X–ray diffraction (XRD) analysis is a versatile and reliable method used in the identification of minerals in solid samples. It is one of the primary techniques geoscientists, mineralogist, solid-state chemists depend on to characterize the composition of unknown samples. In recent years there has been a growing interest among researchers to have readily accessible and large dataset to use to calibrate their experiment or to simply build various statistical models. Sadly, this is difficult to come by. Most well-curated datasets are propriety in nature and often too expensive for the average researcher. Additionally, when these datasets are available, they might not be suitable for purpose due to lack of proper coverage for certain a mineral of interest. For these reasons, we have carefully selected and curated samples rich in calcium carbonate that will be useful for various applications. Our dataset includes 1680 X-ray diffraction scans of samples collected from carbonate rich rock formations outcrops in Spain, Italy, and Saudi Arabia. They represent materials with total carbonate concentration range between 30-99%. The spectra were acquired on a Malvern PANalytical EMPYREAN Diffractometer system at two theta range 2- 70 and 0.01 step size. This dataset will be valuable to geoscientists, mineralogist, solid-state chemists, data scientists alike looking to design experiments, build mineralogical reference databases or statistical models with sufficient data points. We currently use the dataset in our own projects to develop comprehensive carbonate library and felt compelled to share.

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.