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Detailed methods and collected data from a geochronological investigation into pegmatites of the Bynoe Pegmatite Field.

All data used to generate tables and figures in the main paper and in the supplementary.

The SMCR-LAPER dataset is data that can be used openly, especially for researchers in the field of Physics education. The SMCR-LAPER dataset is data collected using the RBAs framework, obtained by researchers through observing students majoring in Physics over 1 semester of lectures. Observations were made using several instruments, including the Force Concept Inventory (FCI), Mechanics Baseline Test (MBT), Force and Motion Conceptual Evaluation (FMCE), Energy and Momentum Conceptual Survey (EMCS), Rotational and Rolling Motion Conceptual Survey (RRMCS), Flight Physics Concept Inventory (FliP-CoIn), and Colorado Learning Attitudes about Science Survey (CLASS). The SMCR-LAPER dataset can be downloaded in the form of a file in .xlsx and .csv format, consisting of 3 sheets. Sheet 1: Raw_data; Sheet 2: Description; and Sheet 3: Codes.

The surface area-to-volume (S/V) ratio refers to the area of the air–liquid interface per unit volume of culture medium under static conditions. Optimizing the S/V ratio can reduce media consumption. However, past studies have provided limited data on its influence on bacterial cellulose (BC) fermentation. Therefore, this study investigates a broader and previously unexplored S/V ratio range (0.09 cm⁻¹ to 3.69 cm⁻¹) in static-batch fermentation using Komagataeibacter xylinus [formerly Acetobacter xylinum] 0416. A 7-day fermentation was conducted in vertical cylindrical glass fermenters, with different S/V ratios achieved by adjusting the volume of Hestrin–Schramm (HS) medium. BC dry mass and yield increased with the S/V ratio up to 2.46 cm⁻¹, but no further improvement thereafter. A significant decrease in media pH was observed when high BC production occurred. Higher tensile stress and strain were observed between S/V ratios of 0.50 cm⁻¹ and 1.50 cm⁻¹, indicating improved mechanical strength of BC. The optimal S/V ratio of 1.48 cm⁻¹ produced the highest BC dry mass (73 ± 4 mg) and a high yield (1.46 ± 0.09 g/L). The highest tensile stress (100 ± 70 MPa), tensile strain (3.2 ± 1.4 %), and water holding capacity (WHC; 18 ± 4 g/g) were observed at S/V ratios of 1.23, 1.48, and 0.09 cm⁻¹, respectively. Since the S/V ratio significantly influences BC yield and properties, it should be considered in static-batch fermentations to achieve a higher productivity and comply with the material requirements in scaled-up productions and industrial applications.

Supporting information for Single-cell lipidomic analysis of the Epithelial-Mesenchymal Transition (EMT) using Mass Spectrometry Imaging by Quinsgaard et al. Published in iScience 2026, available at doi: 10.1016/j.isci.2026.115847

The dataset, comprising both raw and some processed data, used to support the findings of this study are contained in structured folder for easy understanding.

Table S1 Major and trace element concentrations of olivines in CE-5 basalt clasts. Table S2 Major and trace element concentrations of pyroxenes in CE-5 basalt clasts. Table S3 Analyzing results of monitor standards in olivine analysis. Table S4 Analyzing results of monitor standards in pyroxene analysis. Table S5 Calculation of the Lu/Hf and Sm/Nd of the CE-5 basalt mantle source. Table S6 The LMO crystallization modelling details. Table S7 The mixing, non-modal melting and crystallization modelling of the potential CE-5 basalt mantle sources that involve 99.5 PCS cumulate. Table S8 Initial conditions for the phase equilibrium simulation. Table S9 Partial melting modelling of the Model 5 mantle source at 4 GPa Table S10 Calculation details of the melt compositions in figure S15b. Table "Major elements modelling": Fractional crystallization modelling output of petrolog_v3.1.1.3 software.

Full WB images (Some membranes were cut post-transfer to reveal different proteins with different molecular weight).

Key Data on HLP-NPs

This project involves methods for extracting and identifying specific colors using artificial neural network techniques and the HSV color space.