Organize data

It is valuable to consider from the outset of a research project how you will organize and structure the data you work with. A well-documented and consistently applied data organisation strategy makes it easier for both you and your project collaborators to locate and manage data and files throughout the research process.  

Maintaining good organization of research data also improves data traceability in research. Whether you need to revisit data yourself to verify findings and demonstrate your research process, or whether a future researcher or reviewer wishes to examine a study to understand how the data were processed and what led to specific results, good order and an intuitive organization is essential.

If your goal from the beginning of the project is to share data, maintaining good structure and documentation throughout will make it easier to compile and summarize research data as the project nears completion. 

Research data management

Managing research data involves systematically handling and organizing data throughout a research project. Well-planned data management ensures order within the project, which minimizes time-consuming errors and makes it easier to find project information, the correct version of data, and relevant documents.

It is recommended to document how data are organized, stored, and backed up in a data management plan, DMP. If your organization does not have a specific DMP template, you can use SND’s checklist for data management plans as a guide.

There is no universal solution for research data management, as research data and projects can vary significantly. However, with good documentation and structured data management routines, handling research materials becomes both more efficient and more secure. These pages provide fundamental advice and tips on how to organize and structure your data.

Keep things organized. Be consistent with data organization.

• Maintain a clear and logical folder structureOpens in a new tab and consistent file naming conventionsOpens in a new tab . This reduces the risk of errors, such as selecting the wrong file during data processing or analysis.
• Do not rely on memory. Document all data processing and modifications so that any errors can be traced and corrected. Documentation is crucial for all projects but is particularly essential for long-term projects, projects with multiple collaborators, and projects that involve new team members over time.

Use secure data management. Data should be accessible to authorized researchers while remaining protected from unauthorized access, destruction, or manipulation. Data storage security should be appropriate to the sensitivity of the data. 

• Utilize reliable storage solutions. Ensure that storage media are of high quality (see the section on data security). If you need guidance on selecting the appropriate storage solution for your research data, consult your IT department.  
• Regularly back up data and documents to reduce the risk of data loss (see the section on data security). Most universities and organizations have backup systems in place, but if you are uncertain about the policies for the systems you use, consult your IT department.

Depending on the type of data you work with, you must also ensure that they are handled appropriately in terms of information classification and protection requirements.