If you've worked in the OHDSI ecosystem, you know the pain: Atlas for cohort definitions, Achilles Results Viewer for characterization, a DQD dashboard for data quality, spreadsheets for feasibility assessments, and a prayer that everyone's looking at the same release of the same data. Ares changes that. Today we're announcing Ares v2 — Parthenon's network-level data observatory — a single unified module that replaces the fragmented constellation of OHDSI data characterization tools with 10 purpose-built analytical panels, 60+ API endpoints, and a clinical UI designed for researchers who need answers, not workarounds.

This is the biggest feature release in Parthenon's history.

Today was one of those satisfying days where two major workstreams converged: we pushed the Ares data quality module from skeleton to a fully featured analytics suite with four distinct intelligence phases, and we permanently fixed a cluster of compounding bugs that had been making Achilles characterization runs fragile on large real-world datasets. Both efforts move Parthenon meaningfully closer to being a production-grade OHDSI research platform.

One of OHDSI's greatest strengths is database agnosticism. The HADES ecosystem — via SqlRender and DatabaseConnector — lets researchers write analyses once and run them against SQL Server, PostgreSQL, Oracle, Snowflake, BigQuery, and seven other platforms without modification. Today, Parthenon achieved full parity with that capability: all 12 HADES-supported database dialects are now covered across both the PHP SQL translator and the R runtime.

After months of a perpetually red CI pipeline, today marks a turning point for Parthenon: 92 commits, a full-spectrum codebase review, a complete AtlanticHealth patient synthesis pipeline, and — most satisfying of all — every CI job green. Here's how we got there.

A quieter day on the Parthenon platform — today's commits centered on documentation infrastructure rather than feature work, with automated help content synchronization and the daily development blog pipeline keeping the platform's knowledge base fresh and up to date.

Eighteen Docker services. Three environment files. A reverse proxy with auto-TLS. Database admin GUI. Container management dashboard. Enterprise SSO. And if you want the full stack? One command:

python3 install.py --with-infrastructure

This is the story of how we built Acropolis — the infrastructure layer that turns Parthenon from a research application into a production platform — and what we learned when we decided to ship it inside the same repository.

Every platform has a weak link. For Parthenon, it was the R container.

PHP handled 200 concurrent API requests without breaking a sweat. Python served AI inference with async workers. PostgreSQL managed million-row queries across six schemas. Redis cached sessions at sub-millisecond latency. And then there was R — single-threaded, fragile, running bare Rscript as PID 1 with no supervision, no timeouts, and a health check that lied.

This is the story of how we tore it down and built Darkstar — a production-grade R analytics engine that runs OHDSI HADES analyses concurrently, recovers from crashes automatically, and executes 35% faster than the container it replaced.

A big architectural day on Parthenon: we shipped the new Workbench launcher experience and drafted the formal plan to collapse our multi-database mess into a single, schema-isolated parthenon database. A noticeably cleaner codebase on the other side.

A massive 116-commit push today centered almost entirely on maturing the Evidence Investigation workbench — from retiring the old FinnGen UI to hardening the investigation experience with proper navigation, KPI metrics, URL-synced state, and ARIA accessibility. We also landed multi-dataset support in Morpheus and set the stage for one of the most requested features on the roadmap: volcano plots powered by the newly-renamed Darkstar R runtime.

A big day on the quality and resilience front: 34 commits landed in Parthenon focused on a comprehensive codebase health audit, a major expansion of our Playwright E2E test suite, and a fork of the StudyAgent submodule. No flashy new features today — instead, we did the unglamorous but essential work of making sure what we've already built actually works, is safe to change, and won't silently break in production.