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221 results for “multi-scales”
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Disentangling effects of climate and land use on biodiversity and ecosystem services – a multi-scale experimental design
<p>1. Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients.</p> <p>2. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximize the potential range and independence of environmental variables at different spatial scales.</p> <p>3. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, i.e. near-natural, agriculture and urban, resulted in 60 study regions (5.8x5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8 °C and a spatial extent of ~310x310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, i.e. forests, grasslands, arable land or settlement (local climate gradient 4.5–10 °C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r≤0.33| and |r≤0.29|, respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements.</p> <p>4. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.</p>
Multi-Scale Computational Screening to Accelerate Discovery of IL/COF Composites for Flue Gas Separation
<p>Covalent organic frameworks (COFs) have emerged as novel adsorbents and membranes for gas separation. Incorporation of ionic liquids (ILs) into COFs is important to exceed the current performance limits of COFs. However, synthesis and testing of a nearly unlimited number of IL/COF combinations are simply impractical. Herein, we used a multi-scale computational screening approach combining COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, Grand Canonical Monte Carlo (GCMC), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations to unlock both the adsorption- and membrane-based CO<sub>2</sub>/N<sub>2 </sub>separation performances of IL/COF composites. Several adsorbent and membrane performance assessment metrics including selectivity, working capacity, regenerability, adsorbent performance score, and permeability were computed. Our results revealed that IL-incorporation into COFs significantly improved CO<sub>2</sub>/N<sub>2</sub> adsorption selectivities (from 12 to 26) and adsorbent performance scores (from 3.7 to 12 mol/kg). By performing DFT calculations, the nature of the interactions between CO<sub>2</sub>, N<sub>2</sub>, COFs and their IL-incorporated composites were evaluated. The high CO<sub>2</sub> selectivity of IL/COF composites was attributed to the cooperative intermolecular effects induced by the COF and the IL. Finally, IL/COF membranes were studied, and results showed that they achieve significantly higher CO<sub>2</sub> permeabilities (2.4 10<sup>4</sup>-9.4 10<sup>5</sup> Barrer) than polymeric and zeolite membranes and comparable selectivities (up to 15.7), which hold great promise to replace conventional materials in membrane-based flue gas separation applications. Our results will be useful in accelerating experimental efforts to design new IL/COF composites that can achieve high-performance CO<sub>2</sub> separation.</p>
Dissociable Multi-scale Patterns of Development in Personalized Brain Networks
<p>The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results demonstrate that the development of functional brain networks align with and refine a hierarchy linked to cognition</p>
Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data
<p>As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should strive to incorporate multi-species perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships. We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe. Our findings revealed substantial variation in the factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale. Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores. Anthropogenic pressures also had an important influence on habitat use, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas. Policy implications: This study highlights the importance of adopting a multi-scale, multi-species approach for critical decision-making processes that depend on understanding wildlife distributions and habitat associations, such as protected area, corridor, and buffer zone prioritisation. We use our findings to identify changing rainfall patterns and increasing livestock numbers as two emerging trends that may impact wildlife distributions, both within sub-Saharan Africa and on a global scale.</p>
GPR-BMP-SPI: High spatial resolution multi-scale SPI datasets over China from January 1984 to December 2020
<p>The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models.</p> <p>The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces.</p> <p>The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map.</p> <p>The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”.</p> <p>Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. <em>Weather and Climate Extremes</em>, <em>40</em>, 100567.</p> <p> </p>
Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
<p>We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future.</p> <p>Location: California, United States </p> <p>Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. </p> <p>Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state.</p> <p>Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.</p>
PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning
<h2>Description:</h2> <p>a. <strong>Feature Engineering</strong>:</p> <p> The file <strong>data_of_145_features.csv</strong> consists of the datasets of the features. The datasets correspond to Fig. 1(a) of the paper.</p> <p>b. <strong>HierCrystalNet of PiezoTensorNet</strong>:</p> <p> The saved models after training HierCrystalNet are placed inside the zip folders, namely, (i)<strong> classification_saved_models.zip</strong>, (ii) PG1_cubic_saved_models.zip, (iii) PG2_tetragonal42m_saved_models.zip, (iv) PG3_orthorhombic222_saved_models.zip, (v) PG4_hex6tetra4mm_saved_models.zip, and (vi) PG5_orthorhombicmm2_saved_models.zip.</p> <p>c. <strong>ModularEnsembleNet of PiezoTensorNet </strong>:</p> <p> The prediction_results.zip folder consists the prediction results of the ModularEnsembleNet. </p> <p>d. <strong>Input data of piezoelectric tensors and stiffness tensors for the</strong> <strong>finite element analysis</strong>: </p> <p>(i) <em><strong>piezoelectric_tensors_AlN_B0.3Er0.5Al0.2N_alloy.zip</strong></em>: The coefficients of piezoelectric tensors (array shape: 3x6) for AlN alloy and B0.3Er0.5Al0.2N alloy are respectively presented in <strong>undoped_AlN_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_unrotated_piezoelectric_tensor.csv</strong> files. Both of these files contain the data for samples whose laboratory coordinate system is same with that of the orientation of crystal lattice (all of the three Euler angles theta, psi and phi = 0 degree ), and so the compositional effect on the piezoelectric behavior can be assessed solely without considering the orientation effect. Rotation or other transformation can cause the vector value of (theta, phi, phis) other than (0,0,0) which indicates that the laboratory coordinate system of a sample being different than its lattice orientation. The effect of two different magnitudes of rotation on the coefficients of piezoelectric tensors for B0.3Er0.5Al0.2N alloy are revealed through the files <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_1_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_2_piezoelectric_tensor.csv</strong>. Orientation 1 refers to<strong> theta = 0 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360 degree</strong> whereas <strong> theta = 180 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360</strong> for orientation 2. The units of the coefficients are C/m^2. The description of the methodology on how to generate this data in this format is provided at https://piezoelectrictensorsdatabase.streamlit.app/ .</p> <p>(ii) <em><strong>stiffness_tensors_AlN_B0.3Er0.5Al0.2N_alloy.csv</strong></em>: The stiffness tensors (array shape: 6x6) for the two materials AlN alloy and B0.3Er0.5Al0.2N alloy at room temperature are presented through the four csv files. The elements of stiffness tensors in the datasets are presented in GPa. The Cijkl tensors are provided first for isotropy and then for orthotropy assumptions. The description of these tensors for AlN and B0.3Er0.5Al0.2N materials are provided in the web apps: https://isotropic-elasticity.streamlit.app/ and https://orthotropic-elasticity.streamlit.app/</p>
Test data for "Imaging of cellular dynamics in vitro and in situ: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy"
<p>This repository contains test data associated with analysis code of low- and high-resolution self-driving multi-scale data from our manuscript "Imaging of cellular dynamics <em>in vitro</em> and <em>in situ</em>: from a whole organism to sub-cellular imaging with self-driving, multi-scale microscopy"</p> <p>by Stephan Daetwyler, Hanieh Mazloom-Farsibaf, Felix Y. Zhou, Dagan Segal, Etai Sapoznik, Bingying Chen, Jill M. Westcott, Rolf A. Brekken, Gaudenz Danuser and Reto Fiolka</p> <p> </p> <p>Code repository: <a href="https://github.com/DaetwylerStephan/multi-scale-image-analysis">https://github.com/DaetwylerStephan/multi-scale-image-analysis</a></p> <p>Documentation: <a href="https://daetwylerstephan.github.io/multi-scale-image-analysis/">https://daetwylerstephan.github.io/multi-scale-image-analysis/</a></p> <p>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2024.02.28.582579v1.full">https://www.biorxiv.org/content/10.1101/2024.02.28.582579v1.full</a></p>
Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset
<p>Dataset for the article "Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset"<br> by Bailly C., Fortin J., Adelinet M., Hamon Y.</p> <p>submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Please refer to the ReadMe file for more details.</p>
Dataset _ Multi-scale factors in blueberry pollination
Open the record for dataset details and reuse information.
Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Dataset: A multi-scale probabilistic atlas of the human connectome
<p>This repository complements the paper submitted to <strong>Scientific Data</strong> named <em>A multi-scale probabilistic atlas of the human connectome</em>.</p> <p><strong>Introduction</strong></p> <p>The assessment of the networks underlying brain processes is key to understand brain-related disorders. However, groundbreaking connectomics research is highly demanding in terms of equipment and expertise. The aim of this work is to create a multiscale probabilistic atlas of the human white matter (WM) to carry out network analyses in the context of clinical research, particularly when diffusion data is not directly available.</p> <p><strong>Methods</strong></p> <p>Sixty six subjects from the Human Connectome Project (HCP) database (29 males, age: 22-36 years old) were used to build the WM probabilistic atlas. MRI acquisition protocols are described in (Van Essen et al, 2012). Besides T1-, T2- and diffusion-weighted (DW) images, the HCP database provides the FreeSurfer outputs (Glasser et al, 2016) namely cortical surfaces (pial and white), subcortical segmentation and a cortical surface parcellation containing 34 structures for each hemisphere (Desikan et al, 2006).</p> <p>For each subject, the DWIs were employed to segment each thalamus in seven nuclei (Battistella et al, 2016) and to estimate the WM streamlines distribution. The constrained spherical deconvolution (Tournier et al, 2007) algorithm was used to compute the intravoxel fiber distribution functions for the anatomically-constrained particle-filter tractography approach (Descoteaux et al, 2009) to compute the WM streamlines.</p> <p>Subcortical, thalamic and multiscale cortical (Cammoun et al, 2012) parcellations were gathered to obtain four individual gray matter (GM) parcellations. Finally, for each scale, individual fiber bundles, were created by selecting the streamlines connecting each pair of GM regions (Figure 1a).</p> <p><em>Atlas construction</em></p> <p>The T1 and T2 images were non-linearly warped to their corresponding MNI templates (Evans et al, 2012, mni_icbm152_tal_nlin_asym_09c version) using ANTs (Avants et al, 2010). The resulting spatial transformations were applied to warp the individual fiber bundles to stereotactic space and the normalized tract density images (TDIs) were created. In these images, each voxel contains the number of streamlines passing through it. Finally, the spatial probability map for each bundle was obtained by binarizing and averaging the bundle TDIs across the subjects (Figure. 1b).</p> <p>Different views of the developed probabilistic multi-scale connectome atlas are shown in Figure 2.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"
<p>This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework".</p> <p>Below are the details of which file is required to produce which figure:</p> <p> </p> <p><strong>Figure 5 (De_dam_fields.pdf) </strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a></p> <p> </p> <p><strong>Figure 6 (convergence.pdf)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a></p> <p>Contains 4 files, each one for a different temporal resolution (delta t).</p> <p> </p> <p><strong>Figure C1 (convergence2.pdf, appendix)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a></p> <p>Each contains 4 files, one for each temporal resolution (delta t).</p> <p> </p> <p><strong>Figure 7 (CPU_time.pdf)</strong></p> <p>No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.</p> <p> </p> <p><strong>Figure 8 (T_h.pdf)</strong></p> <p><strong>Left panels, a, c, e</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a></p> <p>Each contains 4 files, one for each healing time (T_h)</p> <p><strong>Right panels, b, d, f</strong></p> <p>comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)</p> <p> </p> <p><strong>Figure 9 (comp_ddam_We_0_001.pdf)</strong></p> <p>comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 10 (comp_ddam_We_0_1.pdf)</strong></p> <p>comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 11 (discussion.pdf)</strong></p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz</p> <p> </p> <p><strong>SI movie</strong></p> <p>SI_movie.tar.gz</p>
Multi-Scale Representation Learning on Proteins
<p>Protein-ligand structures from PDBBind (v2019), and protein structures from the enzyme dataset as described in the paper: "Multi-Scale Representation Learning on Proteins" with associated code at https://github.com/vsomnath/holoprot.</p> <p><strong>Paper Abstract</strong></p> <p>Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein – HoloProt – connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and structure – comprising secondary and tertiary components – capture finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level. We test the learned representation on different tasks, (i.) ligand binding affinity (regression), and (ii.) protein function prediction (classification). On the regression task, contrary to previous methods, our model performs consistently and reliably across different dataset splits, outperforming all baselines on most splits. On the classification task, it achieves a performance close to the top-performing model while using 10x fewer parameters. To improve the memory efficiency of our construction, we segment the multiplex protein surface manifold into molecular superpixels and substitute the surface with these superpixels at little to no performance loss.</p>
Data from: Multi-scale predictors of parasite risk in wild male savanna baboons (Papio cynocephalus)
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Data from: Thalamus and focal to bilateral seizures: a multi-scale cognitive imaging study
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Evidence for multi-scale power amplification in skeletal muscle
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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