Research data is any systematic collection of information that is used by researchers for analysis. Typical examples of data include:
Examples: Sensor data, telemetry, survey data, sample data, neuroimages
Examples: gene sequences, chromatograms, toroid magnetic field data
Examples: climate models, economic models
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.
(Retrieved from Kettering University Library)
Research data, unlike other types of information, "is collected, observed, or created, for purposes of analysis to produce original research results”.
(Retrieved from University of Edinburgh)
The following are tips to help you keep your data organized.
Use consistent file naming and appropriate descriptive text
|Survey_Answer_Percent||Survey Answer %||
Keep different versions and drafts series of a documents by adding _01 or V01 to the end or beginning of your files
Save your data in an "Open source", standard encoding formats to keep files accessible over time
Metadata (descriptive information) about your data to make it searchable, discoverable, identifiable and usable in the future. Describe project and data with descriptive, structural, technical, administrative data to define what is this data, how it was collected, who, how, when, where can find and use it: