SCHARE (Science Collaborative for Health Disparities and Artificial intelligence Reduction of Errors) is a cloud-based population science data platform designed to provide a centralized resource for population science data sets, and to accelerate research in health disparities, health, and health care delivery outcomes by utilizing transparent artificial intelligence (AI) approaches with a focus on the reduction of errors in the use and reuse of models.

The aim of the SCHARE program is to foster the use of transparency and other error reduction strategies to advance innovative AI in research opportunities afforded by Big Data and to facilitate the development of effective and efficient fit-to-purpose models to improve health for all.

Aims to foster innovative research:

• Leverage population science, place-based, and behavioral Big Data and cloud computing tools to foster a paradigm shift in health disparity and healthcare delivery outcomes research to generate innovative whole-person health discoveries
• Advance use of transparency and sophisticated inquiry to develop innovative strategies and differing perspectives to reduce AI errors
• Upskill novice untrained users in data science through cloud computing skills training, cross-discipline mentoring, and multi-career level collaborating on research
• Provide a data science cloud computing resource for community colleges and low resource institutions and organizations
• Offer a project data repository centered on core common data elements for enhanced data interoperability and compliance with NIH Data Management and Sharing Policy

An integral part of the program is the SCHARE Think-a-Thons webinar series, conceived to prepare novice untrained researchers in how to use the SCHARE platform and how to leverage Big Data and cloud computing for more comprehensive research designs and analytics. Participants will share knowledge and skills, and form cross-disciplinary, multi-career level collaborations around innovative research projects that can lead to scientific discoveries that will improve public health.

Mission

SCHARE’s mission is to foster innovative, effective, efficient, and transparent AI solutions that enable scientists to accelerate research using big data to improve health outcomes and reduce health disparities.

Vision

SCHARE will help foster a paradigm shift toward use of Big Data and artificial intelligence applications in population science research, such as health disparities, health care delivery, and public health outcomes. The platform will provide a centralized place for the ingestion, organization, and analysis of relevant population science datasets; and will facilitate the development and testing of innovative AI/ML/LLM models and tools that are scalable and fit-to purpose to improve health outcomes for all.

Through Think-a-Thons, researchers without training in data science will gain the knowledge and skills to use Big Data in research. Use of AI tools enables researchers to conduct faster data analysis, identify complex patterns in large datasets that might be missed by humans, automate repetitive tasks, improve accuracy through precise analysis, and allows scientist to focus on higher-level interpretation and new knowledge to impact public health outcomes.

Components

SCHARE co-localizes within the cloud:

• Datasets relevant to health disparities, health care delivery, and health outcomes research, including social determinants of health and other social science behavioral data.
• A project data repository for NIH-funded projects centered on Core Common Data Elements for enhanced data interoperability and compliance with NIH Data Management and Sharing policy.
• Secure, collaborative workspaces and for researchers and relevant collaborators.
• Computational capabilities for collaboratively evaluating, designing, and assessing fit-for-purpose utilization of datasets and algorithms to generate AI models that are effective and efficient.

Getting Started

• Register for SCHARE.
• Learn more about and access the SCHARE platform components.
• See example tutorials and other tips for getting started on the Tutorials and Resources page.

Page updated March 12, 2025