Applications
Emory's OMOP ecosystem supports two ways to work with the data — visual tools for point-and-click analysis, and direct code access for full flexibility. Many researchers use both.
Web-based tools for cohort building, data quality assessment, and standardized analyses — no coding required.
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ATLAS
Define study populations, explore vocabularies, and run characterizations through a web-based interface.
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Data Quality Dashboard
Assess data completeness, conformance, and plausibility across Emory's OMOP instance.
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ARES
Explore data source characterization, quality metrics, and concept-level analysis across Emory's OMOP data.
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CohortDiagnostics
Evaluate cohort definitions with standardized analyses.
On Emory's roadmap — not yet available.
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PhenotypeLibrary
Reusable phenotype repository for sharing and reusing cohort definitions across studies.
On Emory's roadmap — not yet available.
Write SQL, R, or Python against Emory's OMOP data lake on Redshift. Full access to every table, every column.
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R & RStudio
The HADES ecosystem, DatabaseConnector, CohortGenerator, and 100+ OHDSI packages — the most mature OHDSI toolchain.
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SQL
Direct Redshift queries using DBeaver, DataGrip, or any SQL client. Emory maintains a curated query library.
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Python
Connect with
redshift_connector, analyze with pandas, and build custom pipelines.
Not sure where to start?
If you're new to OMOP, start with ATLAS to explore concepts and build your first cohort visually. When you need more flexibility, move to SQL or R for custom queries. See our Training page for a recommended learning path.