“When we have all data online it will be great for humanity. It is a prerequisite to solving many problems that humankind faces.” – Robert Cailliau, Belgian informatics engineer and computer scientist who, together with Tim Berners-Lee, developed the Worl
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.
GUIDE TO RESEARCH DATA MANAGEMENT
This guide presents information on the effective management of data created through research — including creating a data management plan for grant or project proposals, preserving data after project completion and sharing data with other researchers.
Why Manage Research Data
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