Research Data Management


Demonstrable good practice in Research Data Management and a commitment to data sharing are increasingly required by UK research councils and other funding bodies. This reflects the increased interest in data as a valuable research output in its own right. Brunel and its researchers therefore have obligations to manage, preserve and provide access to its research data. 


Brunel Figshare ( is a Data Repository where Brunel’s research staff can deposit their digital Research Data, to make it available in a citable, shareable and discoverable manner. 

A user guide to the different features offered by Brunel figshare can be accessed here.


Brunel University London’s Research Data Management Policy

Brunel views effective research data management as an essential ingredient of a quality research culture.  To support this, a new Research Data Management (RDM) policy for the University was approved by Senate in June 2014 which builds on the previous statement for managing research data approved in November 2011. Under the policy, all researchers, whether they are funded or not, are expected to prepare a research data management plan and to manage and publish research data appropriately.


What do we mean by research data?

Research data and its management applies across all disciplines, irrespective of what data may refer to in each subject area. Research data has been defined as the “evidence base on which academic researchers build their analytic or other work” (HEFCE, 2008) or as “the recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs“ (University of Oxford).

There is however no clear consensus on a definition because research data means different things to different people in different contexts and the definition varies depending on your subject discipline and research funder.

This means that research data can be qualitative, textual, numerical, quantitative, preliminary, final, physical, digital or print. Examples of research data include, but are not limited to – 

1.Results of experiments

2.Statistics and measurements

3.Simulations, models, algorithms and software

4.Collections of digital resources

5.Observations and fieldwork

6.Interview recording and transcripts, and coding applied to these

7.Survey results

8.Databases compiled from secondary sources


10.Textual source materials and annotations

11.Physical artefacts, samples and specimens

12.Lab books 

Research progress reports for funders, email or file backups and data produced by non-research activities such as project administration and teaching are not considered to be Research Data.  

A useful and concise online module about Research Data, by the University of Edinburgh, is available here.

It is important to note that not all research data used or generated in a research project needs to be shared. Only data identified as having long term value and data underpinning publications needs to be made open access. See our page on selecting data to preserve for guidance.

What is research data management?

Research data management (RDM) is a broad term covering how you organize, structure, store, preserve and share for the data used or generated during a research project. There are key points when RDM is particularly important such as during the proposal stage, when you start your research, when you publish your findings and as you approach the end of a project. The University can support you in this process and the following web pages provide guidance on each stage of the research data lifecycle.


The research data lifecycle:


PLAN - In the grant application stage you should plan how you are going to manage your data throughout the project. The University and most funders now expect you to create (and submit in most cases) a formal document called a data management plan.


ORGANISE – During project start-up, you should establish processes and procedures for managing your data. Learn more about:


  • what data storage options are available to you; and
  • how you might back up and encrypt data where necessary


PRESERVE AND SHARE– Thinking about how and where you will preserve your data long term will ensure that your research outputs remain usable, understandable, accessible and sharable well after the end of your project. See our guidance on:

What are the benefits of good research data management?

For researchers:
  • Enhances the visibility of your research data and increases the number of citations
  • Ensures compliance with both funders’ and institutional research data expectations and policies
  • Reduces the risk of data loss by keeping your research data safe and secure
  • Improved research integrity and validation of research results
  • Facilitates the sharing and re-use of research data for future research
  • Provides opportunities for collaboration with other researchers within your discipline, or even with other disciplines
For institutions:
  • Ensures compliance with the research data expectations of all the funding bodies
  • Showcases research data outputs to a global audience
  • Attracts new collaborators and research partners
  • Strengthening of the research environment and infrastructure
For external stakeholders:
  • Improved research data workflows and data availability and discovery
  • Visibility of research outputs from publicly funded research
  • Enhances citizen science and public engagement activities
  • Improved research integrity and validation of research results

Page last updated: Monday 11 July 2016