Computationally intensive, large-scale, networked and collaborative forms of research and scholarship across all disciplines, including all of the natural and physical sciences, related applied and technological disciplines, biomedicine, social science and the digital humanities.
- CASRAI Dictionary
Environmental research data
Individual items or records (both digital and analogue) usually obtained by measurement, observation or modelling of the natural world and the impact of humans upon it, including all necessary calibration and quality control. This includes data generated through complex systems, such as information retrieval algorithms, data assimilation techniques and the application of numerical models. However, it does not include the models themselves.
- NERC Data Policy
Examples of research data:
Error is the difference between the measured value and the ‘true value’ (NPL, 1999). Errors can come from the measuring device itself, including bias, changes due to wear, instrument drift, electrical noise and device resolution. Other errors can be introduced by difficulties in performing the measurement and by operator skill. To avoid sampling error, sufficiently dense measurements in space and time should take place to make sure that full variability is captured e.g. diurnal cycles, variations across a site.
Errors can be random or systematic (NPL, 1999). With random errors, each measurement gives a different result, so the more measurements (of the same thing) the better the estimate and the more certain the measurement becomes. Systematic errors arise from a bias, e.g., a stretched tape measure, and more measurements do not produce a better estimate of the ‘true value’.
Experimental research data
Research data from experimental results, often reproducible, but can be expensive.
Examples: data from lab equipment, metagenomic sequences recovered from soil samples, results of a field experiment.