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.

A Novel Protein Elicitor BAR11 From Saccharothrix yanglingensis Hhs.015 Improves Plant Resistance to Pathogens and Interacts With Catalases as Targets

Frontiers in Microbiology(2018)10.3389/fmicb.2018.00700Source: DataRank Database

A Novel Protein Elicitor BAR11 From Saccharothrix yanglingensis Hhs.015 Improves Plant Resistance to Pathogens and Interacts With Catalases as Targets is a research paper published in Frontiers in Microbiology (2018). On theSindex it has a DataRank of 0.533. It has been cited 26 times, with 1 citing works in its 1-hop citation network.

N/A

0.533DataRank · unranked

0.533

26 citations · base score 3.3

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.

Run a calibrated FAIR evaluation for this paper →

DataRank Breakdown

Base Score 93%Citation Network 7%

Base Score Contribution

0.494

From this paper's citation signal

Citation Network Contribution

0.0391

From 1 citing papers with measurable signal

Learn more about DataRank methodology →

Top 1 citer driving the network score

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

A new elicitor of the hypersensitive response in tobacco: a fungal glycoprotein elicits cell death, expression of defence genes, production of salicylic acid, and induction of systemic acquired resistance
The Plant Journal1995105 citationsDataRank 5.2

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 93% comes from its base citations and 7% 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.

Read the full methodology →

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

Authors (5)

Xia Yan,Hongmei Guo,Feiyang Zhao,Lili HuangORCID,Yanan Zhang