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608
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Dataset results
608 results for “ensembles”
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>
CongDong2023_Macroscale regionalized ensemble estimation and analyses of wave periods across Canada
<p>Data for temporal variabilities and trends of Canadian wave periods.</p>
Theoretical analysis of four-wave mixing on semiconductor quantum dot ensembles with quantum light
<p>Dataset of the publication "Theoretical analysis of four-wave mixing on semiconductor quantum dot ensembles with quantum light" H. Rose, S. Grisard, A. V. Trifonov, R. Reichhardt, M. Reichelt, M. Bayer, I. A. Akimov, and T. Meier, Proc. SPIE 12419, Ultrafast Phenomena and Nanophotonics XXVII, 124190H (2023). ( <a href="https://doi.org/10.1117/12.2647700">https://doi.org/10.1117/12.2647700</a> ). The zip file includes the data on which the plots shown in figures 1 and 2 are based.</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>
Animation of PHELS global ensemble average hazard (rzmc&rainfall) for the year 2015
<p>This animation of daily global landslide hazard for the year 2015 comes from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for the combination of rainfall and root-zone soil moisture (rzmc, 0-100cm depth) as hydrological predictor variables alongside global landslide susceptibility estimates. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events.</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>
Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images
<p>This Dataset contains the train and test NLO images in pickle format used for the following publication: Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images</p>
Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins
<p>Simulation data and the corrosponding analysis script for the work "<strong>Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins</strong> " by <em>Hiranmay Maity, Lipika Baidya </em>and<em> Govardhan reddy</em> are deposited here. </p> <p>Analysis Scripts:</p> <p>The scripts for analysing the simulation data are in analysis_script.zip. The folder contains:</p> <ul> <li> <p>autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C.</p> </li> <li> <p>average_property.cpp: code for calculating average property such as radius of gyration (R<sub>g</sub>) from trajectory files in C++.</p> </li> <li> <p>calculate_saxs_kratky.c: code for calculating scattering profile (SAXS and Kratky) from simulation data in C.</p> </li> <li> <p>compute_contact_map.cpp: code for calculating contact map in C.</p> </li> <li> <p>probablity_distribution.c: code for calculating probablity distribution of Rg in C.</p> </li> <li> <p>structure_factor.c: code for calculating structure factor in C.</p> </li> </ul>
Human genome fasta file from ensembl (GRCh38 v109)
<p>Human genome fasta file from ensembl (GRCh38 v109), <span>corresponds to GenBank Assembly ID </span><span>GCA_000001405.28</span></p>
Results of Climate change impact on sediment discharge using a large ensemble rainfall dataset
<p>Calculation results for each resolution of Climate change impact on sediment discharge using a large ensemble rainfall dataset</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>
ResNet 34 Ensemble Predictions on TinyImageNet
<p>Predictions generated by an ensemble of 4 ResNet 34 Deep Neural Networks Trained on TinyImagenet, as used in repository https://anonymous.4open.science/r/ensemble_attention-7616/README.md. Ensembles are trained to encourage/discourage predictive diversity. Each timestamped folder contains individual training runs, with the labels and probabilistic predictions of the ensemble on 1) the training set (train_labels.npy, train_preds.npy) and 2) the test set (ind_labels.npy, ind_preds.npy) for tinyimagenet. The file (tinyimagenet/resnet34/version_0/hparams.yaml) contains specific hyperparameters used on a particular training run. Figures visualizing training results can be generated by:<br> 1.unzipping the four folders in to the directory `ensemble_attention/scripts/outputs/` <br> 2. running the script `ensemble_attention/scripts/vis_scripts/all_weights_resnet34_tinyimagenet.py`.</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>
High quality figures of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>This repository provides the figures for the publication "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
The Data and Codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)
<p>Note: the monthly averaged NCAM, SPCAM, and CAM5 results are uploaded as *_h0.tar.gz!</p> <p>The data and codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en) are stored in this repositary.</p> <p>This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook.</p> <p>Please make sure to install all python packages used in an environment.</p> <p>Please read the ReadME-2.txt.</p> <p>For the entire training and testing datasets in both the baseline and +4K SST climates. Please download them from Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 and https://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0).</p>
Results of the study "Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hind-cast ensemble for the Baltic Sea" in Ocean Science
<p>This archive stores the main results, the main scripts, and the model code of the study:</p> <p>Lorenz, M. and Gräwe, U.: Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hind-cast ensemble for the Baltic Sea, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-820, 2023.</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>
Machine learning based methods to generate conformational ensembles of disordered proteins (len54)
<p>data is in rep_1 for all sequences, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy)</p>
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.