21st Century Science Initiative

Funding Opportunities

In 2000 the James S. McDonnell Foundation (JSMF) announced new grantmaking guidelines for a new funding initiative: the 21st Century Science Initiative.

JSMF believes that private philanthropic support for science is most effective when it invests in the acquisition of new knowledge and in the responsible application of knowledge for solving the real world problems. Applicants are encouraged to keep this in mind when preparing proposals. Projects supported through the 21st Century Science Initiative are expected to meet highly selective intellectual standards.

Interested in applying for a grant? JSMF supports research and scholarship via review of proposals submitted in response to to foundation-initiated programs and calls for proposals. JSMF currently provides funding for grants in three program areas. Compare JSMF grant types and the program areas funded in each. Unsolicited requests that do not fit into a program area and are not research related are not funded.

Studying Complex Systems

This program supports scholarship and research directed toward the development of theoretical and mathematical tools that can be applied to the study of complex, adaptive, nonlinear systems. It is anticipated that research funded in this program will address issues in fields such as biology, biodiversity, climate, demography, epidemiology, technological change, economic development, governance, or computation. 

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Understanding Human Cognition

This program supports research studying how neural systems are linked to and support cognitive functions and how cognitive systems are related to an organism’s (preferably human) observable behavior.

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Mathematical & Complex Systems Approaches for Brain Cancer

Despite some recent cause for optimism for advancing the clinical treatment of brain cancers, for many patients brain tumor remains a devastating diagnosis. Progress against this disease has been stymied by limited understandings of the molecular, metabolic, and physiological characteristics of human brain cancers across multiple temporal and spatial scales and by the failure of many preclinical models to predict patient responses.

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