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This section of the resource introduces practices to be considered at the planning stage in order to help make your data and code more FAIR. This checklist will ensure that your project is set up in the best way to enable you to make your research outputs as FAIR as possible.
Have you considered what materials from your research might be useful to others? [✓]
Have you considered what materials from your research might be useful to others? [✓]
Think about what data you are going to use and collect, or what materials you will create, and what can be shared at the end of your research:
Can it be reused for future research by yourself? Could it be reused for other research projects by other researchers?
Can your experiment design, workflow, or code be reused?
Will you need consent for all planned uses of data? [✓]
Will you need consent for all planned uses of data? [✓]
It's important that you consider at an early stage whether and how you intend to share data from your research at the end of the project. If your research involves participants and you wish to store the data long term or share the data, you must ensure that this is clearly stipulated in your consent form and participant information sheet, as well as your ethics application. It may be a good approach to clearly distinguish between consent to the research itself, and consent for the storage and sharing of data.
It's also good to remember that consent can be withdrawn (from the study and/or from sharing the data), but this might be only possible before the data is anonymised, and the participants should be made aware of this, ideally with a timeline.
A sample participant consent form is available from Research Services webpages.
There is also additional guidance on informed consent, including retrospective consent for data sharing, in the CESSDA guidelines.
Further information about consent in relation to specific data types can be found on the following pages:
Have you secured all necessary permissions? [✓]
Have you secured all necessary permissions? [✓]
You should secure the permissions you need from any data owners or your research partner/s (commercial or otherwise) in order to execute your storage and sharing plans, otherwise you will need to revise these plans. This is often easier and quicker to co-ordinate at the beginning of your research, rather than attempting to amend contracts/sharing agreements later in the project (although this may still possible). Further information specific to data type and category can be found on the pages below:
Data management planning
Data management planning
A Data Management Plan (DMP) describes the data you will collect during your research and how it will be managed, both during and after the project. It is useful for thinking ahead and planning the data you will gather, the processing that will take place, how best to store your data, and if/how you intend to share it. This is a valuable process in ensuring considerations are made in advance which will later allow you to make the data and code FAIR.
Have you created a Data Management Plan? [✓]
Have you created a Data Management Plan? [✓]
You can use the DMPonline platform to help create a DMP:
Some elements of your DMP will be useful for completing other documents for your project (or vice versa), such as ethics applications or participant information sheets.
Via the DMPonline system, you can request feedback on your plan from a data management specialist based in the University Library.
Guidance on using DMPonline can be found on the Library's information page.
DMP and FAIR
DMP and FAIR
Writing a DMP will give you a chance early in your research to think about how to successfully embed the FAIR principles in your work, reducing effort later on and ensuring you and others get the maximum benefit from your outputs. You should read the 'Applying FAIR' section of this resource before completing your DMP. You may choose to give special thought to the following areas:
What data will be created
What data will be created
Consider what data (and other outputs) your project will either create, collect, or process, and how this could be useful to either yourself or other researchers in future. For example, is the raw data collected more valuable than the processed data and/or cleaned data? If not, how will you clearly document the steps that have been taken to process one into the other?
Handling of research data
Handling of research data
The way you handle your data will have implications at the end of your project regarding how easy it is to make the data FAIR. Are you storing your data in open/accessible file formats, which will make uploading it to a repository a straightforward process? Are you keeping your work in a sensible folder structure (preferably incorporating version control) that can be easily described, and including a README document that is up to date?
Methodology and standards
Methodology and standards
While your methodology will be led by the research question, there may be small alterations that can be introduced at the planning stage which could help make your data more FAIR. For example, you might consider anonymising your data in the course of your project, which may make it easier to share (see the sensitive data page), or you may consider ways to reduce the size of your data if you think it is going to be quite large (~1TB+) - again, this may help you to share the data at the end of your project.
Alongside this, consider whether there are any standards in your field of study that you could utilise that would make your data easier to understand and reuse, or more attractive to other researchers.
Have you made plans for data archiving and sharing? [✓]
Have you made plans for data archiving and sharing? [✓]
This is a key area for making your data and research outputs as FAIR as possible, and is covered on the Archiving and Sharing page. The earlier you can put plans in place for this (and update if required throughout the project), the better.
Have you included any data sharing plans in your ethics application? [✓]
Have you included any data sharing plans in your ethics application? [✓]
If you have already stipulated more limited data sharing plans in your ethics application, or if you have stated that data will not be shared and are now reconsidering, you will need to submit an amendment to your ethics application if you wish to change this. Amending an ethics application to allow further sharing of data or code would normally constitute a minor amendment (unless you are collecting sensitive data). For guidance on the process of making a minor amendment to your ethics application, you should contact your departmental Ethics Administrator.
Funding applications - building in FAIR
Funding applications - building in FAIR
Have you factored in time and resources to your funding application for making data and code FAIR? [✓]
Have you factored in time and resources to your funding application for making data and code FAIR? [✓]
This might include incorporating time and costs in your research funding application for a research assistant or associate to devote time to making your outputs FAIR, e.g. selecting sample data for sharing and anonymising, de-identifying interview transcriptions, or creating documents that make the code or dataset more usable once shared.
To help you do this, you might:
Consult the guidelines available from your prospective funder.
Consult this useful information about costing research data management into your project.
Make an appointment to speak with a member of the University Library's Research Data Management team (rdm@sheffield.ac.uk) to discuss the needs of your specific project in terms of FAIR.
Make an appointment to speak with a colleague from your local Research Support Hub for additional guidance.
Speak with colleagues who have recently applied similar processes to their work.
View colleagues' reports from the Unleash Your Data and Software Showcase, a 2022 competition in which researchers across the University were awarded grants of up to £5,000 to make their research data and code more FAIR. You can watch recordings of the talks online.
Consider whether there will be any cost to archiving and sharing your data, for example, in a repository. More information can be found on the Large data page.
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