Data for: Four-Dimensional Perspectives on Crystal Cargoes Reveal Mafic Rejuvenation of Mantle Mushes and Antecryst Recycling Beneath Cumbre Vieja

Description

Numerical data associated with the study “Four-Dimensional Perspectives on Crystal Cargoes Reveal Mafic Rejuvenation of Mantle Mushes and Antecryst Recycling Beneath Cumbre Vieja,” accepted for publication in Geochimica et Cosmochimica Acta. The dataset contains sample and analytical metadata, WDS calibration and standards, reduced clinopyroxene, olivine, and glass EPMA data, clinopyroxene–melt equilibrium tests and thermobarometric calculations, olivine diffusion-chronometry results, and antecryst-subtraction calculations. Text-based versions of the supplementary data tables are provided together with the original supplementary Excel workbook for convenience.

Steps to reproduce

1) Use T1–T2 for sample metadata, analytical coverage, WDS calibration, and standards, and T3–T5 for the reduced clinopyroxene, olivine, and glass EPMA datasets. Analytical procedures, data screening, crystal-selection criteria, and compositional definitions are described in Sections 2.1–2.3 of the associated manuscript. 2) Reproduce the clinopyroxene–melt equilibrium tests using the paired mineral and melt compositions and component-based criteria reported in T6. Clinopyroxene–melt pressure and temperature estimates can then be recalculated from the accepted pairs in T7 using Putirka (2008) Eq. 30 for pressure and Eq. 33 for temperature, with the H₂O contents, Na₂O uncertainty, and pressure–depth conversion described in Section 2.5. 3) Reproduce the olivine diffusivity uncertainty calculations using the supplied Python script Cortese et al 2026-Diffusivity Monte Carlo_Dohmen & Chakraborty2007.pyw. The script implements Fe–Mg diffusivity following Dohmen & Chakraborty (2007), samples temperature, pressure, and oxygen fugacity, applies the crystallographic orientation correction, and calculates effective diffusivity distributions for each modeled compositional boundary. Set the sample-specific input files, parameter ranges, sampling distributions, and crystallographic orientations according to Section 2.6 and Supplementary Data T8. The Monte Carlo sample size is controlled by n in the script. 4) The Monte Carlo calculation is stochastic. With random_seed = None, independent runs will contain different individual random draws but should reproduce the reported diffusivity distributions and summary statistics when the same inputs and sufficiently large sample sizes are used. Setting random_seed to an integer generates an exactly repeatable realization. 5) Diffusion-profile best-fit times and their uncertainty summaries are reported in T8. Best-fit profiles are calculated using the median effective diffusivity, and timescale uncertainty is obtained by rescaling the reference fit across the Monte Carlo diffusivity distribution as described in Section 2.6. 6) Reproduce the antecryst-subtraction calculations using the phase proportions, antecrystic volume fractions, mineral compositions, and bulk-rock inputs in T9 together with the mass-balance equation described in Section 4.3. Corrected compositions are normalized after subtraction. Third-party datasets used for comparison are identified and cited in the manuscript and supplementary material and should be obtained from their original sources. The original supplementary Excel workbook is included for convenience in addition to the text-based numerical files.

Institutions

• The Graduate Center, CUNY

  New York, New York
• Queens College, CUNY

  New York, New York
• Instituto Tecnológico y de Energías Renovables

  Canary Islands, Tenerife

Categories

Licence