From implementation to application: FAIR digital objects for training data composition
Python (programming language)
Data Reuse
Data Sharing
Proof of concept
Version:
-
DOI:
10.3897/rio.9.e108706
Publication Date:
2023-08-22
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AUTHORS (2)
ABSTRACT
Composing training data for Machine Learning applications can be laborious and time-consuming when done manually. The use of FAIR Digital Objects, in which the is machine-interpretable -actionable, makes it possible to automate simplify this task. As an application case, we represented labeled Scanning Electron Microscopy images from different sources as Objects compose a set. In addition some existing services included our implementation (the Typed-PID Maker, Handle Registry, ePIC Data Type Registry), developed Python client relabeling Our work provides Proof-of-Concept validation usefulness on specific task, facilitating further developments future extensions other machine learning applications.
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