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.

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

International Journal of Remote Sensing(2010)10.1080/01431160903401387Source: DataRank Database

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data is a research paper published in International Journal of Remote Sensing (2010). On theSindex it has a DataRank of 2.2. It has been cited 55 times, with 42 citing works in its 1-hop citation network.

N/A

2.2DataRank · unranked

2.2

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 28%Citation Network 72%

Base Score Contribution

0.604

From this paper's citation signal

Citation Network Contribution

1.6

From 38 citing papers with measurable signal

Learn more about DataRank methodology →

Top 3 citers driving the network score

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

Red and photographic infrared linear combinations for monitoring vegetation
Remote Sensing of Environment197911,226 citationsDataRank 1.4
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Remote Sensing of Environment20029,472 citationsDataRank 1.4
A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan
Remote Sensing20208 citationsDataRank 0.506

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 28% comes from its base citations and 72% from the citation network (38 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