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FAIR Principles for Research Data
In order to support open science and increase access to and reuse of data, proposed best practices emphasize that research data should be FAIR:
The following resources will help you better understand what FAIR means and how to achieve it.
The FAIR Data Principles
A concise overview of the 15 FAIR principles, developed via a FORCE11 working group. Details were published in 2016, in the Scientific Data article linked below.
A set of proposed metrics, based on the 15 FAIR principles, to quantify levels of FAIRness.
FAIR in practice - 2018 Jisc report on the Findable Accessible Interoperable and Reuseable Data Principles
"This report investigates the meaning and (potential) impact of the FAIR data principles in practice. These principles were established by a group of diverse stakeholders engaging via a working group in FORCE11. They are referenced in many policy documents and in developments of open science, for example, the European Open Science Cloud."
Open science is all very well but how do you make it FAIR in practice?
by Rachel Bruce and Bas Cordewener, 12 July 2018, on the Jisc blog.
Discusses the recent "FAIR in Practice" report. "Open science is about increasing the re-use of research, and making sure that publicly funded research is accessible to all. It sounds straightforward, but there are some issues that we need to iron out first, and this is where FAIR comes in."
Turning FAIR Data into Reality: Interim report from the European Commission Expert Group on FAIR data
"The Group has a remit to provide recommendations, indicators and input on the financing of activities required to turn FAIR data into reality at an EU, Member State and international level. A FAIR Data Action Plan
has also been proposed." (2018)
FAIR Data Advanced Use Cases: From principles to practice in the Netherlands
"The purpose of this report is to build and share expertise on the implementation of FAIR data policy in the Netherlands. The six use cases included in this report describe developments in FAIR data, and different approaches taken, within different domains. For SURF, it is important to gain a better picture of the best way to support researchers who want to make their data FAIR." (2018, SURF)