FAIR principles open science

What is FAIR?

As a whole, Erasmus University Rotterdam works towards open science as a standard. This means that research data and all academic output should meet the FAIR principles.

The FAIR principles were created to maximize the reuse potential of research data by encouraging researchers to make their data Findable, Accessible, Interoperable, and Reusable.

It is important to note that there is no ‘one-size-fits-all’ in FAIR, and how closely you can follow some of its principles may depend on your research. Not all FAIR data is open and not all open data is necessarily FAIR; instead, it should be “as open as possible, as closed as necessary”. There may be legitimate reasons not to make research data available open access, such as privacy or commercial reasons. Metadata, on the other hand, should always be open.

How can you make your research FAIR? 

Findable

Your data should be easy to find for both humans and computers, with metadata and an assigned persistent identifier that facilitate searching for specific datasets after publication.

Checklist:

• Deposit your data in a trustworthy repository (i.e.: EUR Data Repository, DANS Data Stations, Zenodo, OSF) 
• Provide rich and descriptive metadata
• Assign a persistent identifier (i.e.: DOI, ORCID, ROR, Crossref)
• Include a README file in the data package describing your research and dataset
  • i.e.: General information; title, principal investigator, ORCID, contact for questions, date of data collection, relevant keywords, etc. You can find an example of a README on this page.

Accessible

Your data should be stored for the long term and can be easily accessed and/or downloaded with well-defined access conditions (open access when possible), whether at the level of metadata or at the level of the actual data. It should be possible for humans and machines to gain access to your data.

Checklist:

• Determine access conditions to your data: open, restricted, or closed 
  • If access to data is restricted, specify conditions for access in metadata
  • Clarify the authentication and authorization steps of the access procedure
• Make your metadata accessible, even if your data is not
• Include in your README file:
  • Licenses or restrictions on the data 
  • Links to other accessible locations of your data

Interoperable

Your metadata and/or data should conform to recognized formats and standards. Therefore, they can be read and are ready to be combined with other datasets by humans or computers. Your metadata provides clear descriptions of the data and software required to open it. Data is logically organized using machine-readable filenames and is saved in preferred file formats.

Checklist:

• Store your data in common preferred file formats
• Ensure your metadata follow relevant, field-specific standards
• Use controlled vocabularies/keywords when possible
• Include in your README file:
  • An overview of your data and files
  • A description of the file formats
  • Relevant naming conventions
  • Information about the software needed to process data

Reusable

Your data and metadata should be well described and therefore ready to be used for future research and to be further processed using computational methods. They have a clear and accessible license, such that others know what kind of reuse is allowed.

Checklist:

• Choose a clear and accessible data license 
• Practice good data documentation
• Include files relevant to your research (i.e.: Interview guides, codebook, etc.)
• Ensure your data and metadata meet relevant domain standards
• Let users know how to cite and credit you
• Include in your README file:
  • Description of methods used for data collection and processing
  • Information about the license

How can you assess your FAIR practices?

FAIRness assessment tools

There are multiple, easy to use, tools available to assess the FAIRness of your dataset. These tools can be used for a quick scan of your dataset and can help you become more aware of what is needed to adhere to the FAIR principles described above.

• The FAIR Aware tool, provided by EUR and DANS.
• The self-assessment tool called SATIFYD , developed by DANS to evaluate how FAIR your data is and provides tips on how to make it more FAIR.

There are also automated tools to assess the FAIRness of your dataset. They use valid persistent identifiers (e.g. a DOI) or URL's that refer to the repository where your data is located. This means that to use these tools, your data already needs to be published.

These tools check both the data and metadata provided in the dataset and compare these with the FAIR principles. One example of an automated FAIRness assessment tools is F-UJI:

• F-UJI is a web service to programmatically assess FAIRness of research data objects (aka data sets) based on metrics developed by the FAIRsFAIR project. It can also be used as a tool to check whether a repository you are considering is publishing FAIR datasets.

Support

If you need advice on making your research data FAIR, you can get in touch with support staff available at EUR: 

• Research Data Stewards
• EUR Data Repository Team
• ORCID Team

 Further reading:

• FAIR-Cheatsheet
• Handout Creating your ORCID record
• Making Qualitative Data Reusable – A Guidebook
• Guide to writing a README file

The content of this webpage was created based on resources from NWO, OpenAIRE, LCRDM, PARTHENOS, Utrecht University, the EUR Data Coffee Breaks and GO-FAIR.

This page was last updated in May 2024. Did you find a broken link or (seemingly) incorrect information? Please send an email with the title 'Website content' to datasteward@eur.nl.