This poster describes the development of a testbed for FAIR Digital Objects, consisting of an ecosystem of interacting services to demonstrate mandatory and optional FAIR use cases and to identify gaps in the specifications. Preprocessing data for research, like finding, accessing, unifying or converting, takes up to 80% of research time spans. The FAIR (Findability, Accessibility, Interoperability, Reproducibility) principles aim to support and facilitate the reuse of data, and are therefore tackling this problem. A FAIR Digital Object (FAIR DO) is one way to capsule research data resources of all kinds (raw data, metadata, software, ...) so they are following the FAIR principles. A FAIR DO ecosystem can be regarded as a set of servi...
Introduction: Assigning a PID to a whole dataset, as common practice within research data management...
A Digital Object (DO) "is a sequence of bits, incorporating a work or portion of a work or other inf...
Data science is facing the following major challenges: (1) developing scalable cross-disciplinary ca...
Preprocessing data for research, like finding, accessing, unifying or converting, takes up to large ...
The FAIR Digital Object Lab is an extendable and adjustable software stack for generic FAIR Digital ...
The Helmholtz Association (Anonymous 2022d), the largest association of large-scale research centres...
The last few years have seen considerable progress in terms of integrating individual elements of th...
The objective of the FAIR Digital Objects Framework (FDOF) is for objects published in a digital env...
As the FAIR Principles about findability, accessiblity, interoperability and reusability of research...
Data preparation and cleansing tasks take valueable time away from data scientists. The FAIR Digital...
This talk discusses the use of Fair Digital Objects (FDOs for short) for a democratised approach to ...
This document is the first iteration of three annual reports on the state of FAIR in European scient...
The scientific community's efforts have increased regarding the application and assessment of the FA...
In this poster we present the FAIR-IMPACT project, "Expanding FAIR solutions across EOSC", which is...
There is broad acceptance that FAIR data (Wilkinson et al. 2016) reuse is desirable, with considerab...
Introduction: Assigning a PID to a whole dataset, as common practice within research data management...
A Digital Object (DO) "is a sequence of bits, incorporating a work or portion of a work or other inf...
Data science is facing the following major challenges: (1) developing scalable cross-disciplinary ca...
Preprocessing data for research, like finding, accessing, unifying or converting, takes up to large ...
The FAIR Digital Object Lab is an extendable and adjustable software stack for generic FAIR Digital ...
The Helmholtz Association (Anonymous 2022d), the largest association of large-scale research centres...
The last few years have seen considerable progress in terms of integrating individual elements of th...
The objective of the FAIR Digital Objects Framework (FDOF) is for objects published in a digital env...
As the FAIR Principles about findability, accessiblity, interoperability and reusability of research...
Data preparation and cleansing tasks take valueable time away from data scientists. The FAIR Digital...
This talk discusses the use of Fair Digital Objects (FDOs for short) for a democratised approach to ...
This document is the first iteration of three annual reports on the state of FAIR in European scient...
The scientific community's efforts have increased regarding the application and assessment of the FA...
In this poster we present the FAIR-IMPACT project, "Expanding FAIR solutions across EOSC", which is...
There is broad acceptance that FAIR data (Wilkinson et al. 2016) reuse is desirable, with considerab...
Introduction: Assigning a PID to a whole dataset, as common practice within research data management...
A Digital Object (DO) "is a sequence of bits, incorporating a work or portion of a work or other inf...
Data science is facing the following major challenges: (1) developing scalable cross-disciplinary ca...