Datasets:
license: cc-by-4.0
task_categories:
- graph-ml
language:
- en
pretty_name: Compact OpenAIRE Citation Graph
size_categories:
- 1B<n<10B
tags:
- citation-graph
- citation-network
- scholarly-data
- bibliometrics
- scientometrics
- openaire
- graph
- dynamic-graph
- temporal-graph
- dynamic-network
- link-prediction
configs:
- config_name: citations
data_files: citations.parquet
- config_name: publications_large
data_files: publications_large.parquet
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📚 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 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
| Nodes | Scholarly publications |
| Edges | Citation links |
| Format | Parquet (PyArrow-friendly) |
| Node metadata | Titles, abstracts, authors, dates, venues, PIDs |
| Best for | Graph ML · temporal / dynamic graphs · text-attributed graphs · bibliometrics |
| License | CC-BY-4.0 |
| Cite | Skarding & Sanda (2026), JOHD 12(1), 63 |
| Dataset DOI | 10.5281/zenodo.18402099 |
Dataset structure
The dataset is a directed citation graph: publications are nodes, citations are edges.
| File | Role | Size (Parquet) | Number of entries |
|---|---|---|---|
citations.parquet |
Edges — the citation links between publications | 9.2 GB | ~2.37B |
publications_large.parquet |
Nodes with additional metadata fields (see below) | 75.5 GB | ~216M |
Features/columns in publications_large
| Field | Type | Description | Memory (GB) | Filled |
|---|---|---|---|---|
nodeId |
int32 | Unique internal identifier for the node (publication) | 0.8 | 100.00% |
openaireId |
str | Identifier assigned by the OpenAIRE platform | 10.1 | 100.00% |
title |
str | Title of the publication | 17.3 | 99.40% |
authors |
list[str] | List of authors associated with the publication | 11.6 | 83.78% |
description |
str | Abstract or short description of the publication | 137.6 | 57.10% |
date |
datetime | Date when the publication was published | 0.8 | 97.33% |
container |
str | Journal, conference, or repository where it was published | 5.7 | 68.35% |
citations |
int | Number of times the publication has been cited | 1.6 | 97.62% |
language |
str | Language in which the publication is written | 1.5 | 100.00% |
pid_dois |
list[str] | DOI identifiers | 5.9 | 80.60% |
pid_mag_ids |
list[str] | MAG IDs | 2.0 | 39.50% |
pid_pmids |
list[str] | PubMed IDs | 1.3 | 18.14% |
pid_handles |
list[str] | Persistent handles | 1.2 | 8.35% |
pid_pmcs |
list[str] | PubMed Central IDs | 1.0 | 4.78% |
pid_arxiv_ids |
list[str] | ArXiv IDs | 0.9 | 1.38% |
Quickstart
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
@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}
}