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What is Research Data Management?

BacteriaResearch Data Management (RDM) is the sum of activities undertaken in relation to the collection, processing, preservation and sharing of research data throughout the research lifecycle. It encompasses activities carried out by both researchers and research organisations. Find out more about the Research Data Management lifecycle.

Research data, by being well managed, can generate benefits for both the University and its researchers in terms of greater research impact, enhanced reputation, and increased return on investment.

Many funders of research have policies requiring researchers in receipt of grant funding to plan for the preservation and sharing of key data beyond the lifetime of the research project. The first principle of the Common Principles on Data Policy states that:

Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner that does not harm intellectual property.

The University fully supports this principle and the policies of public funders of research. The University of Reading Research Data Management Policy (PDF) sets out the requirements that research staff and students must observe in the management, preservation and sharing of research data.

Find out more about the University's Research Data Management policy and research funders' policies.


What are research data?

Research data are the raw materials collected, processed and studied in the undertaking of research. They are the evidential basis that substantiates published research findings.

They may be primary data generated or collected by the researcher, or data collected from existing sources and processed as part of the research activity.

Data include the information and materials necessary to interpret or regenerate recorded outputs, such as experimental methods, algorithms and research software.

Data may exist in digital or non-digital formats, and may include, but are not limited to:

  • Results of experiments or simulations;
  • Statistics and measurements;
  • Computational models and software;
  • Observational data;
  • Survey results;
  • Interview recordings and transcripts, and coding applied to these;
  • Images from cameras and scientific equipment;
  • Databases compiled from secondary sources;
  • Collections of digital resources;
  • Textual or linguistic corpora;
  • Lab books;
  • Physical objects, such as samples and specimens.


What data are in scope?

Research Data Management requirements typically apply to primary data, i.e. new data collected or generated in the research activity.

Researchers are not expected to preserve and publish secondary data, i.e. data that already exist and are held under another authority, such as published literature or documentary materials held in archives, which are consulted for purposes of interpretation, criticism and review.

But where the systematic processing of secondary materials to generate new outputs is an aim of the research, there may be a requirement to preserve and share the derived data or outputs. Examples of new data derived from secondary sources might include the results of running computer simulation models using supplied observational data, or a digitised collection of archive materials that has been published online.

Find out more in the Research Data Explained module of the MANTRA training course from the University of Edinburgh.


Why manage your data?

Management of research data is integral to the research process. By actively managing your research data you will:

  • Safeguard the essential materials of your research against corruption and loss by storing and managing them securely;
  • Increase the efficiency of your research and minimise effort by collecting, documenting and organising data;
  • Ensure any legal obligations are met, such as those under the Data Protection Act;
  • Ensure that you can comply with any funder requirements for the retention and sharing of data;
  • Maintain the accuracy, reliability and integrity of your data;
  • Demonstrate transparency and accountability in your research practice by permitting others to consult your data and validate your findings;
  • Enhance the long-term value of your data, and increase the reach and impact of your research;
  • Undertake responsible stewardship of the intellectual property you generate for the University (as an employee), for others (if in partnership/under contract), or for yourself (if you are a student and have not assigned IP elsewhere). 



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