Making Research FAIR
Guidance for researchers
‘The FAIR principles are key to being able to produce or make our data and code available to as wide a range of people as possible.’
Dr Stephen Livingstone, Department of Geography
‘We can't grow as communities if everything remains on our laptops.’
Dr Alice Pyne, Materials Science & Engineering
‘Making your data and software FAIR is the most effective and efficient way to generate impact.’
Professor Haiping Lu, Department of Computer Science
What is FAIR?
FAIR is a set of principles created with the intention of making data and other outputs more available to, and reusable by, others. The FAIR principles - Findable, Accessible, Interoperable and Reusable - often seek to enhance data's ability to be machine-discoverable and usable.
This resource clarifies these principles and provides practical guidance on how you can apply them.
How to use this resource
If you are unfamiliar with the FAIR principles or would like to reinforce your understanding of how, in general terms, to make your research data and code FAIR, the general guidance contained in this resource will be beneficial.
There are also sections that focus on key actions that can be taken at different stages of a research project to help make your outputs FAIR, focusing on pre-, during, and post-project.
If you already have a general understanding of FAIR principles and practices, you may instead choose to consult the data type-specific advice. These sections are available from the menu at the top under 'Your data type/code', but are also linked to at relevant points in the general guidance.
Researchers planning a project will also find the University's Researcher Landing pages a useful resource.
FAIR as a spectrum
It's worth noting that FAIR isn't all or nothing - a data or code output can be FAIR to a greater or lesser degree, and any movement in the direction of FAIR is a positive one. While following all of the actions in this guidance may seem a little overwhelming, and in some instances may not be possible, taken separately they are fairly straightforward to undertake. By making your outputs FAIR, you will be making your research more visible and improving the research landscape.
The FAIR principles overview - descriptions and actions
You can find a complete outline of the FAIR principles here, but a condensed overview can be found below. This includes a bullet-point breakdown of the key actions needed to fulfil the different principles:
Findable:
Has a unique, persistent identifier - e.g. a DOI.
Has richly descriptive metadata (data about the data).
Indexed in a searchable resource - e.g. a data repository.
To make data/code Findable:
Store data in a repository, which gives it a DOI.
Cite it in publications (using the DOI).
Make sure it’s fully documented - complete all relevant fields when depositing, & include a README file.
Accessible:
Retrievable using a standard, free and open protocol that allows authentication where needed, e.g. http/
i.e. retrievable online
Metadata are accessible even where data is not.
To make data/code Accessible:
Use an appropriate repository for your data/code - see selecting a repository
If you can’t share your data/code, create a metadata-only record.
Interoperable:
Can be integrated with other data, applications and workflows.
Use of open or commonly used file formats.
To make data/code Interoperable:
Check the file formats you’re using are standard or open ones - see 'Is your data in an open/accessible format?' under Archiving and Sharing.
Use standard & accessible vocabularies.
Reusable:
Published with a licence indicating how it can be reused - e.g. a CC licence.
In keeping with community standards.
Clearly documented.
To make data/code Reusable:
Use a Creative Commons licence for data - see Sharing research data | Library | The University of Sheffield
For open source software, choose an appropriate licence from here: https://choosealicense.com
Give as much information (metadata) about the data as possible when submitting to the repository
Include a README file to explain and contextualise the data
Why make research data & code FAIR?
There are a number of reasons why researchers are increasingly adopting FAIR practices.
‘FAIR principles are just completely indispensable for the work we do. The data is so expensive to collect and there is so much information in the datasets we collect that are not relevant necessarily to the question we're asking, that it's just inconceivable that this data shouldn't be out there being reused.’
Dr Ian Sudbery, School of Biosciences
‘‘There have definitely been a range of benefits in sharing these data. For example, in a teaching context, I can use these data for teaching statistics for example. There are some researchers who reused it to look into the reliability and validity of these particular measures that we used.’
Dr Claudia von Bastian, Department of Psychology
FAIR principles help to:
Maximise the value and usefulness of data & code
The usefulness of your data/code doesn't need to stop at the end of your research project - it can go on to contribute to other research going forward, by yourself and others. For example, datasets can be combined with other datasets to create innovative new insights; your outputs can provide a model of how others might go about comparable projects; they could also be used as real world examples in teaching, giving more interesting and realistic insights to students.
Increase the accountability & verifiability of research
In the aftermath of the replication crisis (which followed failed attempts in the 2010s to replicate the results of studies in a number of disciplines), FAIR provides a way of foregrounding sound research practices and allowing others to 'follow your working' in order to verify the soundness of your approach and the replicability of your findings.
Enhance the potential for collaboration
FAIR practices mean that your data and code can be found and used by other researchers working on intersecting topics and projects, creating the potential for productive collaborations.
Speed up the progress of research
Where existing data and code are available to researchers planning new projects, these can be drawn on to avoid going over ground that has already been covered. Instead, the existing outputs can be utilised to more quickly progress to other areas and topics of research.
Ensure you get credit for all your outputs - not just publications
Applying the FAIR principles ensures that your data and code outputs can be recognised and cited alongside your publications.
Promote good data management and ensure long-term preservation.
What are the obstacles?
Obstacles to making data and code FAIR can include lack of time to do so, though the time and resources needed are minimised where these considerations are made as early in the research planning process as possible. When you are applying for funding for a project, you can factor in time and resources (which might include some time from a Research Assistant) to support FAIR processes. Your local Research Hub and the Library's RDM team can advise on this.
Another main obstacle for researchers is lack of familiarity with the principles and how to apply these to your specific project. There may be questions and concerns related to your specific data - for example, what if the data is sensitive, or takes the form of an extremely large quantitative dataset? What if it's derived from data owned by a third party? This is what this resource is designed to address. As well as general guidance, it contains advice and examples specific to particular types of data and/or disciplines.
Feedback?
We're very interested to receive your feedback on this resource. For example, are there any topics you feel are lacking, or additional content you'd be interested in contributing or working with us to provide? Contact rdm@sheffield.ac.uk with any feedback you'd like to pass on.
Acknowledgements: This resource draws on the materials developed during the University Library's FAIR Disciplinary Guidance pilot project by the following colleagues: Tha'er Abdalla, Daniel Bowman, Charlotte Cotterill, Shuangke Jiang, Yi Liu, Nerea Okong'o, Itzel San Roman Pineda, Denis Simsek, Asia Szczepaniak, Matt Tipuric, James Wingham, and Zuzanna Zagrodzka. The resources resulting from this project are available online.