Notice of Special Interest (NOSI): Computational and Statistical Methods to Enhance Discovery from Health Data
Notice Number:

Key Dates

Release Date:

January 20, 2023

First Available Due Date:
February 05, 2023
Expiration Date:
January 08, 2026

Related Announcements

PAR-23-034 - NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional)

PA-20-184 - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)

PA-20-185 - Research Project Grant (Parent R01 Clinical Trial Not Allowed)

Issued by

National Library of Medicine (NLM)


The National Library of Medicine is issuing this Notice to highlight its interest in receiving grant applications focused on research that aims to reduce or mitigate gaps and errors in health data sets.


Recent successes with the use of data-centric artificial intelligence (AI) methods such as deep learning (DL) are stimulating interest in harnessing large and complex digital health-related data sets to advance the goals of biomedical informatics research. Applying AI methods to large-health related data sets are becoming increasingly important for discovering, diagnosing and predicting, with computational tools, the improvement of health-related outcomes and the reduction of healthcare costs. Many human and non-human public datasets are becoming available that encourage the development of specialized tools and platforms that can be used in research. However, recent work in identifying and addressing data problems, such as systematic biases, missing data, data set imbalance and blind spots in data, have highlighted an array of potential problems with fairness, accuracy, safety, and reproducibility of inferences and conclusions, leading to bias in the AI tools that are derived from the data.

New technologies are emerging to address clinical care and biobehavioral practices, such as telemedicine and using wearable devices for patient data collection. Advances in biological research technology, such as sequencing and omics data generation, have generated new challenges in data analysis and integration, which are hinged on data completeness, quality, and availability. The availability of COVID-19 data from multiple initiatives, institutions and cohorts, and their access via cloud storage, present new challenges and opportunities for long-COVID research. Research using health data from humans requires special care to protect the data sources and privacy, as well as considerations to support minority and under-served populations to ensure health care equity. These AI ethical issues are important and need to be addressed beyond problems with data bias and other gaps.

There is an increasing need to address challenges in the use of large data sets for biomedical research using statistical algorithms and computational tools, generalizable to multiple applications. Tools developed using biased and incomplete data sets may contribute to erroneous analyses resulting in population marginalization and health inequities. Statistical fallacies and representational errors unrelated to the research question can introduce systematic errors. Some of the core questions for understanding and mitigating these and other problems in health data research include: “What are the new challenges and areas of gaps preventing the safe use of biomedical data for accurate data analysis?”, "What can be done, computationally and/or statistically, to reduce or mitigate gaps and errors in data sets used for health research?’, and "How can we improve tools used for discovery, understanding, and visualization in health data sets and their analyses?". The problem genesis, such as incomplete health data or inadequate tools, requires the ability to develop generalizable methodologies and approaches to strengthen the reproducibility and applicability of data-centered research in all areas of NLM’s interests.

Research Objectives

NLM invites research grant applications that propose innovative state-of-the-art methods and generalizable approaches to address problems with large health data sets or analytic tools, whether the data are obtained from electronic health records, public health data sets, biomedical imaging, omics repositories, literature, and social media data, or other biomedical or social/behavioral data sets. Applications in response to this NOSI are expected to help address AI ethical issues and mitigate algorithm bias caused by data problems.

Areas of interest include but are not limited to (1) developing and testing innovative, generalizable and scalable computational or statistical approaches, including ones for a cloud computing environment, applied to large and/or merged health data sets holding human or non-human data, with a focus on understanding and characterizing the gaps, errors, biases, and other limitations in the data or inferences based on the data; (2) exploring approaches to correct biases or compensate for missing data, including but not limited to the introduction of debiasing techniques, novel imputing methods, effective data sample strategies, and policies or the use of synthetic data; (3) testing new statistical algorithms or other computational approaches to strengthen research designs for use with specific types of biomedical, biological, and social/behavioral data; (4) generating metadata that adequately characterizes the data, including its provenance, intended use, and processes by which it was collected and verified; (5) improving approaches for integrating, mining, and analyzing health data from multiple sites, sources, cohorts and research domains that preserve the confidentiality, accuracy, completeness and overall security of the data. Applicants should address ethical issues that might arise from their proposed approach.

Application and Submission Information

This notice applies to due dates on or after February 5, 2023 and subsequent receipt dates through January 8, 2026 

Submit applications for this initiative using one of the following funding opportunity announcements (FOAs) or any reissues of these announcements through the expiration date of this notice.

PAR-23-034 - NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional)

PA-20-184 - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)

PA-20-185 - Research Project Grant (Parent R01 Clinical Trial Not Allowed)

All instructions in the SF424 (R&R) Application Guide and the funding opportunity announcement used for submission must be followed, with the following additions:

  • For funding consideration, applicants must include “NOT-LM-23-001”  (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.


Please direct all inquiries to the Scientific/Research, Peer Review, and Financial/Grants Management contacts in Section VII of the listed funding opportunity announcements.