Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present the core components and features of the FDP, its approach to metadata provision, the criteria to evaluate whether an application adheres to the FDP specifications and the service to register, index and allow users to search for metadata content of available FDPs
For open science to flourish, data and any related digital outputs should be discoverable and re-usa...
The FAIR Principles provide 15 high-level recommendations to make data Findable, Accessible, Interop...
One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize ...
FAIR Data Point (FDP) is a RESTful web service that enables data owners to expose their datasets and...
FAIR Data Point (FDP) is a RESTful web service that enables data owners to expose their datasets and...
While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the ...
This document is the second report from Task 2.2 of the FAIRsFAIR project. It demonstrates how the f...
As a service manager how may I assist my organisation to make research data we hold both FAIR and “a...
The FAIR principles were mostly thought with data (i.e., data at datasets) in mind. However, they ar...
Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to defin...
Since their publication in 2016, the FAIR Data Guiding Principles (Findable, Accessible, Interoperab...
The FAIR principles outline key attributes to make digital resources more Findable, Accessible, Inte...
DataONE has consistently focused on interoperability among data repositories to enable seamless acce...
This poster illustrate an approach and set of open source software tools to produce machine-actionab...
The FAIR principles refer frequently to metadata as a key enabler in discoverability, but also havin...
For open science to flourish, data and any related digital outputs should be discoverable and re-usa...
The FAIR Principles provide 15 high-level recommendations to make data Findable, Accessible, Interop...
One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize ...
FAIR Data Point (FDP) is a RESTful web service that enables data owners to expose their datasets and...
FAIR Data Point (FDP) is a RESTful web service that enables data owners to expose their datasets and...
While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the ...
This document is the second report from Task 2.2 of the FAIRsFAIR project. It demonstrates how the f...
As a service manager how may I assist my organisation to make research data we hold both FAIR and “a...
The FAIR principles were mostly thought with data (i.e., data at datasets) in mind. However, they ar...
Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to defin...
Since their publication in 2016, the FAIR Data Guiding Principles (Findable, Accessible, Interoperab...
The FAIR principles outline key attributes to make digital resources more Findable, Accessible, Inte...
DataONE has consistently focused on interoperability among data repositories to enable seamless acce...
This poster illustrate an approach and set of open source software tools to produce machine-actionab...
The FAIR principles refer frequently to metadata as a key enabler in discoverability, but also havin...
For open science to flourish, data and any related digital outputs should be discoverable and re-usa...
The FAIR Principles provide 15 high-level recommendations to make data Findable, Accessible, Interop...
One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize ...