Research Data Management

There are many ways to classify data. Two of the more common are:

• Primary and Secondary: Primary data is data that you collect or generate.  Secondary data is created by other researchers, and could be their primary data, or the data resulting from their research.
• Qualitative and Quantitative: Qualitative refers to text, images, video, sound recordings, observations, etc.  Quantitative refers to numerical data.  

Data usually fall into one of five categories:

Observational

• Captured in real-time
• Cannot be reproduced or recaptured. Sometimes called 'unique data'.
• Example include sensor data, human observation, and survey results

Experimental

• Data from lab equipment and under controlled conditions
• Usually reproducible, but expensive to do so
• Examples include gene sequences, chromatograms, spectroscopy.

Simulation

• Data generated from test models studying actual or theoretical systems
• Models and metadata where the input may be of greater importance than the output
• Examples include climate models, economic models, systems engineering.

Derived or compiled

• The results of data analysis, or aggregated from multiple sources
• Reproducible, but very expensive
• Examples include text and data mining, compiled databases, 3D models.

Reference or canonical

• Fixed or organic collection datasets, usually peer-reviewed, and often published and curated.
• Examples include gene sequence databanks, census data, chemical structures.

Data come in many forms.  Common ones are text, numeric, audio, models, code, instrument, images, and video.