Research Data Management

• Protect your data from loss by maintaining good backups and documentation

• Secure your data through effective management of sensitive data

• Conduct research efficiently by analyzing your data practices

• Simplify the use and reuse of your data through proper documentation and application of standards

• Increase your research visibility by publishing your datasets and documentation in

• Meet funding agency, legal and ethical requirements for dissemination and documentation of your research

• Preserve and provide access to your data in the long term, allowing future scholars to build on your work

Research data is any systematic collection of information that is used by researchers for analysis.  Typical examples of data include: 

• Observational data: data captured in real-time, usually irreplaceable
  Examples: Sensor data, telemetry, survey data, sample data, neuroimages

• Experimental data: data from lab equipment, often reproducible, but can be expensive
  Examples: gene sequences, chromatograms, toroid magnetic field data

• Simulation data: data generated from test models where model and metadata (inputs) are more important than output data. 
  Examples: climate models, economic models

• Derived or compiled data: data that is reproducible (but very expensive)
  Examples: text and data mining, compiled database, 3D models, data gathered from public documents
   

Research data can also include video, sound, or text data, as long as it is used for systematic analysis.  For example,  a collection of video interviews use to gather and identify gesture and facial expressions in a study of emotional responses to stimuli would be considered research data.

All research data must be appropriately structured and documented in order for it to be used effectively for analysis.  Additionally, any unique programs or models needed to analyze the data should also be preserved.