Making it FAIR

Last updated on 2024-02-20 | Edit this page

Making it FAIR

Emphasis should be placed in following the FAIR principles when creating multidimensional data or bringing together existing data within a project/initiatives.

FAIR refers to the following actions which should be promoted.


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“The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services…”

  • Data should be linked to rich and structured metadata.

  • Where possible this should be made accessible through a searchable resource such as an aggregation platform.

  • Data should be accessible through a persistent identifiers (which do not change over time). For example, DOIs can be assigned to data through platforms such as Zenodo or Github.


access data
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The user “need to know how they can be accessed, possibly including authentication and authorisation”.

  • Metadata should be accessible via using a protocol for web, such as HTTP/HTTPS which allows to access a webpage over the browser or query a database through a service known as Application Programming Interface (API).

  • Where necessary, the protocol show allow for authentication and authorization to enforce data management rights.

  • Consider who will be excluded from access the data, for instance if this is only available via an institutional platform or in a particular language.


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“The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.”

  • Consider how other users will bring together content from various data sets, for instance to create a new project.
  • For visual media, including images, video and 3D, IIIF (generally pronounced “triple-eye-eff”) supports its interoperability of across websites and institutions.
  • This framework allows to provide access and shared link to a file, as well as its (meta)data .
  • When implemented across many institutions overcomes data silos.

For example, through IIIF it is possible to bring together objects which physically might be in different locations. It does not require a user to download the files but simply to access the files and metadata over the web.


Creative common
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“The ultimate goal of FAIR is to optimise the reuse of data.”

  • Multidimensional data should be released with a clear and accessible data usage license.

  • Provenance data will help for data to not become lost.

CARE data principles

In addition, a series of principles known as CARE have been proposed by the Global Indigenous Data Alliance.

These principles include: Collective Benefit, Authority to Control, Responsibility, and Ethics.

Their focus on enhancing these principles by leveling power relationships where data is created within certain social and historical context.

Challenge: CARE principles for your data practice

Could you reflect on what implications following the CARE principles has for your personal practice when creating, collecting and using data.

For more information:

  • Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J.D., Anderson, J. and Hudson, M., 2020. The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1), p.43.DOI:
  • Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).

Key Points

Principle Key Points
Findability - Available metadata
- Allow for searchability
- Persistent IDs
Accesibility - Use web protocols for access
- Allow for authorisation
- Digital inclusion/exclusion
Interoperability - Data integration
- Overcomes data silos
- IIIF for visual media
Reuse - License content
- Avoid data becoming lost