Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

The Pascal Visual Object Classes (VOC) Challenge

International Journal of Computer Vision(2009)10.1007/s11263-009-0275-4Source: DataRank Database

The Pascal Visual Object Classes (VOC) Challenge is a dataset published in International Journal of Computer Vision (2009). On theSindex it has a DataRank of 26.7, placing it in the top 1.1% of the data-sharing corpus. It has been cited 19,428 times, with 194 citing works in its 1-hop citation network. Its calibrated FAIR score is 65/100.

Top 1%percentile

26.7DataRank

26.7Top 1%

Dataset Open Access19428 citations · base score 9.8

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (1/3)

Dataset classification

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

65FAIR score

F Findable

100

A Accessible

I Interoperable

R Reusable

Top 4% by FAIRdeterministic✓ full text read

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 6%Citation Network 94%

Base Score Contribution

1.5

From this paper's citation signal

Citation Network Contribution

25.3

From 194 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

Ranked by citation count — the same ordering the engine uses when summing log1p(C_q) over citers.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence201753,338 citationsDataRank 1.6
ImageNet Large Scale Visual Recognition Challenge
International Journal of Computer Vision201540,012 citationsDataRank 1.6
Histograms of Oriented Gradients for Human Detection
200531,745 citationsDataRank 1.6
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
2014 IEEE Conference on Computer Vision and Pattern Recognition201431,492 citationsDataRank 1.6
Fast R-CNN
2015 IEEE International Conference on Computer Vision (ICCV)201527,585 citationsDataRank 1.5

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 6% comes from its base citations and 94% from the citation network (194 citing papers contributed measurable signal).

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank