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

摘要 狗衰老项目（DAP）是一项全国性的社区科学研究，研究遗传和环境因素对狗衰老的影响。 伴侣犬健康衰老的决定因素。这项长期研究已经招募了30，000多名 参与者在全国范围内，与收集丰富的数据集的目标，每个犬参与者在其整个 生活每个参与者提供的数据来自业主报告的健康，生活经验，认知功能， 和家庭环境，以及兽医电子医疗记录（如果可用）。当地环境数据 包括空气质量、水质、天气数据、步行能力评分等。DAP将收集 10,000只狗的全基因组测序数据，以及其中1000多只狗的年度测量数据， 广泛的系统生物学数据（活动记录，临床病理学测量，代谢组，微生物组， 表观基因组和流式细胞术）。DAP是一项开放科学研究-所有数据都将提供给 世界各地的研究人员，其目标是最大限度地发挥科学发现的影响， 这些数据。因此，DAP数据集为那些有兴趣应用AI/ML的人提供了巨大的机会 有趣的、重要的数据集。这项建议有两个目标。首先，它将资助一个数据 具有处理大型数据集经验的科学家，以确保DAP数据最大限度地符合要求 AI/ML分析方法的需求。本补编资助的工作将确保工作流程 建立数据和元数据结构，对数据进行预处理、清理和过滤， 缺失数据和元数据，以及维护数据文档。其次，AI/ML就绪数据至关重要， 和AI/ML方法来分析数据，可供DAP研究团队和更广泛的领域使用 社区考虑到这一点，DAP团队正在与华盛顿大学电子科学学院合作 研究所，全国首批数据科学研究所之一，其明确的目的是提供培训和 资源，研究人员与大型，复杂和嘈杂的数据集，并与广泛的专业知识， AI/ML正在接近。DAP团队将与eScience Institute密切合作，设计并展示一系列 全年的网络研讨会，介绍DAP研究人员，他们已经有了广泛的统计数据， 在自己的领域，AI/ML方法的力量以及如何在DAP数据上实现它们的经验。 在本补充材料的资助期结束时，eScience Institute将运行DAP AI/ML hack 本周，对AI/ML方法感兴趣的外部研究人员将加入DAP团队。开展的工作 这里将为实现工具奠定关键基础，这些工具通过以下方式促进DAP数据的强大AI/ML分析： 世界各地的研究人员。

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.