Research Data
Research data may be described as "... data that is collected, observed, or created for analysis purposes to produce original research results."
Research data may be generated for different purposes and through different processes and may be divided into the following categories. Each category may require a different type of data management plan.
Observational
- captured in real-time
- usually irreplaceable
- examples include: sensor readings, survey results, telemetry, sample data, neurological images
Experimental
- data from lab equipment
- often reproducible (this can be expensive)
- examples include: gene sequences, magnetic field readings
Simulation
- data generated from test models
- models and metadata where the input is more important than the output data
- examples include: climate models and economic models.
Derived or compiled
- reproducible (expensive)
- examples include: text and data mining, 3D models
Reference or Canonical
- a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets
- most probably published or curated.
- examples include: gene sequence databanks, chemical structures, and spatial data portals.
These data can come in many forms: text, numerical, multimedia, models, software, discipline-specific (i.e., FITS in astronomy, CIF in chemistry), or instrument-specific.