Datasets:

almanach
/

halvest-geometric

pretty_name: HALvest-Geometric
license: cc-by-4.0
configs:
  - config_name: en
    data_files: en/*.gz
  - config_name: fr
    data_files: fr/*.gz
  - config_name: ict-2
    data_files:
      - split: train
        path: ict-2/train-*
  - config_name: ict-4
    data_files:
      - split: train
        path: ict-4/train-*
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
dataset_info:
  - config_name: ict-2
    features:
      - name: halid
        dtype: string
      - name: year
        dtype: string
      - name: affiliations
        sequence: string
      - name: domains
        sequence: string
      - name: authors
        sequence: string
      - name: query
        dtype: string
      - name: positive
        dtype: string
      - name: negative
        dtype: string
    splits:
      - name: train
        num_bytes: 1665821697
        num_examples: 976619
    download_size: 954211030
    dataset_size: 1665821697
  - config_name: ict-4
    features:
      - name: halid
        dtype: string
      - name: year
        dtype: string
      - name: affiliations
        sequence: string
      - name: domains
        sequence: string
      - name: authors
        sequence: string
      - name: query
        dtype: string
      - name: positive
        dtype: string
      - name: negative
        dtype: string
    splits:
      - name: train
        num_bytes: 3185622246
        num_examples: 971620
    download_size: 1747142363
    dataset_size: 3185622246

HALvest-Geometric

Citation Network of Open Scientific Papers Harvested from HAL

Dataset Description

Repository: GitHub

Dataset Summary

overview:

French and English fulltexts from open papers found on Hyper Articles en Ligne (HAL) and its citation network.

You can download the dataset using Hugging Face datasets:

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 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

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}, 
}