Treffer: From implementation to application: FAIR digital objects for training data composition.

Composing training data for Machine Learning applications can be laborious and timeconsuming when done manually. The use of FAIR Digital Objects, in which the data is machine-interpretable and -actionable, makes it possible to automate and simplify this task. As an application case, we represented labeled Scanning Electron Microscopy images from different sources as FAIR Digital Objects to compose a training data set. In addition to some existing services included in our implementation (the Typed-PID Maker, the Handle Registry, and the ePIC Data Type Registry), we developed a Python client to automate the relabeling task. Our work provides a Proof-of-Concept validation for the usefulness of FAIR Digital Objects on a specific task, facilitating further developments and future extensions to other machine learning applications. [ABSTRACT FROM AUTHOR]

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