## 2 Research Data Quality and its Evaluation through Measurement Uncertainty  
Standards, e.g., _International Guide to the Expression of Uncertainty in Measurement_[4] (or GUM as it is now often called) and its US equivalent ANSI/NCSL Z540-2-1997 _US Guide to the Expression of Uncertainty in Measurement_[5] request the provision of a quantitative indication of the quality of the measurement result along with the result itself, so those who use the measured data can assess its reliability. The GUM standard's approach groups the components of uncertainty in the result of a measurement into two categories according to the way in which their numerical value is estimated: those which are evaluated by statistical methods are classified as \"Type A\", and those which are evaluated by other means are classified as \"Type B\". A Type B evaluation of standard uncertainty is usually based on scientific judgment using all of the relevant information available, which may include:* previous measurement data;
* experience with, or general knowledge of, the behavior and property of relevant materials and instruments;
* manufacturer's specifications;
* data provided in calibration and other reports, and uncertainties assigned to reference data taken from handbooks. As real-time sensor platforms become the norm in environmental sensing, there is a need to develop automated procedures to incorporate scientific judgments of the streamed data in the evaluation of the measurement uncertainty and associated data quality. As the judgments are often based on experts' opinions and estimates, the data quality assurance systems could be designed within an expert system framework that uses fuzzy rules to characterize the properties and sources of the judgment. Fuzzy systems have been used in applications where the solution is highly dependent on human experience; because of either imprecise information being available or the empirical nature of the problem (e.g., [6], and references therein). Using a fuzzy system, it is possible to encode linguistic rules and heuristics, reducing the solution time since the expert's knowledge can be built in directly. In addition, its qualitative representation form makes fuzzy interpretations of data very natural and an intuitively plausible way to formulate and solve several problems. Qualitative aspects can be implemented and can also be updated making this system useful to solve problems that are very difficult or impossible to solve analytically. Fuzzy heuristics can be applied to model a measurement result and its uncertainty [7, 8], classifying the existing models of measurement uncertainty into the following groups:  
1. Statistical model (standard model of uncertainty): the measuring function \(f\) is a random function and the measurement results _Xi_ are real numbers. NB In this model the type B component of inexactness may not be well represented. 2. Fuzzy set model: the measuring function is fuzzy, and the space Y of measured results are the fuzzy intervals _J_Y characterized by the membership function \(\mu(x)=\mu_{\text{J(x)}}\left(\text{x}\right)\). In this model: 1. The result of a single measurement is a fuzzy interval; the uncertainty of this measurement is described by a membership function; 2. The mathematical operations performed on the measurement results are operations on fuzzy intervals. Arithmetic operations on fuzzy intervals could be defined in a variety of different ways. 3. Random-fuzzy model: the measuring function \(f\) is a random function, and the space Y of measurement results is the set of fuzzy intervals \(J\). In this model, the results of measurements are random fuzzy intervals. It is possible to define the extended uncertainty for a given significance level using the fuzzy \(\alpha\)-cut technique [9]. Over the last few years, new applications demonstrating more complicated schemes for using fuzzy formalized expert information in measurement procedures have been published. [PERSON] _et al_.