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.

Computation of atomic electronegativity values using atomic and covalent potential: a FSGO based study

Journal of Mathematical Chemistry(2022)10.1007/s10910-022-01376-1Source: DataRank Database

Computation of atomic electronegativity values using atomic and covalent potential: a FSGO based study is a research paper published in Journal of Mathematical Chemistry (2022). On theSindex it has a DataRank of 0.340. It has been cited 5 times, with 2 citing works in its 1-hop citation network.

N/A

0.340DataRank · unranked

0.340

5 citations · base score 1.8

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 (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 79%Citation Network 21%

Base Score Contribution

0.269

From this paper's citation signal

Citation Network Contribution

0.0708

From 2 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.

Concerning electronegativity as a basic elemental property and why the periodic table is usually represented in its medium form
Foundations of Chemistry201345 citationsDataRank 1.9

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 79% comes from its base citations and 21% from the citation network (2 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