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Research Data Management

This guide provides best practices and resources for managing your research data for any discipline.

Data Types

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.