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.

Efficient Parallel Processing of Analytical Queries on Linked Data

Lecture Notes in Computer Science(2013)10.1007/978-3-642-41030-7_33Source: DataRank Database

Efficient Parallel Processing of Analytical Queries on Linked Data is a research paper published in Lecture Notes in Computer Science (2013). On theSindex it has a DataRank of 0.221. It has been cited 3 times, with 1 citing works in its 1-hop citation network.

N/A

0.221DataRank · unranked

0.221

3 citations · base score 1.4

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 (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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

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DataRank Breakdown

Base Score 94%Citation Network 6%

Base Score Contribution

0.208

From this paper's citation signal

Citation Network Contribution

0.0131

From 1 citing papers with measurable signal

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Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 94% comes from its base citations and 6% from the citation network (1 citing paper 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.

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