Making data from the center for GWAS in outbred rats FAIR and AI/ML ready

摘要(非-OD-21-094 P50DA037844行政副刊) 在这一补充应用中，我们正在寻求资金来改善数据的AI/ML准备情况 由GWAS中心在远缘繁殖的大鼠中产生。该中心自2014年成立以来，我们收集了 关于8,000多只遗传独特的异种系(HS)大鼠的大量数据，并已确保 提供资金，到2025年将这一数字增加到1.6万人。数据类型包括数以百万计的 核苷酸多态(SNPs)、复杂行为和生理表型、RNAseq、ATACSeq、 单细胞RNASeq、单细胞ATACSeq、微生物组和代谢组学数据。当中心专注于 在与药物滥用相关的特征方面，这些数据集的适用性要广泛得多。它们包括其他 与神经科学所有领域相关的行为特征和与众多器官相关的生理特征 系统和疾病。这些数据经过了精心的管理，包括大量的人工数据和自动数据 质量控制步骤，并被组织为每个唯一个人可用的数据类型。然而，在那里 没有面向公众的数据描述，也没有努力使它们为AI/ML做好准备。 在这份提案中，我们将通过组建一支具有以下专业知识的团队来改善这种情况 具体的数据集，2)信息共享的最佳实践，以及3)遗传应用的AI/ML。我们会 首先，将团队聚集在一起，找出最重要和可以解决的缺点。我们会 然后开始实现这些目标，经常开会监测进展并克服意外情况 挑战。最后，随着工作的完成，我们现有的AI/ML合作者网络将执行简单的 AI/ML练习以确认改进是成功的。这将是一个迭代的过程；这意味着 我们可以在项目过程中修改具体的行动项目，以努力最大限度地发挥影响。我们 预计这些改进将包括建立一个网站，并使我们的所有数据都可以找到。我们会 使用Protocols.io记录每个研究方案，将RRID分配给所有个人，并将最佳使用 使所有数据公平和AI/ML准备就绪的做法。这一副刊将为以下工作提供动力和资金 召集一支优秀的团队，确保NIH在这一独特数据集上的投资能够得到利用 用于尖端的AI/ML方法。此项目在父奖项的范围内，但不在 复制父授权已支持的任何工作。

Summary (NOT-OD-21-094 Administrative Supplement to P50DA037844) In this supplement application, we are seeking funds to improve the AI/ML-readiness of data generated by the Center for GWAS in outbred rats. Since the center’s formation in 2014, we have collected extensive data on more than 8,000 genetically unique heterogeneous stock (HS) rats and have secured funding to grow that number to 16,000 by 2025. Data types include genotypes at millions of single nucleotide polymorphisms (SNPs), complex behavioral and physiological phenotypes, RNASeq, ATACSeq, single cell RNASeq, single cell ATACSeq, microbiome, and metabolomic data. While the center is focused on traits relevant to substance abuse, these datasets are much more broadly applicable. They include other behavioral traits relevant to all fields of neuroscience and physiological traits relevant to numerous organ systems and diseases. These data have been carefully curated, including numerous human and automated quality control steps, and are organized as data types available for each unique individual. However, there is no public facing description of the data, and no effort has been put into making them AI/ML-ready. In this proposal, we will improve this situation by bringing together a team with expertise in 1) this specific dataset, 2) best practices for information sharing, and 3) AI/ML for genetic applications. We will begin by bringing the group together to identify the most important and addressable shortcomings. We will then begin to address these goals, meeting frequently to monitor progress and overcome unanticipated challenges. Finally, as the work is completed, our extant network of AI/ML collaborators will perform simple AI/ML exercises to confirm that the improvements are successful. This will be an iterative process; meaning that we may revise specific action items over the course of the project in an effort to maximize impact. We anticipate that improvements will include establishing a website, and making all of our data findable. We will use protocols.io to document each research protocol, will assign RRIDs to all individuals, and will use best practices to make all data FAIR and AI/ML ready. This supplement will provide the impetus and funding to bring together an outstanding team to make sure that NIH’s investment in this unique dataset can be used for cutting edge AI/ML approaches. This project is within the scope of the parent award but does not duplicate any work already supported by the parent grant.