Making data interoperable

Christian Lovis

A critical and sometimes challenging task in data science is to process heterogeneous data. This heterogeneity can be due to different formats, for example in image formats; different acquisition devices, a frequent situation in radiology; different units; etc.  Making data interoperable means identifying all the elements that can improve the ability to analyze data from different sources, concerning very different measurements, in different contexts, in particular.