“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.
Several federal research agencies now require data management plans as part of their funding proposals. Researchers are increasingly expected to provide open access to publicly funded research as part of verifying and replicating research results. This workshop provides a high-level overview of the research data lifecycle, focusing on areas to consider in order to effectively and responsibly manage research data.
Participants will learn about the basic requirements of a data management plan and where to go for additional, customized help in data management planning. Additionally, time will be set aside in the workshop to discuss future topics for additional workshops focusing on data management.