التفاصيل البيبلوغرافية
العنوان: |
From implementation to application: FAIR digital objects for training data composition. |
المؤلفون: |
Blumenröhr, Nicolas, Aversa, Rossella |
المصدر: |
Research Ideas & Outcome Journal; 8/22/2023, p1-9, 9p |
مصطلحات موضوعية: |
MACHINE learning, SCANNING electron microscopy, METADATA, AUTOMATION, PYTHON programming language |
مستخلص: |
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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قاعدة البيانات: |
Complementary Index |