Health Care Seed Funding

In 2014-2015, the Seed Funding Opportunity invited CITRIS Principal Investigators at CITRIS campuses (UC Berkeley, UC Davis, UC Merced and UC Santa Cruz) to apply for a one-year seed grant that will support CITRIS research initiatives, enhance cooperation among campuses, and facilitate early-stage research that can lead to external funding.
To leverage the broad capabilities of CITRIS researchers, the 2015 Seed Funding Program offered a supplementary call supporting multidisciplinary, multi-campus teams focused on innovation in Big Data for Health Science solutions.
Biomedical research is becoming increasingly data-intensive as researchers are generating and using increasingly large, complex, and diverse data sets. In parallel, the U.S. health system has a critical need to adopt data analytic solutions to advance the practice of care. Federal funding agencies such as NIH-NCATS and PCORI are investing in national efforts at the intersection of these needs.
Investigators were encouraged to submit proposals that address integrative issues of health data analytics using transformative solutions and methods. A supplementary pool of funds was available in addition to the core CITRIS Seed Funding Program to support this line of research.
All CITRIS Seed Funding Program guidelines apply. Access the full program overview and application materials here.
CITRIS Seed Funding Overview and Application

Areas of Interest in Big Data for Health Science

Proposals are encouraged in, but not limited to:
Developing new methods for analyzing biomedical Big Data

  • Projects to use cloud computing and application programming interfaces (APIs) to solve specific biomedical Big Data challenges.
  • Projects to analyze non-traditional health datasets—such as the increasingly large datasets generated from social media, search engines, mobile devices, and environmental sensors—to better understand behavioral, social, and environmental contributors to health and disease. Projects may require new techniques for scientific validation due to the novel data types.

Organizing, managing, and processing biomedical Big Data

  • Projects to integrate and analyze longitudinal, multimodal, clinical, and sensor data; and mine clinical information from electronic health records to translate into knowledge for diagnoses and clinical decision-making.
  • Projects to integrate biological, behavioral, environmental, and social data for individuals over time to identify thresholds of exposure and the relevance of critical periods of development and risk or protective factors (e.g., nutritional factors, vaccinations, medications, exercise behavior, social support).

Extending policies and practices for data and software sharing

  • Projects to explore the coordination of research data requirements with improved tools for phenomic data capture including subjective and objective data contributed by clinicians in healthcare settings and patients/research subjects in non-traditional settings.