Data sharing & management

No Action (Quant.) 

Data are not shared*, and no formal data management plan exists. Datasets are stored in inaccessible, inconsistent, or poorly organised formats, making them difficult to locate, understand, or reuse. 

* If you use publicly available datasets, clearly cite the source, and share the portion you used, your engagement already exceeds the No Action level. 

Moving from No Action to Emerging in Data Sharing and Management (Quant.) 

1. Begin by understanding why organising and sharing research data matters for transparency, reproducibility, and long-term impact. 
2. Create a simple data management plan. Use free tools such as DMPOnline or the University of California’s DMPTool to outline how you will store, organise, and share your data. 
3. Organise your files. Set up clear folder structures and use consistent, descriptive filenames so datasets are easy to locate and understand later. 
4. Document your data. Add a brief README file explaining what the data contain, how they were collected, and any key variables. 
5. Choose where to share. Explore open repositories such as Zenodo or OpenICPSR that support quantitative datasets. 
6. Protect participant privacy. If your data include personal information, review anonymisation guidance from the UK Data Service. 
7. For an overview and checklist on managing and sharing data ethically and efficiently, consult the UK Data Service Planning Guide. 

Emerging (Quant.)

Data sharing occurs occasionally, usually limited to collaborators or upon request. Some effort is made to organise datasets, but documentation and metadata remain minimal, making reuse or replication difficult. 

Moving from Emerging to Evolving in Data Sharing and Management (Quant.) 

1. To advance from Emerging to Evolving, strengthen your data management practices by improving how you plan, document, and share your datasets. 
2. Update your data management plan. Use free tools such as DMPOnline or DMPTool to describe how you will organise, document, store, and share your data. 
3. Document your dataset. Add a clear README file explaining how the data were collected, what each variable represents, and any data cleaning or transformation steps. 
4. Select a trusted repository. Explore Zenodo, OpenICPSR, or use the re3data registry to find a field-specific data repository. 
5. Make your data findable. When uploading to platforms like Zenodo or Figshare, obtain a free Digital Object Identifier (DOI) to ensure your data are easy to cite and track. 
6. Organise and back up your files. Use clear, consistent filenames and version control to manage updates (see the file-naming guide). 
7. Protect privacy. If your dataset includes personal or sensitive information, review anonymisation guidance from the UK Data Service. 

Evolving (Quant.)

Data are shared in suitable repositories with basic documentation and metadata. A data management plan exists for some projects but is not applied consistently or reviewed throughout the research process. 

Moving from Evolving to Sustained in Data Sharing and Management (Quant.) 

1. To progress from Evolving to Sustained, make data management a consistent and integrated part of every project. 
2. Create and maintain a detailed plan. Use DMPOnline or DMPTool to develop a data management plan for each project and update it regularly. 
3. Enhance documentation. Clearly describe how the data were collected, processed, and cleaned, and define all variables using a README file. Follow recognised standards such as DataCite where possible. 
4. Use trusted repositories. Deposit datasets in platforms such as Zenodo or Figshare, which issue free Digital Object Identifiers (DOIs) so your data can be easily cited and tracked. 
5. Apply an open licence. Choose a suitable licence from Creative Commons to clarify how others can reuse your data. 
6. Track versions and use open formats. Manage file versions with GitHub or the Open Science Framework, and store data in open formats such as CSV or TXT. 
7. Check data quality. Use the UK Data Service quality checklist to ensure your data are clear, accurate, and reusable. 
8. Protect privacy and monitor impact. Review your datasets for sensitive information and anonymise as needed using the UK Data Service guide. Track downloads and citations through repository metrics, such as those provided by Zenodo. 

Sustained (Quant.)

Data are systematically shared in trusted repositories that follow the FAIR principles. Comprehensive metadata and documentation ensure that datasets are fully understandable and reusable by others. Data management is embedded throughout the research workflow and reviewed regularly to maintain quality and compliance. 

Guidance for Sustained Level in Data Sharing and Management (Quant.) 

Congratulations on reaching this level of practice. You are consistently managing and sharing data to a high standard. As a next step, consider contributing to your research community by sharing your expertise and promoting good practice. To maintain and further improve at this level, consider the following: 

• Support others. Offer workshops or training sessions using free materials such as the DataONE Education Modules or UK Data Service Training Resources. 
• Shape institutional policy. Engage with your university’s open data or research integrity committees to help strengthen policies and support systems for data sharing. 
• Monitor data use. Track how your data are accessed, downloaded, or cited using platforms such as OpenAIRE. 
• Cite data properly. Follow DataCite guidelines and include dataset citations in your papers, presentations, and reports.