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Modern biomedical applications often involve time-series data, fromhigh-throughput phenotyping of model organisms, through to individual diseasediagnosis and treatment using biomedical data streams. Data and tools fortime-series analysis are developed and applied across the sciences and inindustry, but meaningful cross-disciplinary interactions are limited by thechallenge of identifying fruitful connections. Here we introduce the webplatform, CompEngine, a self-organizing, living library of time-series datathat lowers the barrier to forming meaningful interdisciplinary connectionsbetween time series. Using a canonical feature-based representation, CompEngineplaces all time series in a common space, regardless of their origin, allowingusers to upload their data and immediately explore interdisciplinaryconnections to other data with similar properties, and be alerted when similardata is uploaded in the future. In contrast to conventional databases, whichare organized by assigned metadata, CompEngine incentivizes data sharing byautomatically connecting experimental and theoretical scientists acrossdisciplines based on the empirical structure of their data. CompEngine'sgrowing library of interdisciplinary time-series data also facilitatescomprehensively characterization of algorithm performance across diverse typesof data, and can be used to empirically motivate the development of newtime-series analysis algorithms.

CompEngine: a self-organizing, living library of time-series data