Supplementary Data for "A novel experimental V-Sc olivine-melt oxybarometer for arc magmas"
Description
This supplementary dataset contains all supporting materials associated with the manuscript, including analytical data, figures, quality assessments, and Python code used for data analysis and modeling. It also serves as the data repository for all tables and figures presented in the main manuscript. The contents are organized as follows: 1. Cacciatore_et_al_GCA_Raw_data_repository.xlsx: This file contains the same data as presented in the tables of the main manuscript. 2. S1_Supplementary_Tables: Excel spreadsheets providing a comprehensive summary of the experimental run conditions and trace element doping information. This section includes complete, processed data from LA-ICP-MS and EPMA analyses, along with quality control metrics for the measurements. It also contains the inputs and results of mass balance calculations, as well as comparisons with QEMSCAN analyses for selected samples. Additionally, this section includes numerical datasets used in statistical modeling (e.g., correlation matrices, fitting statistics), coding inputs, and the full dataset underlying the tables presented in the main manuscript. 3. S2_Supplement_Quality_of_Experimental_Approach: A document providing detailed information on the quality and reproducibility of the experimental procedures used in the study. 4. S3_Supplementary_Figures: Figures illustrating the precision and accuracy of EPMA and LA-ICP-MS measurements, along with graphical outputs of the statistical modeling. This section includes figures referenced in both the main manuscript and supplementary material. 5. S4_Supplement_AI_Generated_Python_Codes: Python scripts (AI-generated) used for data processing and statistical analysis, which produced the regression models presented as Equations 5 and 6 in the main manuscript.
Files
Steps to reproduce
Data Collection and Methods Overview The data presented in this study were generated through a series of carefully designed experimental and analytical procedures, as outlined below to facilitate reproducibility and transparency: 1. Experimental Setup: • Experiments were conducted using a new prototype high-pressure vessel equipped with a custom-designed hydrogen membrane, enabling flexible, precise, and accurate control of oxygen fugacity (fO₂). • Fractional crystallization series were performed on starting compositions representative of natural arc magmas under water-saturated conditions and 200 MPa. 2. Analytical Techniques: • SEM (Scanning Electron Microscopy) Imaging: Conducted to obtain textural overviews and characterize the microstructure of experimental run products. • EPMA (Electron Probe Microanalysis): Employed to determine major and minor element compositions and validate phase compositions. • LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometry): Used to measure trace element concentrations in run product phases of experimental samples. • QEMSCAN: Applied for detailed quantitative mineralogical analyses on selected samples. • Raman Spectroscopy: Utilized to measure water content in silicate glass, with spectral data processed using the SilicH2O software for quantitative H₂O analysis. 3. Data Processing and Quality Control: • Raw data from LA-ICP-MS and EPMA were processed using standard calibration protocols and internal reference materials and standards to ensure accuracy and precision. • Quality control measures included repeated analyses of standards/reference materials and replicate measurements to assess precision and reproducibility. 4. Statistical Modeling and Analysis: • Processed datasets were subjected to statistical analysis including correlation matrices and fitting procedures to model partitioning behavior. • AI-generated Python scripts were developed to automate data processing and generate regression models presented in the manuscript (Equations 5 and 6). 5. Software and Workflow: • Data analysis and visualization were performed using Lab Origin, R, and Python, utilizing libraries such as NumPy, pandas, and matplotlib. • The Python code used for data processing and statistical analysis is provided in the supplementary materials to facilitate reproducibility.
Institutions
- Universite de Geneve departement des sciences de la Terre