Identification of hexagonal boron nitride thickness by colourimetry and contrast

The datafiles contain the data used for Figures 2-6 of the paper "Identification of hexagonal boron nitride thickness by colorimetry and contrast" by E. Blundo et al.. A python code used for the analysis of the optical images is also available. The content of the files is described below: "RGBgrey_experimental_and_interpolated_intensity_data__70nm_150nm_280nm__Fig_2":Experimental RGB data and calculated luminance (grey-scale) data; interpolation of the RGB data and calculated grey data. The data for the different substrates are in separate sheets. "RGBgrey_experimental_and_interpolated_intensity_data__90nm_215nm_271nm_297nm__interpolation_of_data_from_literature": Analogous to the previous file but starting from the experimental data available in the literature. The data for the different substrates are in separate sheets. For every substrate, the source for the experimental data is described. It should be noticed that some experimental points were showing clear deviations from the behaviour described bu the other experimental points and were excluded during the interpolation process. "RGBgrey_contrast_and_derivative_data__All_substrates__Figs_3_and_4_and_5": Contrast data (C_X) and its derivative (D_X) for X = RGBL (where L corresponds to grey), calculated starting from the interpolated RGB/grey data contained in the previous two files. Every sheet contains a different C_X or D_X dataset. "RGBgrey_intensity_and_contrast_data__70nm__1-10_layers__Fig_6": RGB/grey intensities and C_X data for the 70 nm SiO2 substrate, for specific hBN thicknesses in the range from 0 to 10 layers. The data were obtained starting from the interpolated data contained in the previous files, for hBN thicknesses t = N*0.33 nm, with N = 0, 1, ..., 10. "average_color_py": Python code which can be used to calculate the average RGB values for a selected area in an image. More specifically, the program allows the user to open the image and manually draw a rectangle to select a region with a given colour; the program then extracts the RGB intensity values for all pixels in the selected region and calculates the average values.本数据集包含E. Blundo等人发表的论文《比色法与对比度法鉴定六方氮化硼厚度》中图2至图6所用的全部数据，同时附带用于光学图像分析的Python代码。 以下对各文件的内容进行说明： "RGBgrey_experimental_and_interpolated_intensity_data__70nm_150nm_280nm__Fig_2"：本文件包含实验RGB数据与计算得到的亮度（灰度）数据，以及RGB数据与灰度数据的插值结果。不同衬底的实验数据分别存储于独立工作表中。 "RGBgrey_experimental_and_interpolated_intensity_data__90nm_215nm_271nm_297nm__interpolation_of_data_from_literature"：本文件与前一文件结构类似，但所用实验数据取自已发表文献。不同衬底的数据分别存储于独立工作表中，且每个衬底的实验数据来源均有标注。需注意，部分实验点与其余实验点的变化趋势存在明显偏差，因此在插值过程中已将其剔除。 "RGBgrey_contrast_and_derivative_data__All_substrates__Figs_3_and_4_and_5"：本文件包含从上述两个文件的插值RGB/灰度数据计算得到的对比度数据（C_X）及其导数（D_X），其中X取值为RGBL（L代表灰度）。每个工作表分别存储一组不同的C_X或D_X数据集。 "RGBgrey_intensity_and_contrast_data__70nm__1-10_layers__Fig_6"：本文件包含70nm二氧化硅（SiO₂）衬底上、厚度范围为0至10层的特定六方氮化硼（hBN）的RGB/灰度强度数据与C_X对比度数据。该数据集基于前述文件的插值数据计算得到，其中六方氮化硼厚度t = N×0.33 nm，N取值为0、1……10。 "average_color_py"：本Python代码用于计算图像中选定区域的平均RGB数值。具体而言，程序支持用户打开图像并手动绘制矩形框以选定目标颜色区域，随后提取选定区域内所有像素的RGB强度值并计算其平均值。