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Managing and storing data

Discover how to describe, document and store your research data.

What is research data management?

Research data management (RDM) refers to organising, documenting, formatting and storing your research data throughout your research project, in ways that support its discoverability, potential sharing and re-use, and preservation. These practices are informed by legal, statutory, ethical and funder requirements. This management is applied from the planning stages of a project through to day-to-day management and long-term preservation and sharing.

What we consider to be research data

The University of Surrey considers research data to be any material collected, observed, processed, or created for the purpose of analysis and on which research findings and outputs are based. This includes data and documentation which are commonly accepted in the scholarly community as necessary for validation or replication of research findings. Research data may be in digital or non-digital formats. This could include:

  • Audio, video, and images or photographs
  • Text documents and spreadsheets
  • Code, scripts, algorithms, models, and software
  • Protocols and methodologies
  • Specimens and samples
  • Collections of digital objects
  • Lab notebooks, field notes, and diaries
  • Questionnaires and codebooks
  • Interview schedules and transcripts
  • Test responses
  • Slides, artefacts, specimens, samples
  • Databases.

Why manage your research data?

At its heart, good research is good research data management. Having a strategy for how you are going to manage your data and documentation during your project will make every stage of research easier and more secure, especially when it comes to sharing and preserving your data for verification and reuse.

Ways to manage research data

Consider including some of the below in your data management plan.

Files, versions, and formats


Documentation is the foundation of good research and should be started early. It makes your research understandable, verifiable, and reusable – first for you and then for others. Imagining these future users can help you assemble the best documentation for your project.

It can be embedded within research files, like in code, scripts, headers, summaries, label descriptions or built-in program documentation. One of the best ways to ensure the quality of your data is to automate your data creation or analysis as much as possible, which in turn becomes indispensable documentation. Take a look at an example from biology.

Documentation exists at several levels:

  • Project or study level: For example research questions, methods, instruments, and the context of data collection and analysis
  • File or data level: For example what each file contains, how files relate to each other, the components, structure, and logic of data files
  • Variable level: For example code books or data dictionaries with definitions of variables, ranges, etc
  • Metadata level: For example structured descriptions of a study or dataset consisting of defined elements to facilitate discovery and reuse, usually created as part of a data repository deposit; sometimes discipline specific. (All of Surrey’s shared and preserved data must have a metadata record in our repository).


The UK Data Service provides extensive advice on how to document your data, including data level documentation and study level documentation. More documentation to consider:


Storage and collaboration

Two of the biggest risks to research data are accidental loss or unauthorised access. We can mitigate those risks by adopting a few simple practices for storing our data.

Use University storage

During active research the best place to house your data is on University storage, where it will be regularly backed up and subject to greater access controls. This includes the University’s SharePoint or OneDrive software.

Research data management policy

(260.9 KB .PDF)

Research data management policy companion guide

(131.3 KB .PDF)


(634.8 KB .PDF)