In October 2018, NSF implemented the Domain-based Message Authentication, Reporting & Conformance (DMARC) email changes required by the Department of Homeland Security (DHS) to improve email security. Some email routing practices (such as auto-forwarding to personal email accounts and sending messages through third-party providers) may cause messages to be flagged as potentially fraudulent by DMARC security checks and blocked. If your email is auto-forwarded to another account, such as a personal email account, you may not receive emails from NSF in that forwarded account. More information about DMARC and email delivery from NSF.
Division of Mathematical Sciences
Joint DMS/NLM Initiative on Generalizable Data Science Methods for Biomedical Research (DMS/NLM)
|Nandini Kannanemail@example.com||(703) 292-8104|
|James Powellfirstname.lastname@example.org||(703) 292-8714|
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 19-1), is effective for proposals submitted, or due, on or after February 25, 2019. Please be advised that, depending on the specified due date, the guidelines contained in NSF 19-1 may apply to proposals submitted in response to this funding opportunity.
The Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) at the National Science Foundation (NSF) and the National Library of Medicine (NLM) at the National Institutes of Health (NIH) plan to support the development of innovative and transformative mathematical and statistical approaches to address important data-driven biomedical and health challenges. The rationale for this interagency collaboration is that significant advances may be expected as the result of continued NSF investments in foundational research in mathematics and statistics as well as inter- and multi-disciplinary research and training at the intersection of the quantitative/computational sciences and domain sciences, while NIH benefits from the enhancement of biomedical data science with new approaches that strengthen the reproducibility of biomedical research and support open science.