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

摘要

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.