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.

Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures - Supplementary material

(2022)10.5281/zenodo.6112762Source: DataRank Database

Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures - Supplementary material is a dataset (2022). On theSindex it has a DataRank of 0.104, placing it in the top 62.1% of the data-sharing corpus. It has been cited 1 time. Its calibrated FAIR score is 29/100.

Top 62%percentile

0.104DataRank

0.104Top 62%

Dataset Open Access1 citations · base score 0.693

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

This dataset contains supplementary material for the paper 'Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures' by Alexis-Michel Mugabushaka, Nees Jan van Eck, and Ludo Waltman. supplementary_material_1_dataset.ods: Dataset of Covid-19 publications. supplementary_material_2_sample.ods: Samples of publications used to assess the accuracy of funding data in the different databases. supplementary_material_3_tables_and_figures.ods: Statistics underlying the tables and figures presented in the paper.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (1/3)

Dataset classification

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

29FAIR score

F Findable

A Accessible

I Interoperable

R Reusable

Top 90% by FAIRLLM-assessed⚠ abstract only

Estimated from the abstract only. The agent couldn't read this paper's full text, so body-dependent criteria (data-availability statement, formats, license) are inferred. For a confident score, upload the PDF or supply full text →

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.104

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

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 →