How to make your data FAIR
How to make your data FAIR - OpenAIRE
Things to remember · Data can be FAIR but not open. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions.
Preparing FAIR data for reuse and reproducibility
Additional tips for preparing your data for sharing · You may choose to include raw data (as originally collected), processed data (e.g., signals encoded), or ...
Make your research data more FAIR
This page will show you how you can make your research data more FAIR by taking you through six FAIRification practices.
FAIR data - CESSDA Data Management Expert Guide
A persistent identifier (PID) for the data object as a whole. Persistent identifiers like DOIs prevent link rot. · A sufficient set of metadata. A sufficient and ...
The FAIR principles - Research Data Management Support - Utrecht ...
Potential benefits of making your data FAIR include: · It may increase the impact of your research (e.g., reusing data for other purposes). · It increases ...
Make your data FAIR | Research | Imperial College London
These principles emphasise the importance of making data findable, accessible, interoperable, and reusable by both humans and machines.
What are the FAIR data principles?
To make data findable, data and supplementary materials should have sufficiently detailed descriptive metadata as well as a unique and ...
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or ...
Making Data FAIR | ARDC - Australian Research Data Commons
An abbreviation for “findable, accessible, interoperable and reusable”, the FAIR Principles provide a framework for sharing data in a way that maximises its use ...
FAIR Data - Research Data Management - LibGuides at UCD Library
Findable – It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, and the ...
FAIR (Findable, Accessible, Interoperable, and Reusable) data principles improve the discoverability of interoperable datasets.
Why make your research data FAIR? · could · help to maximise your research impact and help peers and your 'future self' understand your research projects and data ...
FAIR Data - Guides at Penn Libraries
What types of data will you collect? · In what format will your data be stored? · How will ensure sensitive data is secure? · How will you make it ...
The FAIR data principles | Swedish National Data Service
The FAIR data principles state that it should be possible to find research data, there should be information about how to gain access to them, they should be ...
Research Data Management: FAIR Data - Maynooth - LibGuides
How to make your Data FAIR · The Four Basics of FAIR · 'Findable' i.e. discoverable with metadata, identifiable and locatable by means of a ...
FAIR Principles for Research Data Management | ARDC
Making your data findable, accessible, interoperable and reusable (FAIR) maximises the impact of that investment, including gaining more ...
The FAIR Data Principles were developed to guide you in the process to make your data findable (your data can be discovered by others), accessible.
FAIR data principles — Research Data Management - KU Leuven
Key elements for FAIR data are rich metadata and documentation, using open or standard file formats, having persistent identifiers for data objects and using ...
Make scientific data FAIR - Nature
Make depositing open and FAIR data a priority for all. Universities, funders, repositories, publishers and societies worldwide need to cooperate ...
Six quick tips to start making your data FAIR | The Hyve
Six quick tips to start making your data FAIR · 1. Describe the origin of your dataset in the metadata · 2. Describe the purpose of your dataset in the metadata.
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