High-resolution X-ray diffraction datasets: Carbonates

被引:16
作者
Amao, Abduljamiu O. [1 ]
Al-Otaibi, Bandar [2 ]
Al-Ramadan, Khalid [3 ]
机构
[1] King Fahd Univ Petr & Minerals KFUPM, Coll Petr Engn & Geosci, Ctr Integrat Petr Res, Dhahran 31261, Saudi Arabia
[2] King Fahd Univ Petr & Minerals KFUPM, Coll Petr Engn & Geosci, Lab Support Serv, Dhahran 31261, Saudi Arabia
[3] King Fahd Univ Petr & Minerals KFUPM, Coll Petr Engn & Geosci, Geosci Dept, Dhahran 31261, Saudi Arabia
来源
DATA IN BRIEF | 2022年 / 42卷
关键词
X-ray diffraction; Carbonates; Spectra; Calcium carbonate rocks; Modelling;
D O I
10.1016/j.dib.2022.108204
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 sta-tistical models with sufficient data points. We currently use the dataset in our own projects to develop comprehensive carbonate library and felt compelled to share. (C) 2022 The Author(s). Published by Elsevier Inc.
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页数:6
相关论文
共 13 条
[1]  
[Anonymous], 2018, J FORAMINIFERAL RES
[2]   powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data [J].
Butler, Benjamin M. ;
Hillier, Stephen .
COMPUTERS & GEOSCIENCES, 2021, 147
[3]  
Henry L., 2020, PURRR FUNCTIONAL PRO
[4]  
Kourmelis N., 2013, POWDER DIFFR, V28, P137, DOI DOI 10.1017/S0885715613000390
[5]  
Kreutzer S, 2020, **DATA OBJECT**
[6]  
Kuhn M., 2021, **DATA OBJECT**
[7]  
Kuhn M., 2020, Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles
[8]   High-Magnesian Calcite Mesocrystals: A Coordination Chemistry Approach [J].
Lenders, Jos J. M. ;
Dey, Archan ;
Bomans, Paul H. H. ;
Spielmann, Jan ;
Hendrix, Marco M. R. M. ;
de With, Gijsbertus ;
Meldrum, Fiona C. ;
Harder, Sjoerd ;
Sommerdijk, Nico A. J. M. .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2012, 134 (02) :1367-1373
[9]  
Makowski D, 2021, CRAN
[10]  
McCauley J.W, 1983, MINERALOGY, P56, DOI [10.1007/0-387-30720-6_20, DOI 10.1007/0-387-30720-6_20]