How to make your data FAIR

This page will show you how you can make your research data more FAIR by taking you through six FAIRification practices:  

  1. Documentation 
  2. File formats 
  3. Metadata 
  4. Access to data 
  5. Persistent identifiers 
  6. Data licences 
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Case introductions

Engineering

Humanities

Health Science

Social Science

Summary

Structure of this page

Each FAIRification practice, i.e. documentation, file formats, metadata, access to data, persistent identifiers, and data licences, is discussed on its own subpage. Each subpage explains what the FAIRification practice means in a FAIR context and provides examples of steps you can take before, during and after your research project to make your research data more FAIR. Recommendations, standards and examples are given for quantitative, qualitative, and sensitive data to help you FAIRify your research data in a way that makes sense for you.  

We recommend you start by watching the case introductions below. 

Sensitive data

To make this website easier to read the words “sensitive data” are used to refer to sensitive, personal, and confidential data. 

Case introductions

Throughout the FAIRification subpages, four research projects are used as examples of how you can make your research data more FAIR. You will see short interview clips of researchers in Engineering, Humanities, Health Sciences, and Social Sciences. The researchers share their methods and solutions for problems specific to quantitative data, qualitative data, and sensitive data. Watch all four case introductions - or just the ones you expect will be most relevant to your research field and data type.  

Quantitative data

Qualitative data

Sensitive quantitative data

Sensitive qualitative data

FAIRification practices

18 min read Documentation

Documentation
12 min read File formats

File formats
20 min read Metadata

Metadata
10 min read Access to data

Access to data
7 min read Persistent identifiers

Persistent identifiers
5 min read Data licences

Data licences

NEXT

Documentation

In summary...

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Findable

You make your research data findable for your collaborators and the rest of the world by: 

  • Publishing your data and/or metadata in a searchable resource such as a repository like Dataverse, Zenodo or Figshare that assigns a persistent identifier

  • Including rich accurate machine-readable descriptive metadata and keywords to your data, preferably according to a community-specific metadata standard (e.g. Dublin Core) or ontology
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Accessible

You make your research data accessible by: 

  • Attaching a data licence or clear data accessibility statement in your openly available administrative metadata 

  • Ensuring your data are archived in long-term storage and retrievable by their persistent identifier using a standard protocol

  • Giving access to the metadata, even if the data are closed
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Interoperable

You make your research data interoperable by: 

  • Including sufficient and standardised structural metadata in accordance with your research community’s standard controlled vocabulary or ontology 

  • Including use of common standards, terminologies, vocabularies, ontologies and taxonomies for the data 

  • Preferring open, long-term viable file formats for your data and metadata 

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Reusable

You make your research data reusable by: 

  • Attaching all the relevant contextual information required for re-use in either the documentation or metadata attached to your data 

  • Including sufficient and standardised structural metadata in accordance with your research community’s standard controlled vocabulary or ontology 

  • Preferring open, long-term viable file formats for your data and metadata

  • Applying a machine-readable data licence