Sharing your data

Sharing research data increases their impact by making them available for reuse by others. This maximises their value and can ensure your data contribute to future research. Many research funders as well as the University encourage you to share your research data.

Data should be shared openly with as few restrictions as possible. There are instances where data cannot, or should not, be shared, but a good rule is to make your data "as open as possible, as closed as necessary."

It is also important that the data you share adhere to FAIR-Principles.

Planning to share

Which data do I share?

Not all the data you produce as part of your research need to be shared, but you should consider sharing those data that:

  • Underpin your research publications
  • Are unique or cannot be easily reproduced
  • Are noteworthy
  • Are anonymised

Remember to check your research funder if you have requirements for sharing data.

What about sensitive data?

Much data, even sensitive data, can be shared if appropriate steps are taken. The most important step in sharing sensitive data is to plan for it from the project start. Strategies for sharing sensitive data include:

  • Have a data sharing agreement
  • Obtain consent to share
  • Anonymise data
  • Apply an embargo
  • Choose an appropriate licence
  • Make only a subset of your data available
  • Choose a repository that has controlled access

See our page Ethical and legal issues for more information on managing sensitive data.

If you have any questions, contact us at rdm@liverpool.ac.uk.

Where do I share my data

Choose a repository

Data are best shared through a suitable research repository that provides your dataset with a DOI. Repositories also support you in making your data as FAIR as possible.

You can share your data through:

Your research funder may specify the repository you should use, so check their requirements.

Deposit with Liverpool's Data Catalogue

The Data Catalogue allows University of Liverpool researchers to create a record of their finalised research data in a secure online environment.

  1. Prepare your dataset using the Data deposit checklist below.
  2. Log into the Data Catalogue and upload your dataset and README file. Be sure to fill in as many fields as you can. The RDM team will check each submission before being made live.

If your data are held elsewhere, you can still create a metadata record. Be sure to include a description, keywords, and the DOI associated with the dataset.

See our video on how to deposit your dataset. 

How to share data

Data deposit checklist

When you are ready to deposit, make sure 

  • The data are as FAIR as possible
  • The dataset is in a standardised and accessible format.
  • Any data variables, value labels, codes, or abbreviations are clear, complete and consistent
  • Data are anonymised
  • The file(s) has a meaningful name
  • There are no copyright or contractual restrictions to sharing the data
  • A README file is included
  • You have chosen an appropriate licence.
Anonymising data

 Anonymisation is a good strategy for sharing data that contain personal or sensitive information.

The UK Data Service has an excellent guide on Anonymisation for both qualitative and quantitative data. They also have a step-by-step guide you can use.

Making data FAIR

The FAIR principles are a useful framework for thinking about sharing data in a way that will enable maximum use and reuse. Depositing your data in an open repository is a good way of ensuring the data align with many of these principles.

Findable

  • Data are described with rich metadata
  • Metadata and data have a unique identifier such as a DOI
  • Registered and searchable such as in a repository

Accessible 

  • Metadata and/or data are as open as possible
  • Available in an open repository
  • Formats and software are standardised

Interoperable

  • Metadata and data use standardised vocabularies

Reusable

  • A README file or documentation provides data descriptions, processes, methodologies, and accompanies the data.
  • A licence accompanies the data and clearly outlines conditions of use and reuse.
README file

 A README file includes information about your data such as:

  • A description or list of files included
  • Methodology, data processing, or quality assurance processes
  • Tools and software used to create or access the data
  • Associated publications
  • Any other information necessary to understand your data

Cornell University's Research Data Management Service Group has created an excellent README template you can use.

Data licences

A licence clearly outlines reuse rights of your data. In some cases, it might be necessary to use multiple licences for different aspects of the data such as source code and databases.

Licences for datasets include Creative Commons, Open Data Commons, or the Open Government Licence.

Use the Licence selector to help you Choose a licence

ORCIDs

An ORCID iD is a unique researcher identifier which can be added to your outputs to ensure your work is easily distinguished from that of other researchers.

The Library's Open Research team provides more information on ORCIDs and how to use them.

Software and code

Making software and code openly available requires similar, but slightly unique, considerations. Here is a checklist specifically for making code open and FAIR:

  • Develop your code with a version control system and host it on an open repository such as GitHub, GitLab, or Bitbucket.
  • Include code-level documentation in the code comments and headers.
  • Document the software and code with README and Markdown files. Include these in the root directory.
  • Add a licence and include the licence file in the root directory.
  • Include a Citation file: CITATION.cff in the root directory.
  • Share the code via a repository such as Zenodo, Figshare, or Software Heritage which assign the code
  • snapshot a DOI.
Data access statements

Data access statements or data availability statements give information about where the data supporting a publication can be found and the conditions of access.

Data access statements should accompany all your publications, even if no data were created or data are unavailable. A data access statement needs to include:

  • Where the data can be found (preferably a repository)
  • The Digital Object Identifier (DOI) or URL link
  • Details of use or restrictions of use

 

Examples of data access statements:

Openly available data

"Data supporting this study are openly available from [NAME OF REPOSITORY] at [DOI]."

 

Data under embargo

"Supporting data will be available from [NAME OF REPOSITORY] at [DOI] after a 12 month embargo period from the date of data collection to allow for the publication of research findings."

 

Data are restricted due to ethical or legal constraints

"Due to [GIVE REASONS DATA CANNOT BE SHARED], supporting data cannot be made openly available. Further information about the data and conditions for access are available at the [NAME OF REPOSITORY]: [DOI]."

"Anonymised interview transcripts from participants who consented to data sharing, plus other supporting information from the UK Data Service, subject to registration, at [DOI]."

"Data supporting this publication are available from [NAME OF REPOSITORY] at [DOI]. Access to the data is subject to approval and a data sharing agreement due to [GIVE REASONS WHY DATA ACCESS IS RESTRICTED]."

 

Secondary analysis

"This study was a re-analysis of existing data that are publicly available from [NAME OF REPOSITORY] at [DOI]."

 

No new data

"No new data were created during the study."

 

The University of Bath has further examples of data statements you can use.