The Power of Open Health Data: Impact, Representation, and Knowledge Diffusion

Area of Science:

• Bibliometrics and Health Data Science
• Scholarly Impact Assessment
• Research Community Analysis

Background:

• Billions in public funding support open health data repositories.
• No standardized framework exists to evaluate their scholarly impact, community composition, or disciplinary reach.
• A novel two-degree citation methodology is introduced to assess knowledge diffusion from open data.

Purpose of the Study:

• To quantify the downstream scholarly impact of major open health data repositories.
• To analyze the composition and diversity of research communities utilizing these repositories.
• To evaluate knowledge diffusion normalized by repository funding.

Main Methods:

• Utilized the OpenAlex bibliometric database (Jan-Feb 2026) to identify first-degree (n=30,049) and second-degree (n=485,396) citing publications.
• Analyzed four repositories: MIMIC, UK Biobank, OpenSAFELY, and All of Us, considering their funding and data types.
• Extracted author demographics (gender, institutional country income) and research topics, using chi-square tests and odds ratios for comparisons.

Main Results:

• Funding-normalized output varied significantly (689 to 1 paper/$1M), influenced by repository type (retrospective vs. prospective).
• A consistent citation amplification ratio of 9.3-11.5× was observed across repositories.
• Significant demographic differences were found: LMIC authorship ranged from 4.3% to 41.8%, and female authorship from 31.8% to 43.2%.

Conclusions:

• Open health data generates substantial indirect citation impact (∼10× amplification).
• Low-barrier access and community building foster diverse research communities, including LMIC investigators in leadership roles.
• Persistent gender gaps in senior authorship indicate structural inequities not solely addressed by data access policies.