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NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional)

Sector: Education • Location: United States of America

Source: Grants.gov

Project
Archived

The National Library of Medicine (NLM) supports innovative research and development in biomedical informatics and data science. The scope of NLM's interest in these research domains is broad, with emphasis on new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences as well as domain-independent, reusable approaches to discovery, curation, analysis,

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The project “NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional)” is an infrastructure initiative in the Education sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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archived

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Description

Description

The National Library of Medicine (NLM) supports innovative research and development in biomedical informatics and data science. The scope of NLM's interest in these research domains is broad, with emphasis on new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences as well as domain-independent, reusable approaches to discovery, curation, analysis, organization and management of health-related digital objects. Biomedical informatics and data science draw upon many fields, including mathematics, statistics, information science, computer science and engineering, and social/behavioral sciences. Application domains include health care delivery, basic biomedical research, clinical and translational research, precision medicine, public health, biosurveillance, health information management in disasters, and similar areas. NLM defines biomedical informatics as the science of optimal representation, organization, management, integration and presentation of information relevant to human health and biology. NIH defines data science as the interdisciplinary field of inquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and/or complex sets of data.

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Source reliability

High

Data quality score

100%

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