Background

Investigators recognize AI as a powerful tool for enabling deeper insights and improving research efficiency, yet its adoption remains challenging. The AI assurance exploratory pilots, conducted through real‑world NIH use cases, underscored how resource‑intensive development, rapidly evolving technology, and inconsistent alignment with ethical, technical, and assurance standards hinder scalable deployment. In the absence of clear, standardized methods, playbooks, and best practices, investigators often rely on custom, in‑house tools and fragmented workflows, spending significant time on costly, dense, duplicative, or poorly tailored resources that slow progress and limit the adoption, safety, and impact of AI in biomedical and health research.

By creating a shared foundation of AI assurance resources, an NIH collaborative AI Assurance Lab would enable researchers to focus on developing novel applications.

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Insights from Pilots

The pilots focused on evaluating barriers and gathering insights from the biomedical and health research community to accelerate responsible AI development and adoption – priorities that are underscored in the NIH Strategic Plan for Data Science (2025) and America's AI Action Plan (2025). The pilots uncovered gaps in the AI assurance resources available to the NIH research community (e.g., curated playbooks, formalized benchmarks, testing and evaluation methods, and standardized tools) that are slowing advancements in the field.

Challenges for the biomedical and health research community include:

• Absence of clear, standardized guidance and best practices for AI development and deployment.
• Inconsistent processes for aligning AI workflows with established ethical, technical, and assurance standards.
• Reliance on inefficient, custom-built tools and processes due to a lack of standardized and accessible AI resources.
• Unscalable and resource-intensive efforts required to develop and maintain AI systems throughout the research lifecycle.

To address these challenges, NIH in partnership with MITRE recommends establishing a collaborative AI Assurance Lab as a trusted resource for the biomedical and health research community. An NIH AI Assurance Lab would leverage collaborative research engagements using real‑world AI use cases to generate tailored lessons learned, curated playbooks, benchmarks, testing and evaluation methods, and other assurance resources that directly support responsible AI‑enabled research. Operating through an iterative framework centered on collaboration, continuous improvement, and validation with real‑world evidence, the Lab would identify emerging assurance gaps, develop solutions, and adapt them to practical biomedical and health research settings (see graphic below). By fostering interdisciplinary partnerships, streamlining AI workflows, and setting new benchmarks for ethical and effective AI, the Lab would accelerate AI adoption across NIH initiatives, ultimately advancing scientific discovery, enabling precision medicine, and improving public health for the benefit of both the scientific community and society at large.

The Collaborative Research for the Advancement of Artificial Intelligence Lab (CORAL)

Partnership

To assess the current state of AI assurance in research and identify solutions to key challenges, NIH partnered with MITRE, operator of the Health Federally Funded Research and Development Center (Health FFRDC). Results from the initial pilot period of this effort, including landscape analysis of existing AI assurance resources and initiatives, overview of real-world pilots, and insights for practical solutions, were gathered into a report. 

Laura Heath, Ph.D. will present "Enhancing Team Science through FAIR Data Principles in the AD Knowledge Portal" from 12:00 p.m.–1:00 p.m. EDT.

About the Seminar

Over the last decade, the ADKP-DCC, in partnership with the NIA, has emerged as a cornerstone in the advancement of AD research. The Portal provides storage for more than a petabyte of multi-omic research data produced by NIA-funded research programs, providing FAIR (Findable, Accessible, Interoperable, and Reusable) access to large genomic and transcriptomic datasets. The centralization of these resources is further leveraged by ADKP DCC-facilitated scientific collaborations within and between ADKP-affiliated consortia (e.g. multi-consortia working groups, data challenges, and technical workshops). As a result, the ADKP has become an essential hub for a rapidly growing, NIA-sponsored AD research data ecosystem. In this talk, Dr. Heath will discuss recent efforts to uphold FAIR data practices in the ADKP through data harmonization, streamlined curation, interoperability, and community engagement.

About the Speaker

Laura Heath, Ph.D., manages scientific coordination and collaborative efforts for the AD Knowledge Portal Data Coordination Center (ADKP-DCC). She received her interdisciplinary PhD training in genetic epidemiology and ethical, legal, and social issues in genomic research at the University of Washington Institute for Public Health Genetics, and she completed a postdoctoral fellowship at the Institute for Systems Biology. Dr. Heath joined Sage Bionetworks as a research scientist in 2020 before taking on management of the ADKP-DCC. She works closely with NIA partners and AD investigators from dozens of institutions and NIA-sponsored programs to enable easier data sharing, more data reuse, and more engaged collaborations through team science initiatives.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Allison Hurst at 301-670-4990. Requests should be made at least five days in advance of the event. 

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight examples of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

David N. Kennedy, Ph.D., will present "From Observation to Knowledge: Harnessing Reproducible Practices to Accelerate Science" from 12:00 p.m.–1:00 p.m. EDT.

About the Seminar

Reproducible research practices are essential for transforming raw scientific observations into robust, actionable knowledge. This talk explores how advances in infrastructure, developed by ReproNim and the community, empower researchers to better share, reuse, and build upon scientific data.

I will highlight our interactions with collaborative platforms like OpenNeuro, to make versioned and re-executable computation accessible at scale. In addition, the ENIGMA Parkinson’s Disease project will serve as an example highlighting harmonization and query across dozens of research sites, and how public and private metadata stores (so-called ReproLakes and ReproPonds) can be integrated to support FAIR (Findable, Accessible, Interoperable, Reusable) and reproducible analyses.

Attendees will gain insights into the technical and cultural challenges of reproducible science, get practical examples of solutions in action, with the hope that this can support advancing data sharing and reuse within their own research programs. Together, we can accelerate the transition from scientific observation to reliable knowledge by harnessing the power of reproducible practices.

About the Speaker

Dr. Kennedy is a Professor of Psychiatry and Radiology at the University of Massachusetts Chan Medical School. He is Director of the Division of Neuroinformatics at the Child and Adolescent Neurodevelopment Initiative (CANDI). He has extensive expertise in the development of image analysis techniques and was a co-founder of the Center for Morphometric Analysis (CMA) at the Massachusetts General Hospital in 1988. His career has seen participation in the advent of such technologies as MRI-based morphometric analysis (1989), functional MRI (1991) and diffusion tensor pathway analysis (1998). He has long standing experience with the development of neuroinformatics resources (such as the NeuroImaging Tools and Resources Collaboratory (NITRC)) and reproducibility initiatives (such as ReproNim: A Center for Reproducible Neuroimaging Computation). In addition, he was a founding editor of the journal Neuroinformatics that debuted in 2003.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Allison Hurst at 301-670-4990. Requests should be made at least five days in advance of the event. 

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight examples of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

Welcome to the March 2026 Director’s Corner! We’re back for the new year to share updates from the NIH Office of Data Science Strategy (ODSS). This month, we’re spotlighting the ODSS collaborations with the NIH Institutes, Centers, and Offices in fiscal year 2025.  We capture these collaborations in PDF documents sent to ICO directors. The PDF documents serve as a great way to disseminate information about the impact of ODSS collaborations, with detailed graphics and funding highlights. 

To compile the PDF documents, the ODSS gathers relevant information such as funding trends, strategic goal trends, funding highlights, collaboration outputs, and funding outputs across ODSS’s NIH portfolio of partners to ensure accuracy and completeness of the data. We gather this information by reviewing co-funded projects and goals from the past fiscal year.

Each highlight opens with a summary of ODSS funding for the IC in the previous fiscal year. The opening notes funding trends from previous years and ties funding into the larger set of NIH Data Science strategic goals. For example, a PDF document might note that an ICO and ODSS have consistently collaborated to support workforce development or develop analytic tools and data infrastructure. This section also includes graphs and charts to show funding trends, the number of funded projects by goal area, and funding distributions. These are great opportunities to highlight how ICO/ODSS collaborations tie into NIH strategic goals. 

Next, the PDF documents show co-funding highlights between the ICO and ODSS from the previous year. Here, we note specific projects funded by ODSS and completed by the ICO. These might be technologies, training programs, data infrastructures, or many other biomedical and research initiatives. Each project is also tied to a specific goal area within the NIH Data Science strategic goals. Finally, a graphic shows collaboration outputs, noting the number of publications, patents, clinical trials, RAS-supported data resources, ChIRP users, Coursera users, and STRIDES accounts from the previous fiscal year. 

ODSS is grateful for the partnerships and collaboration with the NIH Institutes, Centers, and Offices that make these highlights possible and illustrate our combined impact in data science, reminding me of a famous African proverb, "If you want to go fast, go alone. If you want to go far, go together."

Check out the highlights at https://datascience.nih.gov/nih-ic-end-of-year-letters.

Stay tuned for more updates from ODSS, and let’s continue working together to transform data into discovery.

Derek Caetano-Anollés, Ph.D., will present "Sequence Read Archive: Leveraging this petabyte-scale database to drive biomedical discovery" from 12:00 p.m.–1:00 p.m. EST.

About the Seminar

The Sequence Read Archive (SRA) is the largest publicly available repository of high-throughput sequencing data. With big data come big challenges, and that includes keeping the SRA sustainable while making sure that data is findable, accessible, interoperable and reusable. Following a brief introduction to the SRA and the expanse of data it holds, we will share best practices for accessing SRA data for your analyses and the various formats you may encounter. Finally, we will describe the SRA Lite file format, which is faster to download with the added advantage of shrinking the overall footprint of SRA. We will demonstrate the use of SRA Lite format in NCBI RNA-seq pipelines and related analyses, and offer appropriate NCBI resources to learn more and engage with us.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Allison Hurst at 301-670-4990. Requests should be made at least five days in advance of the event. 

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight examples of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.