The University of Edinburgh, Research Data Management website provides some very useful information on Research data:Classification of research data
Research data can be generated for different purposes and through different processes (Research Information Network classification):
- Observational: data captured in real-time, usually irreplaceable. For example, sensor data, survey data, sample data, neuroimages.
- Experimental: ldata from lab equipment, often reproducible, but can be expensive. For example, gene sequences, chromatograms, toroid magnetic field data.
- Simulation: data generated from test models where model and metadata are more important than output data. For example, climate models, economic models.
- Derived or compiled: data is reproducible but expensive. For example, text and data mining, compiled database, 3D models.
- Reference or canonical: a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets, most probably published and curated. For example, gene sequence databanks, chemical structures, or spatial data portals.
Research data (traditional and electronic research) may include all of the following:
- Documents (text, Word), spreadsheets
- Laboratory notebooks, field notebooks, diaries
- Questionnaires, transcripts, codebooks
- Audiotapes, videotapes
- Photographs, films
- Test responses
- Slides, artefacts, specimens, samples
- Collection of digital objects acquired and generated during the process of research
- Data files
- Database contents (video, audio, text, images)
- Models, algorithms, scripts
- Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
- Methodologies and workflows
- Standard operating procedures and protocols