Digital Image Correlation (DIC) Strain Data of Minerals Under Temperature Evolution

This dataset includes Digital Image Correlation (DIC) strain data for four minerals: biotite, plagioclase, quartz, and amphibole, as they experience temperature variations. The DIC technique measures the strain distribution within these minerals, offering valuable insights into their mechanical behavior and deformation characteristics in response to thermal changes. The thermal strain of both mineral grains and their boundaries is derived by averaging the thermal strain of the micro-pillars. The mineral composition of each micro-pillar is detailed in the following table. Pillar No. Mineral type Grain boundry type Notes p1 Am, Bt Am-Bt p2 Am, Qz Am-Qz p3 Am, Qz Am-Qz p4 Qz, Bt Qz-Bt p5 Qz, Bt Qz-Bt p6 Pl, Qz Pl-Qz p7 Bt, Pl Bt-Pl p8 Bt, Pl Bt-Pl p9 Bt, Pl Bt-Pl p10 Pl, Qz Pl-Qz p11 Pl, Qz Pl-Qz p12 Pl, Qz Pl-Qz p13 Pl, Qz Pl-Qz p14 Pl, Qz Pl-Qz p15 Am, Pl Am-Pl p16 Bt, Qz Bt-Qz p17 Am, Pl Am-Pl p18 Am, Bt Am-Bt p19 Am, Bt Am-Bt p20 Am, Pl Am-Pl p21 Am, Pl Am-Pl p22 Am, Pl Am-Pl p23 Am, Pl Am-Pl p24 Bt, Am Bt-Am p25 Bt, Am Bt-Am p26 Am, Qz Am-Qz p27 Am, Qz Am-Qz p28 Am, Qz Am-Qz README: By importing SEM images of surface morphology changes with temperature (found in the zip file "micro-pillar-SEM-image.zip") into the VIC-2D software, we can determine the thermal strain distribution on the surface of the micro-pillars. The thermal strain distribution for each micro-pillar at the target temperature is quantified, allowing us to calculate statistics such as maximum, minimum, and average values (provided in the CSV files "p1.csv," "p2.csv," ..., "p28.csv"). Finally, by averaging the thermal strains of micro-pillars with the same mineral composition at the same target temperature, we derive the thermal strain as a function of temperature for each mineral type or grain boundary (as detailed in the Excel sheets, e.g., "Qz-thermal-strain-data.xlsx" and "GB-Pl-Am-thermal-strain-data.xlsx").