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High-resolution X–ray diffraction datasets: Carbonates

https://doi.org/10.1016/j.dib.2022.108204

“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.”

Fig. 1 below is a sample spectrum from the dataset depicting a typical spectra file.

Fig 1

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Fig. 1. The scan example from a sample “Carbonates_1550” selected from the dataset

Fig. 2 Result of Uniform Manifold Approximation and Projection (UMAP) and hierarchical clustering (HCPC) showing the variability among the carbonates spectra files.

Fig 2

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Fig. 2. Visualising the differences among the shared files

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