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.

Try it now Read the methodology

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 their DataRank percentile (1–100), 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*Σ[log1p(Cq)/outdeg(q)], with d=0.85 and self-citation filtering.

Percentile & Ranking

Papers are ranked by their DataRank percentile (1–100), 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.

Team

Meet the computational scientists and open-science advocates behind DataRank.

Meet the team

Resources

Open-source notebooks, datasets, APIs, and models — all freely available.

View artifacts