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 new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application

Science of The Total Environment(2019)10.1016/j.scitotenv.2019.06.310Source: DataRank Database

A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application is a research paper published in Science of The Total Environment (2019). On theSindex it has a DataRank of 2.4. It has been cited 55 times, with 54 citing works in its 1-hop citation network.

N/A

2.4DataRank · unranked

2.4

55 citations · base score 4.0

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 25%Citation Network 75%

Base Score Contribution

0.604

From this paper's citation signal

Citation Network Contribution

1.8

From 51 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.

An Overview of CMIP5 and the Experiment Design
Bulletin of the American Meteorological Society201214,723 citationsDataRank 21.5Top 4%
A high-resolution projected climate dataset for Vietnam: Construction and preliminary application in assessing future change
Journal of Water and Climate Change202222 citationsDataRank 0.646Top 47%

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 25% comes from its base citations and 75% from the citation network (51 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