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.

Targeting Glutaminolysis: New Perspectives to Understand Cancer Development and Novel Strategies for Potential Target Therapies

Frontiers in Oncology(2020)10.3389/fonc.2020.589508Source: DataRank Database

Targeting Glutaminolysis: New Perspectives to Understand Cancer Development and Novel Strategies for Potential Target Therapies is a research paper published in Frontiers in Oncology (2020). On theSindex it has a DataRank of 4.6. It has been cited 180 times, with 175 citing works in its 1-hop citation network.

N/A

4.6DataRank · unranked

4.6

180 citations · base score 5.2

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 (0/2)

Interoperable (2/2)

DataCite relations
Linked datasets

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 17%Citation Network 83%

Base Score Contribution

0.780

From this paper's citation signal

Citation Network Contribution

3.8

From 153 citing papers with measurable signal

Learn more about DataRank methodology →

Top 2 citers driving the network score

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

Hallmarks of Cancer: The Next Generation
Cell201165,643 citationsDataRank 1.7
Glutaminase inhibition as potential cancer therapeutics: current status and future applications
Journal of Enzyme Inhibition and Medicinal Chemistry202430 citationsDataRank 0.787

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 17% comes from its base citations and 83% from the citation network (153 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 →

Click a node to highlight its connections. Use scroll to zoom. Drag to pan.

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