FAIR-DOscope
![FAIR-DOscope FAIR-DOscope](
https://helmholtz-metadaten.de/storage/1739/conversions/HMC-Tools_FAIRdoScope-lg.jpg 900w,
https://helmholtz-metadaten.de/storage/1739/conversions/HMC-Tools_FAIRdoScope-md.jpg 500w,
https://helmholtz-metadaten.de/storage/1739/conversions/HMC-Tools_FAIRdoScope-sm.jpg 300w,
https://helmholtz-metadaten.de/storage/1739/conversions/HMC-Tools_FAIRdoScope-xl.jpg 1400w,
)
FAIR-DOscope is an easy-to-use, generic FAIR Digital Object viewer and browser accepting PIDs of FAIR DOs and presenting the associated PID record in a graphical and user-friendly way. It offers a tabular view of the contents of a PID record and a graphical representation of related FAIR DOs.
Features
Portable - Can be used standalone in your Web browser or hosted on a Web server
Type-driven rendering of PID record elements
Search history functionality including suggestion mode for PID input
Two different ways of rendering PID records (plain or interactive)
Extensible by additional types
On-the-fly creation of FAIR DO graph
Source Code
https://github.com/kit-data-manager/fairdoscope
https://github.com/kit-data-manager/fairdoscope
![FAIR Digital Object Application Case FAIR Digital Object Application Case: Composing Machine Learning Training Data](
https://helmholtz-metadaten.de/storage/1726/conversions/HMC-Use-Cases_FDO-Application_q-lg.jpg 900w,
https://helmholtz-metadaten.de/storage/1726/conversions/HMC-Use-Cases_FDO-Application_q-md.jpg 500w,
https://helmholtz-metadaten.de/storage/1726/conversions/HMC-Use-Cases_FDO-Application_q-sm.jpg 300w,
https://helmholtz-metadaten.de/storage/1726/conversions/HMC-Use-Cases_FDO-Application_q-xl.jpg 1400w,
)
Collecting data from different storages and using it to compose a training data set for Machine Learning (ML) is a time-consuming task. Even if FAIR principles are fulfilled, scientists still need to perform several steps to obtain a ready-to-use training dataset. This application case presents a solution for supporting scientists while relabeling training datasets for ML by representing their single elements as FAIR Digital Objects (FDOs).