What is research data?
That which is collected, observed, or created for purposes of analysing to produce original research results.
- Definition from The University of Nottingham Research Data Management policy
Good practice in research data management is important for several reasons:
- it is beneficial to researchers and institutions, ensuring both are able to meet the expectations of the research funders.
- it can improve research efficiency and facilitates the availability of research for sharing, validation and re-use.
Classification of research data
The Massachusetts Institute of Technology (MIT) has classified data into four types:
- observational: data captured in real-time, usually irreplaceable, examples include neuroimages, survey data, sensor data, and sample data.
- experimental: data from laboratory equipment, often reproducible, examples include gene sequences, and chromatograms.
- simulation: data generated from test models where model and metadata (inputs) are more important than output data, examples include climate models and economic models.
- derived or compiled: data that is reproducible, examples includes compiled database, 3D models, text and data mining.
Research data types
Research data types may include all of the following:
- video recordings
- log books
- test responses
- slides, artefacts, specimens, samples
- audiotapes, photographs, films
- models, algorithms, scripts
- questionnaires, transcripts, codebooks
- methodologies and workflows
- standard operating procedures and protocols