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141 results for “ensemble; model”
Estimates of daily river flows for 190 catchments in Great Britain from the GR6J model using CRCM5 large ensemble
<p>Simulated river flows for 200 catchments using the GR6J hydrological model driven by the CRCM5 50-member large ensemble</p>
A New Model of Interpretation and Communication for Ensemble Music-Making - Appendices
<p>Appendices for Music Ph.D</p>
LCM ensemble model results with GCCN and TICE effect
<p>Original data from the LCM ensemble model results (time series and spectra data)</p>
Cluster configurations of a generalized Deffuant model on hypergraph ensembles
<p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one folder (after unpacking the tar archive).<br> Note that some parameter values are missing, if they did not converge in reasonable time.</p> <p><br> * Barabasi Albert with a mean degree of `c=9` and hyperedge size of `k=3`: `ba_c9_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Barabasi Albert with a mean degree of `c=10` and hyperedge size of `k=5`: `ba_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=3`: `er_c10_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=4`: `er_c10_k4`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=5`: `er_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=6`: `er_c10_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=150` hyperedge size `k=6`: `er_c150_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=5` and `c_5=5`: `er_c3_5_c5_5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=30/8` and `c_5=50/8`: `er_c3_375_c5_625`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Lattice a mean degree of `c=12` hyperedge size `k=3`: `lat_c12_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32761`<br> * Lattice a mean degree of `c=15` hyperedge size `k=5`: `lat_c15_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`</p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p><br> ## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the `er_c10_k3`<br> case, with a function to read this format is given in `example.py`.</p>
Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"
<p>Ensemble of intrinsically disordered proteins used in: <em>"Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"</em>.</p> <p>Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for:</p> <ul> <li>Hst5</li> <li>RS</li> <li>DSS1</li> <li>Sic1</li> <li>ProTa</li> <li>NHE6cmdd</li> <li>A1</li> <li>aSyn</li> <li>ANAC046</li> <li>GHR-ICD</li> <li>Tau</li> </ul>
Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations
<p>The model codes, data, and plot scripts used in the paper, "Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations".</p> <ul> <li>parcel model code.zip contains model code.</li> <li>Results.zip contains output data of each experiment in this study.</li> <li>Figs and scripts.zip are the NCL scripts used for figures.</li> </ul>
Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model
<p>This dataset includes the calculation steps and optimization process of an ensemble model, along with various results and related intermediate files</p>
Multi-model ensemble bias-corrected precipitation dataset for historical and future climate (1961–2099) in China
<p>本文基于耦合模式比较项目第六阶段(CMIP6)的27个全球气候模式(GCM),采用随机森林(RF)模型和EQM方法整合27个大气监测模型的降水模拟数据,进一步修正中国综合月降水数据。修正后的降水资料在月降水量和极端降水量方面均明显优于原GCM降水资料。数据以 GeoTIFF 格式,其中嵌入了具有 1° 空间分辨率的地理配准信息。LST在GeoTIFF中的单位是mm。压缩文件被命名为历史文件.zip、SSP126.zip、SSP245.zip 和 SSP585.zip。压缩文件中的每个文件都命名为“yyyymm.tif”,其中“yyyy”和“mm”分别表示年份和月份。例如,文件“196101.tif”存储了 1961 年 1 月中国每月降水量。</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Ensembles of climate model parameters of Mars and significance values
<p>Data set containing an ensemble of model parameters for each of the candidate climate models in Table 1 of Izquierdo et al., (2023). These ensembles are stored as Python objects using the Pickle module. Files for each candidate model are named based on the accumulation and lag sub models and the number of steps used in the Markov chain Monte Carlo algorithm. From each Python object, it can be extracted the distribution of accumulation and retreat rates with time following the scripts and notebooks of the repository referenced in the open research section of the paper. </p> <p>The folders in this repository refer to the ensembles of all candidate models dependent of insolation values (insolation), ensembles of all candidate models dependent on obliquity values (obliquity), and the mean likelihood and Bayes factor of each candidate model (bayesFactor). </p>
A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability
<p>Data and analysis scripts for figures of Journal article (A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability)</p>
Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years
<p>1 Introduction</p> <p>The data archive provides the reconstructed dataset capturing the annual runoff across Europe, partitioned into a grid format and preserved in NetCDFv4 (.nc) format for enhanced geospatial information.</p> <p>1.1 Coordinate system and spatial resolution</p> <p>Each grid cell in the dataset corresponds to a 0.5-degree spatial resolution, using the World Geodetic System 1984 (WGS84) as the standard coordinate frame.</p> <p>1.2 Temporal resolution</p> <p>The data encapsulates a yearly temporal resolution, offering a comprehensive outlook from 1500 to 1999. For example data for 1500 are represented by the layer 01/01/1500.</p> <p>1.3 Units</p> <p>Runoff measurements are quantified in millimeters per year (mm/year), providing hydrological data throughout the noted time frame.</p> <p>1.4 Example</p> <p>library(terra)<br> library(raster)</p> <p>> dt_cc<-rast("HEMMF_ERUN_1500_1999.nc")<br> > dt_cc<br> class : SpatRaster <br> dimensions : 70, 104, 500 (nrow, ncol, nlyr)<br> resolution : 0.5, 0.5 (x, y)<br> extent : -12, 40, 35, 70 (xmin, xmax, ymin, ymax)<br> coord. ref. : lon/lat WGS 84 (EPSG:4326) <br> source : HEMMF_ERUN_1500_1999.nc <br> varname : runoff <br> names : runoff_1, runoff_2, runoff_3, runoff_4, runoff_5, runoff_6, ... <br> unit : mm/year, mm/year, mm/year, mm/year, mm/year, mm/year, ... <br> time (days) : 1500-01-01 to 1999-01-01 </p> <p> </p> <p>1.5 Citation</p> <p>The specific data file, named ’HEMMF ERUN 1500 1998.nc,’ is conveniently structured to facilitate easy handling and interpretation of the information. Please ensure to attribute the correct citation when utilizing this dataset, adhering to the subsequent reference: [Singh et al., 2023] References Ujjwal Singh, Petr Maca, Martin Hanel, Yannis Markonis, Rama Rao Nidamanuri, Sadaf Nasreen, Johanna Ruth Bl¨ocher, Filip Strnad, Jiri Vorel, Lubomir Riha, and Akhilesh Singh Raghubanshi. Hybrid multi-model ensemble learning for reconstructing gridded runoff of europe for 500 years. Information Fusion, 97:101807, 2023. ISSN 1566-2535. doi: https://doi.org/10.1016/j.inffus. 2023.101807. URL https://www.sciencedirect.com/science/article/pii/S1566253523001161#d1e5346.</p>
A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble
<p>Supporting information for manuscript titled "A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble"</p><p>Datasets S1. Early to Middle Miocene NetCDF files: E2MMIO280.nc, E2MMIO400.nc, E2MMIO560.nc, E2MMIO850.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S2 Middle to Late Miocene NetCDF files: M2LMIO280.nc, M2LMIO400.nc, M2LMIO560.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S3 Preindustrial NetCDF files: PI contains MioMIP1 climate variables used to make manuscript figures.</p><p>Dataset S4 CSV file MioMIP_MAP_compilation contains newly revised miocene reconstructed mean annual precipitation from proxies. </p>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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Data from: Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models
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Data from: Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution
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A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA
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Data from: Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models
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Data from: How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities
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ScienceDex guides
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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.