UCSF’s Information Commons...
Harnessing the power of machine intelligence to advance precision medicine.

What is Information Commons?

Information Commons is a fast and easily searchable and accessible repository of all UCSF clinical data and models, and related basic science & population data, that enables UCSF research and discovery of new health insights underlying precision medicine, to improve patient and health care.


Linkable multi-modal clinical data

  • 5.5M+ Patients of UCSF Health
  • 100M+ Encounters
  • 40+ years of clinical activity
  • 120M+ Clinical Notes
  • 7.5M+ Clinical Images
  • Genomics Coming Soon!

Deidentified * Standardized * No-IRB-Access

Learn about the Data


Tools to support all parts of research workflow

  • Easy-to-use data exploration (PatientExploreR, OHDSI Atlas, cBioPortal, EMERSE, MIX)
  • Data processing and analysis (Jupyter, Dask, Spark, R and Python machine & deep learning libraries)
  • NLP and text processing (concept extraction, deidentification, annotation)
  • Image processing (ask Jason Crane for examples)

Learn about the Tools


A variety of computing environments that serve a wide range of research needs. Ready to use with

  • Pre-installed data analysis and machine learning applications
  • Hosted and maintained UCSF research data assets
  • Cloud-based autoscaling capabilities
  • Secure on-premise workspaces for PHI-compliant analysis
  • On-demand access to GPUs
  • Access to UCSF Enterprise GitHub for secure version control

Learn about the Infrastructure



Information Commons is currently in beta.  For a full list of features, and how to gain early access, see our wiki.

If you are a UCSF researcher with a computational project, Information Commons can offer a number of options to suit your research needs. 

See our Wiki


Information Commons specializes in supporting integrative research.  Clinical data, doctors’ notes, radiology images, specialist and lab reports – all are used in multifactor studies, with many more forms to come.


See our Featured Research