Enabling reuse and impact

When data are well prepared, documented and licensed, they can be discovered, reused and cited by others, extending the value and impact of the original research. Supporting reuse also helps ensure that data producers receive appropriate credit for their work and that the wider research community benefits from shared resources.

Two important components of enabling reuse are data citation and monitoring reuse and impact.

Data citation

Data should be cited in the same way as other research outputs, such as journal articles or books. Formal data citation supports transparency, enables others to locate the exact dataset used in a study and ensures that data producers receive appropriate academic credit.

When data are deposited in responsible repositories, persistent identifiers such as Digital Object Identifiers (DOIs) are usually assigned. These provide a stable, long-term reference to the dataset and should be included in publications, reports and other outputs that use the data.

A standard data citation typically includes:

• creator or data producer name(s)
• year of publication or release
• data title
• repository or archive name
• version number (where applicable)
• persistent identifier (such as a DOI).

Including version information is particularly important for datasets that are updated over time, as it allows users to reference the exact data used in their analysis.

Repositories usually provide recommended citation formats for deposited data. Data producers should include this information in documentation and encourage users to cite the data correctly.

For example:

• APA style: Office for National Statistics, Department for Environment, Food and Rural Affairs. (2026). Living Costs and Food Survey, 2023-2024. [data collection]. 2nd Edition. UK Data Service. SN: 9468, DOI: http://doi.org/10.5255/UKDA-SN-9468-2
• DataCite style: Office for National Statistics, Department for Environment, Food and Rural Affairs, (2026). Living Costs and Food Survey, 2023-2024. 2nd Edition. UK Data Service. [data collection]. DOI: http://doi.org/10.5255/UKDA-SN-9468-2
• Harvard style: Office for National Statistics, Department for Environment, Food and Rural Affairs, 2026, Living Costs and Food Survey, 2023-2024, [data collection], UK Data Service, 2nd Edition, Accessed [insert date]. SN: 9468, DOI: http://doi.org/10.5255/UKDA-SN-9468-2
• Vancouver style: Office for National Statistics, Department for Environment, Food and Rural Affairs. Living Costs and Food Survey, 2023-2024. [data collection]. 2nd Edition. UK Data Service, 2026 [Accessed [insert date]]. Available from: DOI: http://doi.org/10.5255/UKDA-SN-9468-2

Reporting reuse and impact

Tracking data reuse helps demonstrate the value of data sharing and supports impact reporting to funders, institutions and stakeholders. Evidence of reuse may include citations in academic publications, downloads or access statistics from repositories, references in policy documents, teaching use, or evidence of reuse in secondary research projects.

Where possible, data producers should:

• monitor usage statistics and citation reports provided by repositories.
• keep records of known reuse cases and collaborations.
• link datasets to related publications and outputs using persistent identifiers.
• include data reuse evidence in grant reports, impact case studies and institutional assessments.

Demonstrating reuse strengthens the visibility of data as a research output and supports recognition of data management as a core research activity.

By enabling clear citation practices and monitoring reuse, data producers help ensure that shared data generate ongoing scientific, social and policy value beyond the original project.

Sharing code and syntax

Sharing research code and syntax is a core component of reproducible research. Increasingly, research transparency depends not only on access to data, but on access to the analytical steps that produced published findings.

Whether you are working with SPSS, Stata, R, Python or other analytical environments, sharing your code enables others to:

• understand how variables were created or transformed
• replicate analytical decisions
• test alternative assumptions
• build on your methods
• reduce unnecessary duplication of effort.

In many cases, code is the clearest record of how data were processed and analysed. Even where data cannot be openly shared, for example due to confidentiality or contractual restrictions, code can often still be made available.

Code as part of the research record

Code should be treated as a research output in its own right. It forms part of the methodological record of a study and supports both verification and reuse.

Code may include:

• data cleaning scripts
• variable construction syntax
• statistical analysis routines
• data linkage or harmonisation procedures
• visualisation workflows.

Where feasible, code should be deposited alongside the data collection to which it relates. Where data are restricted, code can still be deposited independently, provided it does not expose confidential information.

Repositories such as UK Data Service ReShare enable researchers to deposit code and syntax as standalone collections or part of data collections. This ensures persistent identifiers, formal citation and long-term accessibility. See further guidance on depositing code in ReShare.