# Development and Use of an AI/ML-Ready Dog Aging Project Dataset

> **NIH NIH U19** · UNIVERSITY OF WASHINGTON · 2021 · $310,571

## Abstract

Abstract
The Dog Aging Project (DAP) is a nationwide Community Science study on the genetic and environmental
determinants of healthy aging in companion dogs. This long-term study has already enrolled over 30,000
participants nationwide, with the goal of collecting a rich dataset about each canine participant throughout its
life. Each participant provides data from owner-reported surveys on health, life experience, cognitive function,
and home environment, and when available, veterinary electronic medical records. Local environmental data
for each dog include air quality, water quality, weather data, walkability scores, and more. The DAP will collect
whole genome sequencing data for 10,000 dogs, and for more than 1000 of those dogs, annual measures of
extensive systems biological data (actigraphy, clinicopathology measures, metabolome, microbiome,
epigenome, and flow cytometry). The DAP is an Open Science study--all data will be made available to
researchers around the world, with the goal of maximizing the impact of scientific discoveries that arise from
these data. Thus, the DAP dataset offers a tremendous opportunity for those interested in applying AI/ML
approaches to interesting, important datasets. The goals of this proposal are twofold. First, it will fund a data
scientist with experience in working with large datasets to ensure that the DAP data are maximally compliant
with the needs of AI/ML analytical approaches. The work funded by this Supplement will ensure that workflows
are in place for data and metadata construction, for pre-processing, cleaning and filtering data, for imputing
missing data and metadata, and maintaining data documentation. Second, it is critical that AI/ML-ready data,
and AI/ML approaches to analyze the data, are available for the DAP research team and for the broader
community. With that in mind, the DAP team is collaborating with the University of Washington eScience
Institute, one of the nation's first data science institutes, whose express purpose is to provide training and
resources for researchers to work with large, complex and noisy data sets, and with extensive expertise in
AI/ML approaches. The DAP team will work closely with the eScience Institute to design and present a series
of webinars throughout the year, introducing the DAP researchers, who already have extensive statistical
experience in their own fields, to the power of AI/ML approaches and how to implement them on DAP data.
Towards the end of the funding period of this Supplement, the eScience Institute will run a DAP AI/ML hack
week, where outside researchers interested in AI/ML approaches will join the DAP team. The work carried out
here will lay the critical groundwork to implement tools that facilitate powerful AI/ML analyses of DAP data by
researchers around the world.

## Key facts

- **NIH application ID:** 10409023
- **Project number:** 3U19AG057377-04S3
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Daniel Edward Promislow
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $310,571
- **Award type:** 3
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10409023

## Citation

> US National Institutes of Health, RePORTER application 10409023, Development and Use of an AI/ML-Ready Dog Aging Project Dataset (3U19AG057377-04S3). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10409023. Licensed CC0.

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