Self-Serve Research Data Assets & Information Commons

Questions? Contact Academic Research Services (ARS)

Overview

Access UCSF’s integrated ecosystem of research data, tools, and secure environments to explore ideas, build analyses, and accelerate discovery—now enhanced by AI-powered workflows.

Information Commons

UCSF’s ecosystem for data-driven and AI-enabled research

The Information Commons is a comprehensive ecosystem of data, tools, secure computational and analysis environments, and community & support services that enables the full cycle of data-driven research. It welcomes self-service use of the data and resources while providing support with a talented team of informatics and data science experts and a growing research data science community. 

Information Commons aims to minimize the time researchers and data scientists need to spend on preparation for projects and get to answering the research question as soon as possible. Its vision is to enable scientific discovery and its translation to clinical care and precision medicine by lowering barriers to access and linking together data, computational capabilities, and shared knowledge.

From Research Question to Insight—Faster

Many researchers experience the same challenge: 
They have an important question. They know the data exists. But getting from that question to a working analysis—finding the right data, setting up a compute environment, and building analysis workflows — can take months. 

The Information Commons is designed to change that. 

By connecting data, tools, environments, and community into a unified ecosystem, IC enables researchers to move more efficiently from idea to data to analysis toward discovery.  

Today, this transformation is being further accelerated by artificial intelligence. Increasingly, AI is becoming the interface between researchers and the IC—enabling natural-language interaction with data and tools, and dramatically reducing the distance between a research question and the analysis needed to answer it. 

A Connected Research Ecosystem

Data | Tools | Environments | Community

The Information Commons connects: 

  • Large-scale multimodal clinical data 
  • Secure, scalable compute environments 
  • Specialized research tools and applications 
  • A collaborative support community 

Together, these components allow researchers to move seamlessly from data exploration to analysis to modeling to deployment, supporting everything from observational studies to advanced AI development.

The Four Pillars of the Information Commons

Data

The IC provides access to linked, de-identified large-scale multi-modal clinical and biomedical data, including: 

  • Structured EHR data (diagnoses, labs, medications, encounters, demographics, etc.) 
  • Clinical notes and text-derived concepts 
  • Imaging and radiology data 
  • Genomic testing datasets 

These datasets span several health systems (UCSF Health, SF DPH, Fresno CHS) and decades of clinical activity, enabling both population-scale research and AI model development. 

👉 Explore: Information Commons Data 

Tools

The IC includes a growing ecosystem of tools and applications that support: 

  • Cohort discovery and data exploration 
  • Clinical text search and abstraction 
  • Knowledge graph and multi-omics exploration 
  • Code-based analytics (Python, R, Jupyter) 
  • Reusable analytics patterns and workflows 

These tools support both no-code exploration and advanced computational research, meeting researchers where they are. 

👉 Explore: Information Commons Tools

Environments 

The IC is powered by a set of purpose-built computing environments that support the full research lifecycle: 

  • Exploration: Secure desktop environments for data access and prototyping 
  • Development: Dedicated compute for building and testing workflows 
  • Execution at scale: High-performance GPU clusters for large analyses and AI training 
  • Cloud expansion: Elastic infrastructure for distributed and high-throughput workloads 

Integrated environments such as IC FAC and IC AWS combine data, compute, and tools into a seamless research experience. 

👉 Explore: IC Environments

Community 

The IC is not just infrastructure—it is a community of practice that helps researchers succeed. 

Support includes: 

  • Training programs and learning resources 
  • Office hours and expert consultations 
  • Slack forums and peer collaboration 
  • Shared workflows and best practices 

This community helps researchers navigate complexity, accelerate onboarding, and build reproducible, collaborative research. 

👉 Explore: Community Support

AI as the Interface Layer 

A key evolution of the Information Commons is the emergence of AI as a unifying interface across the ecosystem. 

Through copilots, agents, and natural-language tools, researchers can: 

  • Translate research questions into data queries 
  • Navigate complex datasets and documentation 
  • Extract and structure information from clinical notes 
  • Build and execute analysis workflows 
  • Automate parts of the research pipeline 

This “AI interface layer” is transforming how researchers interact with data—collapsing the distance between question and analysis. 

From Idea to Impact 

By integrating data, tools, environments, and community—and layering AI across them—the Information Commons enables researchers to: 

  • Accelerate time from idea to analysis 
  • Work across large, complex datasets with confidence 
  • Build scalable and reproducible workflows 
  • Collaborate across teams and institutions 
  • Translate research into real-world impact 

The result is a platform that supports the full spectrum of research outcomes: 

Discoveries → Models → Publications → Clinical Insights 

Get Started

Whether you are exploring data for the first time or building advanced AI workflows, the Information Commons provides the foundation to support your work. 

Resources