About DataRank
The metric data sharing has been waiting for
theSindex.org computes DataRank scores for scientific papers using a citation-only 1-hop model built from seed citation counts and citer citation-network signal. FAIR and DataCite metadata are preserved for context and transparency.
Multi-Source Dataset
Papers from the NIH DOI metadata repository, enriched with OpenAlex citation graphs plus FAIR/DataCite context metadata.
DataRank Engine
Citation-only 1-hop approximation with damping d=0.85: base score from seed citations plus propagated citer citation signal.
Percentile Ranking
Papers are ranked by DataRank percentile and placed into five tiers (S1βS5), giving researchers, funders, and institutions a clear benchmark.
The pipeline
From DOI to DataRank
Five stages transform raw metadata into a percentile-ranked citation-network score with explicit FAIR/DataCite context.
Data Ingestion
We parse DOI metadata and enrich each paper with DataCite/FAIR/repository context for interpretation and UI display.
FAIR Checklist
Each paper is evaluated against a FAIR checklist for context. FAIR signals are surfaced to users but are not used in v4 scoring.
Citation Graph
We fetch the citer neighbourhood from OpenAlex, building a directed graph that captures the flow of scholarly influence.
DataRank Computation
Citation-only 1-hop model: DataRank(p)=(1-d)*log1p(Cp)+d*Ξ£[log1p(Cq)/outdeg(q)], with d=0.85 and self-citation filtering.
Percentile & Ranking
Papers are ranked by their DataRank percentile and assigned to tiers (S1βS5) β placing each paper in context against all others in the corpus.
Go deeper
Explore the methodology, meet the team, or browse the open-source tools we've built.
Methodology
Full technical details on the citation-only 1-hop scoring system (v4.0) and percentile classification.
Read moreTeam
Meet the computational scientists and open-science advocates behind DataRank.
Meet the teamResources
Open-source notebooks, datasets, APIs, and models β all freely available.
View artifacts