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608 results for “ensembles”
Raw Data and Full Ensemble Output (1880-2020) for "A NASA GISTEMPv4 Observational Uncertainty Ensemble"
<p>Contains the Raw and Final data for "A NASA GISTEMPv4 Observational Uncertainty Ensemble" as accepted at JGR:Atmospheres (August 2024). </p> <p><code>FinalEnsembleOutput/</code> Contains the official GISTEMPv4 uncertainty ensemble from 1880-2020 with anomalies relative to a 1951-1980 climatology. The ensemble is organized into three subdirectories</p> <p><code>FinalEnsembleOutput/FullEnsemble</code> Contains a netCDF file [lon,lat,month] for each of the 200 members on a 2x2 grid.</p> <p><code>FinalEnsembleOutput/GriddedSummary</code> Contains netCDF files [lon,lat,month] of statistics summarizing the 200-member ensemble. The statistics contained are the ensemble mean, ensemble sd, quantiles, and sample size (can be less than 200 due to differences in the homogenization in data-sparse regions). The quantiles provided are (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975).</p> <p><code>FinalEnsembleOutput/KeySeries</code> Contains 200-member ensembles of key, large scale time series at monthly resolution. Global, hemispheric, and zonal mean series are provided each for land, ocean, and combined (land and ocean) mean temeperature. <strong>Use this data when working with these series rather than calculating yourself from the full ensemble as the method for creating these series incoproates unertainty due to regions without coverage.</strong></p> <p><code>Raw/</code> Contains the raw, source data for the GISTEMP ensemble. This directory is not very large, but contains ~50k files as the GHCN product is distributed as an individual text file for every station in the record.</p> <p>Intermediate data products from the analysis can be found here: <a href="https://doi.org/10.5281/zenodo.13344579" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13344579</a></p>
On response of proteinoids ensembles to Fibonacci sequences
<p>Data for the paper "On the response of proteinoid ensembles to Fibonacci sequences."</p>
A Better Understanding of an Extremely High Ozone Episode with Ensemble Simulation
<p><span><span>Severe ozone pollution</span></span><span><span>s</span><span> may occur in the Great Bay Area (GBA) when typhoons approach South China. However, numerical models often fail to capture the high ozone concentrations during the episodes, leading to uncertainties in understanding their formation mechanisms. This study conducted an ensemble simulation with 30 members (EMs) using the WRF-Chem model, coupled with a self-developed ozone source apportionment method, to analyze an extremely high ozone episode associated with Typhoon NIDA in the summer of 2016. The newly proposed index effectively distinguished between well-performing (good) and poorly performing (bad) EMs. Compared to the bad EMs, the good EMs accurately reproduced surface ozone variations, particularly capturing the extremely high concentrations observed in the afternoon of July 31. The formation of such high ozone levels was attributed to the retention of ozone in the residual layer at night and the enhanced photochemistry during daytime. As Typhoon NIDA approached, weak winds confined large amounts of ozone in the residual layer at night. The development of planetary boundary layer (PBL) facilitated the downward transport of ozone aloft, contributing to the rapid increase in surface ozone in the following morning. The enhanced photochemistry was primarily driven by increased ozone precursors resulting from favorable accumulation conditions and enhanced biogenic emissions. During the period of high ozone concentrations, contributions from local and surrounding regions increased. </span></span><span><span><span><span>Additionally, ozone from southeastern Asia could transport to the GBA at high altitudes and then contribute to surface ozone when the PBL developed.</span></span></span></span></p>
1D simulation of land subsidence with ensemble Kalman filter
<p>This folder contains:</p> <p>a) output files from 1D simulations of land subsidence with ensemble Kalman filter in heterogeneous and highly compressible aquitards, reported in Zapata-Norberto et al., 2024.</p> <p>b) R Scripts to reproduce all figures and supplementary material in the cited reference.</p>
Biomod2 codes and CSV data for the ensemble model of C. marmorata
Open the record for dataset details and reuse information.
IFS CAT EDR Ensemble Data
<p>This dataset contains ECMWF IFS ensemble forecast data of CAT-EDR index (https://www.ecmwf.int/en/newsletter/168/meteorology/forecasting-clear-air-turbulence) analyzed in Gisinger, Bramberger, Dörnbrack, and Bechtold (2024, GRL).</p> <p> </p>
Data to "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"
<p>The zip files contains the tex file, figure, matlab files, and raw experimental and simulation data of the paper "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"</p>
Supporting data and code for the published paper: Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach
Open the record for dataset details and reuse information.
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>
Extended gtf based on a customized gtf file from Ensembl version 108 mm39 for Gastruloid
<p>This gtf has been generated based on https://doi.org/10.5281/zenodo.7510797 and extends 3' of genes using RefSeq and bulk RNA-seq from GSE106225, GSE113885 and time-course samples from GSE205781. All command lines can be found at <a href="https://github.com/lldelisle/extendMouseGTFUsingGastruloidData">https://github.com/lldelisle/extendMouseGTFUsingGastruloidData</a>.</p>
Data for "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble"
<p>The files uploaded here include the data shown in Figures 3.c), 4.a) and 4.b) of the article "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble", that can be found in: arXiv:2409.11117 </p> <p>Four datasets are included: </p> <p>df_diffusion.csv --> Figure 3.c</p> <p>df_vtransport.csv --> Figure 4.a </p> <p>df_ttransport.csv --> Inset figure 4.a </p> <p>df_decay.csv --> Figure 4.b</p> <p> </p>
Data from: Protocol dependence and state variables in the force-moment ensemble
Stress-based ensembles incorporating temperature-like variables have been proposed as a route to an equation of state for granular materials. To test the efficacy of this approach, we perform experiments on a two-dimensional photoelastic granular system under three loading conditions: uniaxial compression, biaxial compression, and simple shear. From the interparticle forces, we find that the distributions of the normal component of the coarse-grained force-moment tensor are exponential-tailed, while the deviatoric component is Gaussian-distributed. This implies that the correct stress-based statistical mechanics conserves both the force-moment tensor and the Maxwell-Cremona force-tiling area. As such, two variables of state arise: the tensorial angoricity and a new temperature-like quantity associated with the force-tile area which we name keramicity. Each quantity is observed to be inversely proportional to the global confining pressure; however only keramicity exhibits the protocol-independence expected of a state variable, while tensorial angoricity behaves as a variable of process.
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>
Example data for bias-restrained ensemble refinement in gmxapi 0.2
<p>Input files are provided for running bias-restrained ensemble refinement using gmxapi 0.2 as described in the accompanying manuscript, doi:10.1101/2021.07.18.452496. </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>
Customized gtf file from Ensembl version 108 mm39
<p>The gtf from Ensembl version 108 (mm39) was filtered to remove readthrough transcripts and all non-coding transcripts from a protein-coding gene. In addition, all genes with the same gene name which overlaps were merged under the same gene id to avoid ambiguous reads.<br> The procedure to generate the gtf is described in the bash file attached.<br> </p>
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks
<p>Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.</p>
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>
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.