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This dataset provides rheometric data of the PDMS Korasilon G 20 OH used for analogue modelling at the Laboratory for Experimental Tectonics at GFZ Helmholtz Centre for Geosciences, Potsdam, Germany. The batch number is 1000039264, purchased in 2022 and opened in 2026. The material sample has been analyzed at the Laboratory for Experimental Tectonics at GFZ Helmholtz Centre for Geosciences, Potsdam (HelTec) using an Anton Paar Physica MCR 301 rheometer in a cone-plate configuration at room temperature (21˚C). Rotational (controlled shear rate) tests with shear rates varying from 10^-4 to 10^-1 s^-1 were performed. According to our rheometric analysis, the material is quasi-Newtonian (n~1) at strain rates below 10^-2 s^-1 and weakly shear rate thinning above. The viscosity of G 20 OH is 1.8*10^4 Pa s.
The Morro São João intrusion is located in the easternmost part of the Serra do Mar province, along the Cabo Frio lineament (Fig. 1) and has an area of approximately 10 km². It is a Late Cretaceous intrusion formed by clinopyroxenites, melagabbros, shonkinites, malignites, nepheline syenites, and phonolite dikes, without olivine, and is thought to have formed by closed system crystallization of a fairly evolved tephritic melt of potassic/ultrapotassic affinity (cf. Brotzu et al., 2007). We have analyzed two malignites, and specifically, their liquidus phases (clinopyroxene, titanite, garnet, amphibole). Analyzing the trace elements in these minerals helps us to better understand the different fractionation of the elements in these coexisting phases, and the implications for the evolution processes that occurred in the Morro São João magma reservoir. These analyses also provided important information about the concentration of rare earth elements (REEs) and high field strength elements (HFSEs), and their change with the magmatic evolution of the suite. This publication results from work conducted under the transnational access/national open access action at Mass spectrometry la-icp laboratory (IGG-CNR, Italy) supported by WP3 ILGE - MEET project, PNRR - EU Next Generation Europe program, MUR grant number D53C22001400005.
This data set consists of Horizontal-to-Vertical Spectral Ratios (HVSR) resulting from the application of the software package HVNEA (HV Noise and Earthquake Automatic Analysis) with the aim of comparing them with those resulting from the application of another method, namely STATION (Seismic sTATion and sIte amplificatiON). The results, relative to more than 24,000 HVSR, derive from the processing of 700,000 seismograms recorded over different time periods by 8 stations of the networks IV (Italian Seismic Network), GU (Regional Seismic Network of North Western Italy) and GV (Mobile RSNI). To compare the results of the two methods as accurately as possible, the waveforms were subjected to the same preprocessing already used to elaborate the results stored in the STATION database. To this end, the methodological workflow applied with HVNEA for station IV.MURB involved the selection of segments from continuous recordings for each event reported in the INGV catalogue located within a radius of 120 kilometres from the station. Starting from the automatically picked S-wave onsets, 12-second windows were then extracted and used for the analysis of earthquake recordings. Regarding the noise analysis, it should be noted that STATION again considers 12-second windows selected before the P-wave onset, while HVNEA requires the use of a signal window of at least 60 seconds. A window of 3,600 seconds was used for the analysis. The comparison of the HVSR was performed in the frequency band 0.1–15 Hz. All analysed curves, for both earthquake and noise recordings, show generally similar shapes and identify significant peaks in correspondence of the same frequency ranges, although the amplitudes obtained with STATION are systematically higher than those obtained with HVNEA. To obtain a quantitative comparison, various statistical metrics commonly used to measure the discrepancy between data sets were applied, namely the Mean Squared Error, the Mean Absolute Error and the Pearson Correlation Coefficient. This publication results from work conducted under the transnational access/national open access action at the Site effects Laboratory – INGV L’Aquila supported by WP3 ILGE–MEET project, PNRR–EU Next Generation Europe program, MUR grant number D53C22001400005.
The data set includes the 3D incremental displacement fields resulting from Digital Image Correlation (DIC) analysis of four strike-slip experiments performed at the laboratory for experimental tectonics at GFZ Helmholtz Centre for Geosciences in Potsdam in 2022. The data here include the incremental displacement time series from four strike-slip experiments with quartz sand G12 (Rosenau et al., 2018): two with 1.2 cm thick sandpack and two with a 3 cm thick sandpack. Ramos Sánchez et al. (in review) used the incremental horizontal displacement fields from strike-slip fault experiments within different materials to train Convolutional Neural Networks (CNN) to predict off-fault deformation from active fault trace maps. Because off-fault deformation is difficult to ascertain in the field, convolutional neural networks trained on data from scaled physical experiment that simulate upper crustal deformation can inform how much off-fault deformation can be expected along crustal strike-slip faults. For training the CNNs Ramos Sánchez et al. (in review) used incremental horizontal displacement fields from experiments in wet kaolin (Cooke et al., 2021) and both poured and sedimented CV32 sand (Visage et al., 2023). All experiments used identical conditions of straight basal velocity discontinuity to produce overlying strike-slip faults. The benefit of training the CNN on strike-slip experiments within different materials is to capture a wide range of strike-slip deformation that may occur within the upper crust. After training of the CNN Ramos Sánchez et al. (in review) tested the trained on unseen fault maps including maps from the two 3 cm thick G12 sand experiments of this dataset. The 1.2 cm sandpack maps were excluded from the study because the strike-slip faults were very fine and closely spaced so they were not as well resolved as those of 3 cm sandpack. By testing the CNN trained on CV32 fault maps with the fault maps from G12 sand experiments, Ramos Sanchez et al. were able to assess if the applicability of the CNN to experiments with similar but not identical sand. Information on the displacement field analysis and CNN training and testing can be found in the main text and supplement to Ramos Sánchez et al. (in review). The file structure of this zip folder is fully described in the list of files.
In this work we aimed to investigate and quantify the relative importance of dynamic conditions (e.g., stirring in a Concentric Cylinder apparatus) on the crystallization kinetics of basaltic magmas (Stromboli). This was achieved by observing the final textures of the samples, analysing the resulting SEM images, and finally relating the resulting parameters to specific growth and nucleation rates. The dataset is made of: 1) A folder called "SEM Images" with two sub-folders inside, representative of the two experiments carried out, called "CG1" and "CG2.3" 2) An Excel file, consisting in 4 data sheets, where all results of image analysis are included, divided in sections. The sheets are also available in CSV format. This publication results from work conducted under the transnational access/national open access action at High Pressure - High Temperature Laboratory (HPHT Lab), Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy supported by WP3 ILGE - MEET project, PNRR - EU Next Generation Europe program, MUR grant number D53C22001400005.
The Limeira I kimberlite (91±6 Ma; Guarino et al., 2013) is part of the Alto Paranaíba Igneous Province (APIP) and was emplaced in the southern part of the São Francisco Craton in Brazil. This Kimberlite contains macrocrysts and phenocrysts of olivine, resorbed phlogopite/ tetraferriphlogopite, Al-free magnetite, chromite, magnesian ilmenite, rutile, perovskite, monticellite, apatite, serpentine and carbonate. It also contains a suite of xenocrysts and xenoliths (among which we recall wehrlite, phlogopite-ilmenite-websterite, olivine-ilmenite-glimmerite, clinopyroxenites bearing potassic-richterite, chromite-monticellite-kalsilite xenoliths, rutile with priderite or perovskite reaction rims, magnesian chromian ilmenite with perovskite rims). In this part of the project, we analyzed the xenocryst minerals and the main minerals found in the xenoliths entrapped in the Limeira I kimberlite. Analyzing the trace element concentrations in these minerals, helped us to better understand the processes that may occur in the subcontinental lithospheric mantle beneath the Alto Paranaíba Igneous Province. These analyses also provided important information about the minerals' enrichment in rare earth elements (REEs) and high field strength elements (HFSEs). This publication results from work conducted under the transnational access/national open access action at Mass spectrometry la-icp laboratory (IGG-CNR, Italy) supported by WP3 ILGE - MEET project, PNRR - EU Next Generation Europe program, MUR grant number D53C22001400005.
This dataset provides friction data from ring-shear tests on quartz sand SIBELCO S80 used in analogue modelling of tectonic processes as a rock analogue for the earth’s upper crust (e.g., Klinkmüller et al., 2016). According to our analysis the material shows a Mohr-Coulomb behaviour characterized by a linear failure envelope. Peak, dynamic and reactivation friction coefficients of quartz sand S80 are µP = 0.75, µD = 0.59, and µR = 0.69, respectively (Table 5). Cohesion of the material ranges between 0-80 Pa. The material shows no rate-dependency (<1% per ten-fold change in shear velocity v). The tested bulk material consists of quartz sand SIBELCO S80 with grain size of ~0.63-355 µm (D50 = 175 µm. Bulk and grain densities are 1300 kg/m³ and 2650 kg/m³, respectively and the hardness is 7 on Moh’s scale. S80 is sold e.g., by the company SIBELCO (sibelco.com).
Kamafugites are 'exotic igneous rocks' with mineralogical and chemical compositions that differ from those observed in common magmas. Despite their importance, kamafugites are still a poorly constrained topic and they lack a general petrological model due to their variable petrographic, geochemical and isotopic features. A detailed comparison among kamafugites is necessary to obtain a deeper understanding of the processes involved in their petrogenesis and of their strongly metasomatised mantle sources. Therefore, this data publication doesn’t only provide new geochemical and isotopic data from kamafugites, but a comprehensive data compilation for kamafugite samples in the Western branch of the East Africa Rift (WEAR) in Uganda and Democratic Republic of Congo, the Intra-Apennine Province (IAP), as well as for the Alto Paranaiba (APIP) and Goiás (GAP) provinces in Brazil. 97 kamafugite whole-rock data for the WEAR, 42 for IAP and 51 for APIP and GAP have been collected and compared to highlight similarities and differences, aiming to reconstruct their petrogenesis. The new data in this data publication results from work conducted at Laboratory of Geochronology and Radiogenic Isotope Geochemistry - Pisa1 (IGG-CNR, Italy) supported by WP3 ILGE - MEET project, PNRR - EU Next Generation Europe program, MUR grant number D53C22001400005.
The data set includes the Digital Image Correlation (DIC) results for four experiments of releasing bends along dextral strike-slip faults that were performed at the University of Massachusetts at Amherst (USA). Gabriel et al. (2025) used the DIC data sets to investigate how releasing bend fault systems evolve within different strength wet kaolin. Information on the experimental set up and methods can be found in the main text and supplement to Gabriel et al. (2025). The data here include the incremental displacement time series, strain animation and surface elevation data at the end of the two experiments with different clay strength, which are presented within Gabriel et al. (2025). We also include in this data repository incremental displacement time series and strain animations from two experiments that repeat the conditions of the experiments featured in Gabriel et al. (2025).
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