prior to using this dataset for any purpose, other than training deep learning models, data mining etc. We do not own any of the text from which these data has been extracted.


## Dataset Copyright

The licence terms for HALvest strictly follows the one from HAL. Please refer to the below license when using this dataset.
- [HAL license](https://doc.archives-ouvertes.fr/en/legal-aspects/)


## Citation

```
@misc{kulumba2024harvestingtextualstructureddata,
      title={Harvesting Textual and Structured Data from the HAL Publication Repository}, 
      author={Francis Kulumba and Wissam Antoun and Guillaume Vimont and Laurent Romary},
      year={2024},
      eprint={2407.20595},
      archivePrefix={arXiv},
      primaryClass={cs.DL},
      url={https://arxiv.org/abs/2407.20595}, 
}
```