Data sharing

What to consider in your DMP

This section focuses on how research data will be shared after a project has ended, rather than how active research data will be managed during the project.

You'll need to think about:

• how you'll make your data available
• which repository you'll use
• what licence or access conditions will apply
• whether any legal, ethical, commercial or contractual restrictions apply
• how people will discover and access the data

Choosing a data repository

A data repository is a trusted online platform for sharing research data, making it easier for others to discover, access and reuse it.

In line with RDM Policy requirements, you must deposit selected research data in an appropriate repository as soon as possible at the end of your project. Make research data as open as possible and only as restricted as necessary.

If your research relies on custom software or code, you should also consider sharing these outputs to support reproducibility.

Licensing and access conditions

Data sharing aims to enable the easy and effective reuse of your research data.

To support reuse, you should apply clear licence terms that explain how others may access and use your data. Where appropriate, choose the most permissive licence possible to maximise opportunities for reuse.

For openly shared datasets, the preferred licences are typically:

• CC0 Public Domain Dedication, which waives copyright and related rights to the fullest extent possible, allowing unrestricted reuse.
• CC BY 4.0, which allows reuse provided the original creators are acknowledged.

If legal, ethical, commercial, or contractual restrictions apply, you may need to limit access to your data or apply specific conditions for reuse. Any restrictions should be clearly documented in the metadata accompanying the dataset.

Research software and source code should generally be shared under an OSI-approved licence, such as:

• MIT
• BSD
• GPL
• Apache 2.0.

Applying a software licence helps others understand how the code can be used, modified, and redistributed.

Restricted and sensitive data

Not all research data or code can be made publicly available. You may need to limit or prevent access to your data if it contains:

• personal information
• commercially sensitive material
• confidential information
• or is subject to legal or ethical restrictions.

In these circumstances, you should retain the data securely in an appropriate storage system for the required retention period. Where data cannot be shared openly, a publicly discoverable metadata record may be sufficient to support transparency and enable others to understand that the dataset exists.

Where appropriate, metadata records should include information on whether access requests will be considered and the process for requesting access.

Costs and retention

Selected research data should typically be retained for a period of at least ten years from publication. This is in line with any legal, contractual, regulatory or ethical requirements, so it's important to be aware of the potential costs.

Anticipating these costs is especially important where you plan to preserve large volumes of data from your project. Many repositories offer free storage up to a defined limit, after which additional charges may apply.

Where you plan to share hundreds of gigabytes or even terabytes of data, consider whether it's necessary to retain all the data. If it's necessary to share a high volume of data, then you should factor this expense into your funding application. Most research funders with an open data policy will consider this to be acceptable cost.

Data citation

Data citation is an important part of good research practice. Datasets should be cited in the same way as other research outputs. This allows others to identify, access, verify, and reuse the underlying data.

When publishing research, you should:

• cite datasets used in your research, including secondary data
• cite any datasets generated by your own research
• include a full dataset citation in the reference list
• use the DOI or other persistent identifier wherever available

A simple dataset citation format is:

Creator (Publication Year). Title. Version. Publisher. Resource Type. Identifier

Green, Nathan (2024). Sound and Sound Quality Metric Data. University of Salford. Dataset. https://doi.org/10.17866/rd.salford.24998678.v1

FAIR Data Principles

Sharing your data in a repository is the best way to meet the FAIR Principles. These are a framework for making your data:

• Findable
• Accessible 
• Interoperable
• Reusable. 

These principles should be put into practice throughout the lifecycle of a research project. They're particularly relevant when it comes to data sharing.

Below you can find some suggested actions to make your data FAIR.

Findable

• Describe your dataset with rich metadata
• Ensure your data is indexed in a searchable data repository
• Create a unique and persistent identifier, i.e. a Digital Object Identifier (DOI)

Accessible

• Provide a clear route to access your data e.g. via a repository
• Be clear about conditions of access where data is restricted
• Create a metadata record in your chosen repository, where it's not possible to share the dataset

Interoperable

• Check FAIRsharing.org for disciplinary standards that apply to your data
• Ensure the data repository you choose allows you to include links or references to other related data
• Use open, non-proprietary file formats to ensure your data can be reused by others, integrated with other datasets, and accessible in the long term

Reusable

• Add as much metadata and documentation as possible when depositing your dataset in a repository e.g. README file
• Apply an open license to your data, preferably CC0 or CC-BY 4.0
• Ensure your data and metadata adhere to relevant disciplinary and community standards