It is becoming increasingly important for researchers to make their data, code, software and other research outputs accessible to all. While this practice, known as ‘open science’, is a powerful driver for transparency and accountability in scientific research and increased collaboration, some researchers have legitimate concerns about the protection of sensitive data (such as privacy, security or commercial interests), and the risk of their openly published research being scooped by others. Some researchers also question how their research information can be organised effectively to make it not just openly available, but also easily found, understood, exchanged and cited.

It is for this reason that institutions such as ICAC, in line with many major funding bodies, support the principles of FAIR data – research information that is as open as possible, and as closed as necessary.

What are the FAIR Principles?

The FAIR Principles (Findable, Accessible, Interoperable, and Reusable) are a set of guiding principles proposed by Force11, a consortium of scientists and organizations to support the reusability of digital assets. These principles have since been adopted by research institutions worldwide. Thankfully, the same digital movement brought along digital platforms where the data could be stored and relayed. But how to use these digital platforms in an organised manner? The FAIR principles are a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.

Which are FAIR data benefits for research(ers)?

Making research data more FAIR will provide a range of benefits to researchers, research communities, research infrastructure facilities and research organisations alike, including:

• Achieving maximum impact from research.

• Increasing the visibility and citations of research.

• Improving the reproducibility and reliability of research.

• Attracting new partnerships with researchers, business, policy and broader communities.

• Enabling new research questions to be answered.

What are the FAIR data requirements?

When making data FAIR, metadata plays an important role.

To be findable

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.

Make your data findable by ensuring:

• Data are described with rich metadata.
• (Meta)data are assigned a globally unique and persistent identifier (for example a DOI).
• (Meta)data are registered or indexed in a searchable resource.

To be accessible

It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not necessarily mean that data need to be open! In cases where the data cannot be made openly accessible, it is still possible to make the metadata publicly available.

Make your data accessible by ensuring:

• The repository you are using to share your data assigns persistent identifiers by which data can be retrieved.
• The access procedure includes authentication and authorisation steps, if necessary.
• Metadata are accessible, wherever possible, even if the data are not.

To be interoperable

To speed up discovery and uncover new insights, research data should be easily combined with other datasets, applications and workflows by humans as well as computer systems.

Make your data interoperable by using:

• When possible, well-known and preferably open formats and software.
• Relevant standards for metadata.
• Community agreed schemas, controlled vocabularies, keywords, thesauri or ontologies where possible.

To be reusable

Research data should be ready for future research and future processing, making it self-evident that findings can be replicated and that new research effectively builds on already acquired, previous results.

Make your data reusable by ensuring the data:

• Is well-documented to support proper data interpretation.
• Have a clear and accessible data usage license so others know what kinds of reuse are permitted.
• Has provenance information to make clear how, why and by whom the data have been created and processed.
• (and metadata) meet relevant domain standard.