  Modern biomedical applications often involve time-series data, from
high-throughput phenotyping of model organisms, through to individual disease
diagnosis and treatment using biomedical data streams. Data and tools for
time-series analysis are developed and applied across the sciences and in
industry, but meaningful cross-disciplinary interactions are limited by the
challenge of identifying fruitful connections. Here we introduce the web
platform, CompEngine, a self-organizing, living library of time-series data
that lowers the barrier to forming meaningful interdisciplinary connections
between time series. Using a canonical feature-based representation, CompEngine
places all time series in a common space, regardless of their origin, allowing
users to upload their data and immediately explore interdisciplinary
connections to other data with similar properties, and be alerted when similar
data is uploaded in the future. In contrast to conventional databases, which
are organized by assigned metadata, CompEngine incentivizes data sharing by
automatically connecting experimental and theoretical scientists across
disciplines based on the empirical structure of their data. CompEngine's
growing library of interdisciplinary time-series data also facilitates
comprehensively characterization of algorithm performance across diverse types
of data, and can be used to empirically motivate the development of new
time-series analysis algorithms.
