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.