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Zmeos/Compact_OpenAIRE_citation_graph

📚 Compact OpenAIRE Citation Graph Based on OpenAIRE Graph v11.1.1 (source on Zenodo). The complete OpenAIRE citation graph, distilled into a handful of compact, analysis-ready files — the full scholarly citation network of the open-science ecosystem, small enough to actually work with. Citation graphs at this scale are usually locked behind multi-terabyte dumps and heavyweight infrastructure. This dataset makes the entire OpenAIRE citation network loadable… See the full description on the dataset page: https://huggingface.co/datasets/Zmeos/Compact_OpenAIRE_citation_graph.

sourceHugging Facecc-by-4.0updated 3mo agoView on Hugging Face
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Dataset Card

<div style="display: flex; gap: 8px; align-items: center; flex-wrap: wrap; margin-bottom: 16px;"> <a href="https://doi.org/10.5334/johd.520"><img src="https://img.shields.io/badge/Paper-JOHD%2012(1)%2063-blue" alt="Paper"></a> <a href="https://doi.org/10.5281/zenodo.18402099"><img src="https://img.shields.io/badge/Zenodo-10.5281%2Fzenodo.18402099-blue" alt="Zenodo DOI"></a> <a href="https://graph.openaire.eu/docs/"><img src="https://img.shields.io/badge/docs-OpenAIRE%20Graph-informational" alt="OpenAIRE Graph Docs"></a> <img src="https://img.shields.io/badge/license-CC--BY--4.0-green" alt="License"> </div>

📚 Compact OpenAIRE Citation Graph

<!-- BASED-ON:START --> Based on OpenAIRE Graph v11.1.1 ([source on Zenodo](https://zenodo.org/records/20428976)). <!-- BASED-ON:END -->

The complete OpenAIRE citation graph, distilled into a handful of compact, analysis-ready files — the full scholarly citation network of the open-science ecosystem, small enough to actually work with.

Citation graphs at this scale are usually locked behind multi-terabyte dumps and heavyweight infrastructure. This dataset makes the entire OpenAIRE citation network loadable on a normal machine: publications as nodes, citations as edges, served as compressed Parquet with a memory-efficient loading path. A richer node file adds titles, abstracts, authors, dates, and a full set of persistent identifiers (DOI, PubMed, MAG, arXiv, and more) for text-attributed and metadata-driven work.

Please cite the paper if you use this data.

At a glance

NodesScholarly publications
EdgesCitation links
FormatParquet (PyArrow-friendly)
Node metadataTitles, abstracts, authors, dates, venues, PIDs
Best forGraph ML · temporal / dynamic graphs · text-attributed graphs · bibliometrics
LicenseCC-BY-4.0
CiteSkarding & Sanda (2026), *JOHD* 12(1), 63
Dataset DOI10.5281/zenodo.18402099

Dataset structure

The dataset is a directed citation graph: publications are nodes, citations are edges.

FileRoleSize (Parquet)Number of entries
citations.parquetEdges — the citation links between publications9.2 GB~2.37B
publications_large.parquetNodes with additional metadata fields (see below)75.5 GB~216M

Features/columns in publications_large

FieldTypeDescriptionMemory (GB)Filled
nodeIdint32Unique internal identifier for the node (publication)0.8100.00%
openaireIdstrIdentifier assigned by the OpenAIRE platform10.1100.00%
titlestrTitle of the publication17.399.40%
authorslist[str]List of authors associated with the publication11.683.78%
descriptionstrAbstract or short description of the publication137.657.10%
datedatetimeDate when the publication was published0.897.33%
containerstrJournal, conference, or repository where it was published5.768.35%
citationsintNumber of times the publication has been cited1.697.62%
languagestrLanguage in which the publication is written1.5100.00%
pid_doislist[str]DOI identifiers5.980.60%
pid_mag_idslist[str]MAG IDs2.039.50%
pid_pmidslist[str]PubMed IDs1.318.14%
pid_handleslist[str]Persistent handles1.28.35%
pid_pmcslist[str]PubMed Central IDs1.04.78%
pid_arxiv_idslist[str]ArXiv IDs0.91.38%

Quickstart

python
import pandas as pd
from huggingface_hub import hf_hub_download

REPO = "Zmeos/Compact_OpenAIRE_citation_graph"

# citations (edges)
cites = pd.read_parquet(
    hf_hub_download(REPO, "citations.parquet", repo_type="dataset"),
    engine="pyarrow", dtype_backend="pyarrow",
)

# publications_large (nodes + metadata) — may not fit in memory;
# select only the columns you need
large = pd.read_parquet(
    hf_hub_download(REPO, "publications_large.parquet", repo_type="dataset"),
    columns=["nodeId", "title", "pid_dois"],
    engine="pyarrow", dtype_backend="pyarrow",
)

Reproducibility

The PySpark pipeline used to produce these files (with a Singularity/Apptainer container for portability) is archived on Zenodo as pipeline.tar.xz and maintained at Codeberg.

License

Creative Commons Attribution 4.0 International (CC-BY-4.0).

Citation (paper)

When using this dataset, please cite the accompanying article:

Skarding, J. and Sanda, P. (2026) 'Making the Complete OpenAIRE Citation Graph Easily Accessible Through Compact Data Representation', Journal of Open Humanities Data, 12(1), p. 63. https://doi.org/10.5334/johd.520
bibtex
@article{skarding2026openaire,
  author  = {Skarding, Joakim and Sanda, Pavel},
  title   = {Making the Complete OpenAIRE Citation Graph Easily Accessible Through Compact Data Representation},
  journal = {Journal of Open Humanities Data},
  volume  = {12},
  number  = {1},
  pages   = {63},
  year    = {2026},
  doi     = {10.5334/johd.520}
}

Dataset archive: Skarding, J. and Sanda, P. (2026). Compact representation of the OpenAIRE citation graph [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18402099 ---