Skip to main content

Research Data Management: What are Research Data?

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

Research Data Classification

Research data may be created by an individual researcher, created collaboratively or contributed by someone else during the course of a research project (for example, a public contribution to an online survey). The data may have been created ‘from scratch’ by research efforts or it may be existing data which has been transformed, adjusted or reinterpreted. The following classification of data was originally compiled by the Research Information Network and highlights the wide range of types that can exist:

  • Observational: data captured in real time that is usually unique and irreplaceable. For example, remote sensing data, survey data, field recordings, sample data
  • Experimental: data captured from lab equipment that is often reproducible, but can be expensive.  For example, gene sequences, chromatograms, magnetic field data
  • Models or simulations: data generated from test models where the model and metadata may be more important than output data from the model. For example, climate models, economic models
  • Derived or compiled: resulting from processing or combining ‘raw’ data, often reproducible, but may be expensive. For example, text and data mining, compiled databases, 3D models
  • Reference or canonical: a static or organic conglomeration or collection of datasets, probably published and curated. For example, gene sequence databanks, collection of letters or archive of historical images


Research data is often thought of in fairly narrow terms, such as the results of experiments or a database of statistics. These are relevant and important kinds of research data though the term can also be used in a far broader sense to cover structured and unstructured material in a wide variety of formats (text, numerical, multimedia, software, etc.) This may include any of the following objects:

  • Documents (text, MS Word), spreadsheets
  • Scanned laboratory notebooks, field notebooks, diaries

  • Online questionnaires, transcripts, surveys or codebooks
  • Digital audiotapes, videotapes and other digital recording media
  • Scanned photographs or films
  • Transcribed test responses
  • Database contents (video, audio, text, images)
  • Digital models, algorithms, scripts
  • Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
  • Documented methodologies and workflows
  • Records of standard operating procedures and protocols