Circadian rhythms are fundamental for understanding biology: they date to the origin of life, are found in virtually every species from cyanobacteria to mammals, and coordinate many important biological functions from the sleep-wake cycle, to metabolism, to cognitive functions. Circadian rhythms are equally fundamental for health and medicine: diet modifications have been linked to molecular-level changes in circadian rhythms; disruptions of circadian rhythms have been linked to health problems ranging from depression to learning disorders to diabetes, to obesity, to cardiovascular disease, to cancer, and to premature aging; finally, a large fraction of drug targets have been found to oscillate in a circadian manner in one or several tissues. A better understanding of circadian oscillations at the molecular level has many direct applications to precision health and medicine. To illuminate circadian oscillations at the molecular level, modern high-throughput technologies are being used to measure the concentrations of many molecular species, including transcripts, proteins, and metabolites along the circadian cycle in different organs and tissues, and under different conditions. Yet informatics tools for processing, analyzing, and integrating the growing wealth of molecular circadian data are not yet in place. This effort will fill this fundamental gap by continuing to develop and disseminate informatics tools to enable the collection, integration, and analyses of this wealth of information and lead to novel and fundamental insights about circadian oscillations' organization and regulation, roles in health and disease, and future applications to precision medicine. Specifically, via close collaborations among computational and experimental scientists, this effort will have four main aims: (1) Data: Aggregate the largest possible collection of circadian omic (e.g., transcriptomic, metabolomic, proteomic) experimental datasets covering as many species, cells, tissues, organs, and conditions (e.g., genetic, epigenetic, environmental) as possible. (2) Analysis: Develop analytical tools, including deep learning tools, to analyze these datasets to identify molecular species with a periodic concentration profile with statistical determination and conduct integrated differential analyses across the different datasets. (3) Web Platform: Import the analyses' datasets and results into an integrated database and serve them publicly through a web server (CircadiOmics platform) as a one-stop shop for viewing or downloading circadian data, annotations, tools, and analyses, enabling other scientists to view and analyze circadian data comparatively. And (4) Applications: Apply the CircadiOmics platform's datasets and tools to specific biomedical problems via multiple efforts in collaboration with other experimental labs to identify the role of circadian oscillations in health and disease and generate mechanistic molecular hypotheses that can then be tested in th...