Automated Organization ProfileUniversitá degli studi "Roma TRE", Rome, Italy
Universitá degli studi "Roma TRE", Rome, Italy
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 8.3 (sum of 6 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset includes raw data used in the paper by Reitano et al. (2022), focused on the effect of boundary conditions on the evolution of analogue accretionary wedges affected by both tectonics and surface processes; the paper also focuses on the balance between tectonics and surface processes as a function of the boundary conditions applied. These boundary conditions are convergence velocity and basal slope (i.e., the tilting toward the foreland imposed prior the experimental run). The experiments have been carried out at Laboratory of Experimental Tectonics (LET), University “Roma Tre” (Rome). Detailed descriptions of the experimental apparatus and experimental procedures implemented can be found in the paper to which this dataset refers. Here we present: • Pictures recording the evolution of the models. • GIFs showing time-lapses of models. • Raw DEMs of the models and Incision DEMs, used for extracting data later discusses in the paper.
Authors
- Reitano, Riccardo ;
- Faccenna, Claudio ;
- Funiciello, Francesca ;
- Corbi, Fabio ;
- Sternai, Pietro ;
- Willett, Sean D. ;
- Sembroni, Andrea ;
- Lanari, Riccardo
This dataset provides friction and elasticity data from ring shear and axial tests, respectively, on rock analogue materials used at the University Roma Tre (Rome, IT) in “Foamquake”, a novel seismotectonic analog model mimicking the megathrust seismic cycle (Mastella et al., under review). Two granular materials (quartz sand and Jasmine rice) have been characterized by means of internal friction coefficients µ and cohesions C. An elastic material (foam rubber) have been characterized by means of Young’s modulus E and Poisson’s ratio v. According to our analysis the granular materials show Mohr-Coulomb behaviour characterized by linear failure envelopes in the shear stress vs. normal load Mohr space. Peak, dynamic and reactivation friction coefficients of the quartz sand are µP = 0.69, µD = 0.56 and µR = 0.64, respectively. Cohesion ranges between 50 and 100 Pa. Rate-dependency of friction in quartz sand seems insignificant. Peak, dynamic and reactivation friction coefficients of the Jasmine rice are µP = 0.70, µD = 0.59 and µR = 0.61, respectively. Cohesion ranges between 30 and 50 Pa. Rate-weakening of Jasmine rice is c. 6% per tenfold change in shear velocity v. The Young’s modulus of the foam rubber has been constrained to 30 kPa, its Poisson’s ratio is v=0.1.
Authors
- Mastella, Giacomo ;
- Corbi, Fabio ;
- Funiciello, Francesca ;
- Rosenau, Matthias ;
- Rudolf, Michael ;
- Kosari, Ehsan
This dataset includes particle image correlation data from 26 experiments performed with Foamquake, a novel analog seismotectonic model reproducing the megathrust seismic cycle. The seismotectonic model has been monitored by the means of a high-resolution top-view monitoring camera. The dataset presented here represents the particle image velocimetry surface velocity field extracted during the experimental model through the cross-correlation between consecutive images. This dataset is supplementary to Mastella et al. (2021) where detailed descriptions of models and experimental results can be found.
Authors
- Mastella, Giacomo ;
- Corbi, Fabio ;
- Funiciello, Francesca ;
- Matthias, Rosenau
This dataset includes the results of Particle Image Velocimetry (PIV) of one experiment on subduction megathrust earthquakes (with interacting asperities) performed at the Laboratory of Experimental Tectonics (LET) Univ. Roma Tre in the framework of AspSync, the Marie Curie project (grant agreement 658034; https://aspsync.wordpress.com). Detailed descriptions of the experiments and monitoring techniques can be found in Corbi et al. (2017). This data set is from one experiment characterized by the presence of a 7 cm wide barrier separating two asperities with equal size, geometry and friction. Here we provide PIV data relative to a 16.3 min long interval during which the experiment produces 138 analog earthquakes with an average recurrence time of 7 s. The PIV analysis yields quantitative information about the velocity field characterizing two consecutive frames, measured in this case at the model surface. For a detailed description of the experimental procedure, set-up and materials used, please refer to the article of Corbi et al. (2017) paragraph 2. This data set has been used for: a) studying velocity variations (Fig. 2 in Corbi et al., 2021) and rupture patterns (Fig. 3a, b in Corbi et al., 2021) occurring during the velocity peak of one of the two asperities (aka trigger).
Authors
- Corbi, Fabio ;
- Bedford, Jonathan ;
- Poli, Piero ;
- Funiciello, Francesca ;
- Deng, Zhiguo
This dataset is supplementary material to the article by Xu et al. (2016) ‘Graben formation and dike arrest during the 2009 Harrat Lunayyir dike intrusion in Saudi Arabia: Insights from InSAR, stress calculations and analog experiments’. The Authors described the spatial and temporal effects of a propagating dike on crustal deformation, including the interaction with faulting, using a multidisciplinary approach. This supplementary material concerns the analog modelling part only. For a detailed description of the experimental procedure, set-up and materials used, please refer to the article of Xu et al. (2016; paragraph 5). The data available in this supplementary publication are: - A folder (2019-003_Corbi-et-al_Fig6.zip) containing: 1. top-view pictures (e.g. ‘lunayyr1_0025.JPG’) and displacement data obtained with MatPiv (e.g. ‘uun25.mat’ and ‘uvn25.mat’; dike parallel and orthogonal components; respectively) shown in figure 6 of Xu et al 2016. 2. a Matlab script (‘fig6_a_h.m’) that allows reproducing the same figure setup as in figure 6 panels a-h of Xu et al 2016. The thick red line highlights dike position. The background shading refers to dike orthogonal displacement. - A folder (2019-003_Corbi-et-al_PIV_data.zip) containing: 1. surface deformation data obtained with MatPiv. Each file (‘vel_fine_piv#.mat’) contains 4 elements (x, y, u, v) representing the coordinates and horizontal and vertical component of incremental velocity field organized in a 143 x 215 matrix; 2. the run_movie.m Matlab script. Running it the user can visualize the space-time evolution of cumulative surface displacement. The background shading refers to dike orthogonal component of displacement. The thick red line highlights dike position. - A folder (2019-003_Corbi-et-al_pictures.zip) containing the whole set of pictures from the experiment shown in Xu et al., 2016. - A movie (2019-003_Corbi-et-al_graben formation.mp4) obtained using the whole set of pictures (96 photos). The thick red line highlights dike position. The amount of dike opening is reported as header. - A movie (2019-003_Corbi-et-al_cum_displacement.mp4) showing the space-time evolution of cumulative surface displacement, where the background shading refers to dike orthogonal component of displacement. The thick red line highlights dike position.
Authors
- Corbi, Fabio ;
- Xu, Wenbin ;
- Rivalta, Eleonora ;
- Jonsson, Sigurjon
This data publication includes movies and figures of twenty-six analogue models which are used to investigate what controls sill emplacement, defining a hierarchy among a selection of the proposed factors: compressive stresses, interface strength between layers, rigidity contrast between layers, density layering, ratio of layer thickness, magma flow rate and driving buoyancy pressure (Sili et al., 2019). Crust layering is simulated by pig-skin gelatin layers and magma intrusions is simulated by colored water. The experimental set-up is composed of a 40.5 X 29 X 40 cm3 clear-Perspex tank where a mobile wall applies a deviatoric compressive stress (C, in Table 1) to the solid gelatin (Figure 1). In each experiment is imposed two layers with different density and rigidity, separated by a weak or strong interface, excluding two experiments characterized by homogeneous gelatin (experiment 4 and 12). Three different rigidity contrast (1, 1.3, 1.8) between the two layers are imposed, defined as the ratio between the Young’s moduli of the upper (Eu) and lower (El) layer. By using NaCl and gelatin concentration, two layers with same rigidity but different densities are obtained, investigating the influence of the density contrasts on sill emplacement. The effects of the ratio between layer thicknesses (i.e. the ratio between upper and lower layer thickness: Thu/Thl) was simulated by changing only the thickness of the upper layer; while magma flow rate are studied changing the flow rate of peristaltic pump. Water density was increased by adding NaCl to analyze the effect of changing driving buoyancy pressure (Pm) that depends on the density difference between host rock and magma (Δρ), gravitational acceleration (g) and intrusion length (H). In the table different colors indicate the experiment result: black = dike; red = sill and blue = sheet. The here provided material includes time-lapse movies showing intrusion propagation of the twenty-six models with a velocity of 5 times higher compared to the real time (1 second in the movie is 25 real seconds). These visualizations are side (XZ or YZ plane in Figure 1) and/or top views (XY plane in Figure 1).
Authors
- Sili, Giulia ;
- Urbani, Stefano ;
- Acocella, Valerio