Defining research data

Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results.

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 comes in many varied formats:

Text - flat text files, Word, Portable Document Format (PDF), Rich Text Format (RTF), Extensible Markup Languague (XML).
Numerical - Statistical Package for the Social Sciences (SPSS), Stata, Excel.
Multimedia - jpeg, tiff, dicom, mpeg, quicktime.
Models - 3D, statistical.
Software - Java, C.
Discipline specific - Flexible Image Transport System (FITS) in astronomy, Crystallographic Information File (CIF) in chemistry.
Instrument specific - Olympus Confocal Microscope Data Format, Carl Zeiss Digital Microscopic Image Format (ZVI).

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

The following research records may also be important to manage during and beyond the life of a project: