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Dataset results
608 results for “ensembles”
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Outputs from Isca perturbed parameter ensemble (PPE) simulations (Part II)
<p>Outputs from Isca perturbed paramter ensemble (PPE) simulations under 1xCO2 and 4xCO2, in which the simulations are prescribed with Q-flux. The output is the extracted data for the first 20-year simulations for the surface temperature and radiative fluxes at the top of the atmosphere (TOA).</p> <p>This is Part II, and Part I can be found at: <a href="https://doi.org/10.5281/zenodo.5150241">10.5281/zenodo.5150241</a></p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Ensemble of global landslide hazard from PHELS
<p>Daily global landslide hazard from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for different hydrological predictor variables alongside global landslide susceptibility estimates: A 7-day antecedent rainfall index (ARI7), daily rainfall, daily root-zone soil moisture (rzmc, 0-100cm depth) and the combination of rainfall&rzmc. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events. For all four hydrological predictor variable (combinations) deterministic hazard estimations are provided. Please note the different order of magnitude in the hazard values when using one or two hydrological predictor variables. For rainfall&rzmc results of ensemble simulations (100 members) are additionally provided, more specifically the ensemble average and standard deviation. The latter is a measure for the uncertainty of the estimated hazard. Details can be found in Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M. (2023): Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling (NHESS, https://doi.org/10.5194/egusphere-2023-869). The global landslide susceptibility estimation is described in Felsberg et al. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling (NHESS, https://doi.org/10.5194/nhess-22-3063-2022)</p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
AdriE ocean climate model ensemble for the Adriatic Sea - monthly fields
<p>This dataset contains the monthly-averaged fields of the key physical oceanographic quantities from the AdriE ocean model ensemble. The model runs were carried out by using the ROMS modelling system (Haidvogel et al., 2008) forced by the SMHI-RCA4 Regional Climate Model (Samuelsson et al., 2021), in turn driven by different General Circulation Models. The period is 1987-2099 in the severe RCP8.5 scenario for the climate simulations, whereas the evaluation runs span the period 1987-2010. Each file contains the results for one run.</p> <p>The model implementation and its validation are fully described in a manuscript recently submitted to Ocean Science (Bonaldo, D., Carniel, S., Colucci, R. R., Denamiel, C., Pranic, P., Raicich, F., Ricchi, A., Sangelantoni, L., Vilibic, I., and Vitelletti, M. L.: AdriE: a high-resolution ocean model ensemble for the Adriatic Sea under severe climate change conditions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1468, 2024).</p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
<p><em>Aim:</em> Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.</p> <p><em>Location: </em>Seamounts around the Cabo Verde Archipelago (NW Africa).</p> <p><em>Methods:</em> We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.</p> <p><em>Results</em>: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.</p> <p><em>Main conclusions:</em> Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.</p>
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
New Ideas for Brain Modelling 4-Figure 3. Neuron Pairing: an ensemble neuron links with a hierarchal neuron. Also figure 4 in Greer (2016)
<p>The model is also based on the idea of an auto-associative neural network. The Hopfield neural network (Hopfield, 1982), and its stochastic equivalents are auto-associative or memory networks. With the memory networks, information is sent between the input and the output until a stable state is reached, when the information does not then change. These are resonance networks, such as bidirectional associative memory (BAM), or others (Rojas, 1996), but they can only provide a memory recall – they map the input pattern directly to the output pattern. If some of the input pattern is missing however, they can still provide an accurate recall of the whole pattern. They also prefer the data vectors to be orthogonal without overlap. This is however ideal for the binding that only wants to reproduce the base ensemble in the hierarchy.</p>
New Ideas for Brain Modelling 4-Figure 2. One level of linking in a temporal model defines a particular ensemble mix.
<p> The image processing of section 0 has already been tried in de Campos, Babu and Varma (2009), where they tested the full dataset. Their results were better overall, with maybe 55% accuracy and over a larger dataset. As stated however, the tests here are only initial results and it would be expected that some improvement would be possible, especially if the images can be scaled. The recently found paper Kowalski (1972) looks significant and the general architecture (Greer, 2016, figure 2, for example)) could have analogies with bi-directional searches in the and-or with theorem-proving graphs architecture of that paper. As suggested, and-or could work from goals to axioms (the neural network in the general model and theorem-proving from axioms to goals (the concept trees in the general model). The paper Sukanya and Gayathri (2013) models at a higher behaviour level, but it is interesting that the behaviours are considered to be unique (time or sequence-based) sets of events and these event patterns are then clustered, rather than each individual event. The idea of using unique sets of nodes to cluster with has also been used for the symbolic neural network (Greer, 2011).</p>
Memory Effects in a Random Walk Description of Protein Structure Ensembles
<p>This <a href="https://www.activepapers.org/">ActivePaper </a>file contains all the code and data that was used in generating the figures for the article "Memory Effects in a Random Walk Description of Protein Structure Ensembles" by Gerald R Kneller and Konrad Hinsen, J. Chem. Phys. <strong>150</strong>, 064911 (2019); <a href="https://doi.org/10.1063/1.5054887">https://doi.org/10.1063/1.5054887</a></p>
Simulation results from HadGEM-UKCA perturbed parameter ensembles for Yoshioka et al. 2019 JAMES
<p>This dataset was created from perturbed parameter ensembles (PPEs) using HadGEM-UKCA atmospheric composition climate model and used in Yoshioka et al. (Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and their Radiative Forcing) submitted to The Journal of Advances in Modeling Earth Systems (JAMES). It contains the following data;</p> <p>N50_sfc_data.tar.gz contains simulated number concentrations of particles larger than 50 nm from one-at-a-time screening experiments used in Figure 1 of the paper. teaca-teacl are job IDs of different experiments where values of parameters RAIN_FRAC and CLOUD_ICE_THRESH were varied;<br> teaca: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.7<br> teacb: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.5<br> teacc: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.3<br> teacd: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.1<br> teace: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.7<br> teacf: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.5<br> teacg: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.3<br> teach: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.1<br> teaci: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.7<br> teacj: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.5<br> teack: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.3<br> teacl: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.1</p> <p>CCN0p2_Stations_for_figure6.xlsx contains cloud condensation nuclei concentration at 0.2% supersaturation calculated for the AER ensemble of simulations at station locations used to create Figure 6 of the paper.</p> <p>Mean_Variance_CCN0p2_L9_AER_2008ANN.nc and Mean_Variance_CCN0p2_L9_AER-ATM_2006ANN.nc are emulator means and variances of annual average cloud condensation nuclei concentration at 0.2% supersaturation at model level 9 (~650m) in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_AOD550_AER_2008ANN.nc and Mean_Variance_AOD550_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol optical depth at 550 nm in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_RFnet_AER_2008ANN.nc and Mean_Variance_ERF_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol radiative forcing in AER ensemble and aerosol effective radiative forcing in AER_ATM ensemble and used in Figures 7, 8 and 9.</p> <p>Mean_Variance_SeaSalt_Load_AER_2008ANN.nc and Mean_Variance_SeaSalt_Load_AER-ATM_2006ANN.nc are emulator means and variances of annual average column mass loading of sea salt aerosol in AER and AER-ATM ensembles and used in Figure 9.</p>
Ice-sheet model simulation ensembles (produced Fall 2018)
<p>Ice-sheet model simulation ensembles over the last interglacial and a future high emissions scenario (RCP8.5), and varied over two model parameters (CREVLIQ and CLIFVMAX). The data was pickled as a pandas dataframe with python 3, and can be retrieved by loading using pickle with the same version.</p>
The dataset for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.
<p>This repository contains data, analysis codes, and model configuration files for NICAM and SCALE-RM, which is used for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.</p> <p>There are 6 fortran codes at the top directory.<br> 1. deep_convective_area.f90<br> 2. algorithm01.f90<br> 3. algorithm02.f90<br> 4. statistics_algorithm01.f90<br> 5. statistics_algorithm02.f90<br> 6. spectrum_calculation.f90<br> You can find description for each code at the top it.</p> <p>All data are archived in subdirectories. Directory tree is following:</p> <p>Sueki-et-al.2020/<br> |-- exp-a<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0800<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx1600<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx3200<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- exp-b<br> | |-- dx0050<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0100<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx0800<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- model-configuration-file<br> | |-- nicam<br> | `-- scale-rm<br> | |-- exp-a<br> | | |-- init<br> | | |-- pp<br> | | `-- run<br> | `-- exp-b<br> | |-- init<br> | |-- pp<br> | |-- run01<br> | |-- run02<br> | `-- run03<br> |-- spectrum<br> `-- statistics</p>
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
Figure 4 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 4. Overlay of Iranian Conservation Network with the habitat suitability map of the Mesopotamian spiny-tailed lizard.
Figure 3 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 3. Model of habitat suitability for the species based on the present climatic data (A) and 2.6 (B) and 8.5 (C) scenarios of the CCSM for the future.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.