Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth

Cancers(2025)10.3390/cancers17203361Source: DataRank Database

Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth is a research paper published in Cancers (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access1 citations

Free full text

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)OA: gold

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

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 →

We only score data papers we can read in full — never from an abstract alone.

Authors (3)

Thomas E. Yankeelov ORCID,David A. Hormuth ORCID,Megan F. LaMonica

Live enrichment

National Cancer Institute

Grant: U24CA226110

National Cancer Institute

Grant: U01253540

National Cancer Institute

Grant: 1R01CA260003

National Science Foundation

Grant: DMS 2436499

Cancer Prevention and Research Institute of Texas

Grant: RR16005

Cancer Prevention and Research Institute of Texas

Grant: RP220225

American Cancer Society

Grant: IRG-21-135-01-IRG

goldLicense: cc-by

publisherhttps://www.mdpi.com/2072-6694/17/20/3361/pdf?version=1760760381

publisherhttps://www.mdpi.com/2072-6694/17/20/3361/pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12564088