The FAIR principles formulate guidelines for the sustainable reusability of research data. FAIR stands for Findability, Accessibility, Interoperability, and Reusability of data and metadata. While there is a growing body of general implementation guidelines, so far there is a lack of specific recommendations on how to apply the FAIR principles to the specific needs of social, behavioural and economic science data. These disciplines work with highly diverse data types that often contain confidential information on individuals, companies, or institutions. These features pose some challenges to the useful implementation of the FAIR principles - especially regarding the machine-actionability of data and metadata that is at the core of the FAIR ...
Although FAIR Research Data Principles are targeted at and implemented by different communities, res...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
This document is the second iteration of three reports on the state of FAIR in the European scientif...
This practice paper describes an ongoing research project to test the effectiveness and relevance of...
For open science to flourish, data and any related digital outputs should be discoverable and re-usa...
In this review, we discuss FAIR Data, why it exists, and who it applies to. We further review the pr...
The FAIR Expertise Hub for the Social Sciences, funded by PDI-SSH, is being established to support d...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
Although FAIR Research Data Principles are targeted at and implemented by different communities, res...
This report investigates the meaning and (potential) impact of the FAIR data principles in practice....
This is a review of book titled "Data Stewardship for Open Science: Implementing FAIR Principles", w...
Despite the fact that the implementation of the FAIR principles (Findable, Accessible, Reusable, Int...
The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoper...
International audienceDespite the fact that the implementation of the FAIR principles (Findable, Acc...
Although FAIR Research Data Principles are targeted at and implemented by different communities, res...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
This document is the second iteration of three reports on the state of FAIR in the European scientif...
This practice paper describes an ongoing research project to test the effectiveness and relevance of...
For open science to flourish, data and any related digital outputs should be discoverable and re-usa...
In this review, we discuss FAIR Data, why it exists, and who it applies to. We further review the pr...
The FAIR Expertise Hub for the Social Sciences, funded by PDI-SSH, is being established to support d...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
Although FAIR Research Data Principles are targeted at and implemented by different communities, res...
This report investigates the meaning and (potential) impact of the FAIR data principles in practice....
This is a review of book titled "Data Stewardship for Open Science: Implementing FAIR Principles", w...
Despite the fact that the implementation of the FAIR principles (Findable, Accessible, Reusable, Int...
The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoper...
International audienceDespite the fact that the implementation of the FAIR principles (Findable, Acc...
Although FAIR Research Data Principles are targeted at and implemented by different communities, res...
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A dive...
This document is the second iteration of three reports on the state of FAIR in the European scientif...