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Research Data Management: Reusing Existing Data

Bringing together University resources and services to facilitate researchers in the production of high quality data.

At a Glance

Reusing existing data can be cost effective & time saving but may require significant effort to get to know the data.

Help@UCD: ISSDA Reusing Research Data

Image of Finding & Reusing Research Data flyer

Help@UCD: ISSDA Data Sources


Reusing Data Pros & Cons


  • Datasets may impossible to create within the scope of your research project
  • I can be cost effective & time saving to use data which has already been collected
  • Ethical issues about data collection have already been dealt with
  • You can spend bulk of time analysing data
  • There is a huge breadth of data available, even in an Irish context



The main disadvantage to reusing existing data is that the data were not collected to answer your specific research questions.

  • Particular information may not have been collected
  • The data may refer to a different geographic region than you are interested in studying
  • The data may refer to a different time period than you are interested in studying
  • Variables may have been defined or categorised differently than you would have liked
  • You were not directly involved in the data collection process
  • There may have bee a low response rate
  • Anonymisation may be quite extensive, so variables you are interested in may not be available

Reusing Existing Research Data

It is essential to:

  • Check out the documentation for collection procedures, data cleaning procedures and other technical information.
  • Spend time getting to know and understanding the data.
  • Be practical about whether data are suitable (good enough) for your research.


Documentation helps you to understand the meaning of the data & to evaluate suitability for your research question. It can help you understand exactly what information was collected, from whom, where & when, as well as what was done to the resulting data before it was archived.

Documentation can include:

  • Study description (metadata)
  • User guide
  • Codebook or data dictionary
  • Survey questions
  • Official reports