Material in this section of this resource draws substantially on work completed by  Denis Simsek and Zuzanna Zagrodzka for the FAIR disciplinary guidance project.

FAIR Code/Software

At the policy level, code is considered part of FAIR, with the European Commission expert group on FAIR data stating that “Central to the realisation of FAIR are FAIR Digital Objects, which may represent data, code or other research resources” (Lamprecht et al., 2020). Applying the FAIR principles to research code will provide similar benefits, such as enabling transparency, reproducibility and reusability of research that facilitate efficient access to code-based knowledge by industry, science, education and society.

Below are some tips and guidance on how to do this so your code meets the FAIR principles where possible.

Translating the FAIR principles to code

Findability

1. Create a description of your code with metadata and ontologies

The following tools may be useful in achieving this:

• Codemeta is a set of keywords used to describe code and how to structure them in a machine readable way

• Edam is an example of an ontology that provides terminology that can be used to describe bioinformatics code; however, there may be a more suitable one for your area of research.

2. Register your code in a code registry

You can place your code in a code registry and associate it with yourself and your research team. Some example of code registries include:

• GitHub 

• Zenodo

• Biotools

3. Get and use a unique and persistent identifier for your code

Giving your code a DOI will mean it can be cited in publication and other communications in order to open up your research to others and invite collaboration, as well as ensuring a constant link to your code. This might be achieved, for example, by exporting a version of the code to a repository (such as ORDA) in order to obtain a DOI. This can be cited in publications and other outputs.

Interoperability:

1. Explain the functionality of your code.

Ensuring that people know what your code does and can therefore use it correctly themselves will enable it to be re-used more, and more effectively. Where possible, use terms from a domain ontology (such as EDAM for bioscientific data) standard across your area. You should also ensure that your code is well documented and commented, in an easy to understand manner.

2. Use standard (community agreed) formats for inputs and outputs that allow data exchange between different pieces of code.

Where possible and applicable, outputs (even between pieces of code) should use open and accessible data formats, which will help if other researchers only wish to use part of your code. However, if there are other, more bespoke, inputs/outputs, these should be as standard and widely used as possible.

For example:

• FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences that has become the community/universal standard, (although there is currently no standard file extension and could be; .fasta, .fna, .ffn, .faa, .frn, .fa.)

• NetCDF is a standard file format used for sharing of array-oriented scientific data.

You should avoid defining your own standards.

Code Repositories and Platforms

Code Repositories:

ORDA: The University of Sheffield repository, allowing researchers to deposit data, code and other materials; you can also get a DOI assigned for your code.

Zenodo: Allows the depositing of research papers, datasets, research code, and reports; and assigns a DOI for your code.

PyPI: A repository specifically for packages for the Python programming language.

The Comprehensive R Archive Network (CRAN): A network of ftp and web servers around the world that store identical, up-to-date versions of code and documentation for R.

Docker Hub: A hosted repository service for finding and sharing container images.