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Latitudes October 18th, 2018
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New Joint NSF/NLM Funding Announcement: Generalizable Data Science Methods for Biomedical Research

Posted by on October 11th, 2018 Posted in: Announcements, Data, Electronic Health Records, Funding, Technology


Significant advances in technology, coupled with decreasing costs associated with data collection and storage, have resulted in unprecedented access to vast amounts of health- and disease-related data. The National Library of Medicine and the Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences (DMS) at the National Science Foundation (NSF) recognize the need to support research to develop innovative and transformative mathematical and statistical approaches to address important data-driven biomedical and health challenges. The goal of this interagency program is the development of generalizable frameworks combining first principles, science-driven models of structural, spatial and temporal behaviors with innovative analytic, mathematical, computational, and statistical approaches that can portray a fuller, more nuanced picture of a person’s health or the underlying processes.

Specific information concerning application submission and review process is through the National Science Foundation via solicitation NSF-19-500. Applicants may opt to submit proposals via Grants.gov or via the NSF FastLane system. For applications that are being considered for potential funding by NLM, the PDs/PIs will be required to submit their applications in an NIH-approved format. Anyone invited to submit to NIH will receive further information on submission procedures. Applicants will not be allowed to increase the proposed total budget or change the scientific content of the application in the submission to the NIH. The results of the first level scientific review will be presented to NLM Board of Regents for the second level of review. NLM will make final funding determinations and issue Notices of Awards to successful applicants. NLM and DMS anticipate making 8 to 10 awards, totaling up to $4 million, in fiscal year 2019. It is expected that each award will be between $200,000 to $300,000 (total costs) per year with durations of up to three years. The application submission window deadline is in early January, 2019.

Collaborative efforts that bring together researchers from the biomedical/health and the mathematical/statistical sciences communities are a requirement for this program and must be convincingly demonstrated in the proposal. While the research may be motivated by a specific application or dataset, the development of methods that are generalizable and broadly applicable is preferred and encouraged. Proposals should clearly discuss how the intended new collaborations will address a biomedical challenge and describe the use of publicly-available biomedical datasets to validate the proposed models and methodology. Applicants are expected to list specific datasets that will be used in the proposed research and demonstrate that they have access to these datasets. The Data Management Plan should describe plans to make the data available to researchers if these data are not in the public domain. Some of the important application areas currently supported by the National Library of Medicine include the following:

  • Finding biomarkers that support effective treatment through the integration of genetic and Electronic Health Records (EHR) data;
  • Understanding epigenetic effects on human health;
  • Extracting and analyzing information from EHR data;
  • Understanding the interactions of genotype and phenotype in humans by linking human sensor data with genomic data using dbGaP;
  • Protecting confidentiality of personal health information; and
  • Mining of heterogeneous data sets (e.g. clinical and environmental).

Inquiries should be directed to Jane Ye, PhD at the National Library of Medicine, (301) 594-4882.

Image of the author ABOUT Alan Carr
Alan Carr is the Associate Director, National Network of Libraries of Medicine, Pacific Southwest Region, based at UCLA.

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This project is funded by the National Library of Medicine, National Institutes of Health, Department of Health and Human Services, under Cooperative Agreement Number UG4LM012341 with the UCLA Louise M. Darling Biomedical Library.

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