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ShareScore release 0.9.0
Dataset results
141 results for “ensemble; model”
Data from: Ensemble modelling of the potential distribution of the whale shark in the Atlantic Ocean
<p><span>This dataset reports observations of whale shark (<i>Rhincodon typus</i>) in the tropical and subtropical Atlantic Ocean. Data were obtained by the Spanish Institute of Oceanography (IEO) observers program, and were rescaled to the resolution of the Bio-ORACLE 2.0 variables, against which they were modelled along with presences from additional online sources. Our data include both presence and surveyed absence records – i.e., places where observers were active and did not detect whale sharks – within 5 arc-minute pixels spanning the surveyed area.</span></p>
Dispersal patterns and potential distribution prediction of three rice planthopper species in China based on the ensemble model
<p>Emergence of three rice planthopper species in China from 1993 to 2022.</p>
Enhancing Streamflow Prediction through Multi-model Ensemble Framework and Machine Learning Techniques
<p>This file contains python code used in this study and data used to plot figures. </p>
AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants : Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance
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The hydrological fluxes of the Upper Brahmaputra River Basin constrained by a multi-physics ensemble (MPE) modeling approach
<p>The data represent monthly hydrological fluxes for four sub-basins within the Upper Brahmaputra River Basin, where yyyy is the year, mm is the month, MPE is the multi-physics ensemble, P is the precipitation, R is the runoff, and ET is the evapotranspiration. The unit is mm. The upper-bounds and lower-bounds represent the upper and lower bounds of the hydrological fluxes constrained by the MPE, respectively.</p> <p> </p> <p>Reference:<br>Lei, X, P. Lin*, H. Zheng, K. Yang, W. Liu, C. Miao, K. Wang, J. Wang: A multi-physics ensemble modeling approach to constraining the uncertainty of hydrological fluxes in sparsely-gauged river basins. Geophysical Research Letters, (submitted), 2024.<br>Contact:<br>xiangyonglei@stu.pku.edu.cn; peironglinlin@pku.edu.cn</p>
Novel ensemble models for groundwater potential mapping: Application of the Split-Point Sampling and Node Attribute Subsampling Classifier in Vietnam
<p>Le Tien Duy</p>
Data from: Ensemble modelling of the potential distribution of the whale shark in the Atlantic Ocean
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Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The data is the monthly NO3- concentrations across China during 2005-2015. These data was obtained using a novel ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms based on satellite data, assimilated meteorology, and other geographical covariates.</p> <p>In the datasets, XX-YY denote the XX month in YY year.<br> For instance, January-05 denotes the January in 2005.<br> NaN in the data denote the missing values.</p>
Data from: Modelling dynamics in protein crystal structures by ensemble refinement
Single-structure models derived from X-ray data do not adequately account for the inherent, functionally important dynamics of protein molecules. We generated ensembles of structures by time-averaged refinement, where local molecular vibrations were sampled by molecular-dynamics (MD) simulation whilst global disorder was partitioned into an underlying overall translation–libration–screw (TLS) model. Modeling of 20 protein datasets at 1.1–3.1 Å resolution reduced cross-validated R_free values by 0.3–4.9%, indicating that ensemble models fit the X-ray data better than single structures. The ensembles revealed that, while most proteins display a well-ordered core, some proteins exhibit a 'molten core' likely supporting functionally important dynamics in ligand binding, enzyme activity and protomer assembly. Order–disorder changes in HIV protease indicate a mechanism of entropy compensation for ordering the catalytic residues upon ligand binding by disordering specific core residues. Thus, ensemble refinement extracts dynamical details from the X-ray data that allow a more comprehensive understanding of structure–dynamics–function relationships.
The best performing landslide susceptibility maps using ensemble machine learning models and precipitation data on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through ensemble machine learning models with included precipitation data for the basin of Valchiavenna, and the Lombardy region in Italy.</p> <p>A list of the used base machine learning methods:</p> <ul> <li>Neural Networks.</li> </ul> <p>A list of the precipitation data included in the models:</p> <ul> <li>Average hourly precipitation for the year of 2020,</li> <li>90<sup>th</sup> percentile for the hourly precipitation for the year of 2020 ,</li> <li>Averaged + 90<sup>th</sup> percentile for the hourly precipitation for the year of 2020.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
LCM ensemble model results considering GCCN and TICE effect
<p>Original ".nc" formatted output data (time series and spectra data) from the LCM ensemble model results.</p> <p>The output file from the faster DSD case (r_mean = 15 micrometer) is not included for storage reasons.</p> <p>In addition, only results with n_SD = 10^5 are included.</p> <p>For those cases, you can contact the first author and request raw output files.</p>
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
<p>The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled <em>Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.</em></p>
Raw data of the heterogeneous Hegselmann-Krause model on network ensembles
<p># Raw data of the heterogeneous Hegselmann-Krause model on network ensembles<br> This is the raw data underlying the results of the article *«On the effects of over-compromising: heterogeneity and network effects on a bounded confidence opinion dynamics model.»*.</p> <p>For each measured combination of the parameters, there is one gzipped file. The parameters are:</p> <p> - Lower and upper bounds of the confidence interval, [ε_l, ε_u].<br> - Topology: the different types of networks and the average degree with which the networks are generated.<br> - System size<br> - Number of realizations for the parameter combination<br> <br> The single files follow a naming scheme of `data_HK_uni[{eps_l},{eps_u}]_topo={topology}_N={N}_trajrecord=0_{m}real.dat.gz`, where:</p> <p> - `{eps_l},{eps_u}` are the values of the lower and upper bounds of the confidence interval.<br> - `{topology}` contains the type of network and the average degree. The possibilities are `BA_k=10`, `ER_c=10`, `sl1`, `sl2`, and `sl3`.<br> - `N` is the system size. The sizes are powers of two.<br> - `trajrecord=0` signals the fact that file contains only the final state.<br> - `{m}` is the number of realizations.</p> <p># Data format<br> Each file contains the final state of each realization back to back. Each final state is encoded as three lines:</p> <p> - The convergence time is a single integer with a line prefix '\# iterations:'<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># Folders structure<br> The files are organized as follows:</p> <p> - **`phase_plots.tar`**: contains the data for the different phase plots (full exploration of the [ε_l, ε_u] space) with `N=16384` and `m=100` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10)<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10)<br> - **`SL`** contains the data for Square lattice with first, second and third nearest neighbors (k=4, 8, 12)<br> - **`swipes.tar`** contains the data for the finite size effects study at fixed ε_l with `m=1000` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10) with ε_l = 0.05<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10) with ε_l = 0.05<br> - **`SL`** contains the data for Square lattice with third nearest neighbors (k=12) with ε_l =0.03<br> - the different videos referenced in the main text and the SM follow various naming schemes:<br> - **`scatter3D_el_eu_Smax_uni_{topology}_N=16384.mp4`**: 360° rotation of the 3D visualisation of the data leading the average phase plots.<br> - **`scatter2D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: evolution of the scatter plot leading the finite size study as a function of N.<br> - **`scatter3D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: same as before, but in 3D where the Z-axis is the extremism.<br> - **`scatter_x0_xt_{topology}_N={N}_{realization_type}.mp4`**: time evolution of the scatter plot of the opinion at time `t` versus initial opinion, color-coded with the extremism. {realization_type} can be mild, skewed or U-turn.<br> - **`traj_2D_SL_k=12_N=16384_{realization_type}.mp4`**: because of the spatial embedding, the time evolution of those realizations on the Square Lattice can be visualized in 2D.</p> <p># Python example for reading the format<br> An example script, which visualizes <S\> vs ε_u graph for the largest size of the ER case, with a function to read this format is given in `example.py`.</p>
Data from: Modelling dynamics in protein crystal structures by ensemble refinement
Open the record for dataset details and reuse information.
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The monthly NO3- concentrations across China during 2005-2015</p>
Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations
<p>This file is for the upload of data for 2019JD031057R.</p>
Corticostriatal cocaine-seeking ensembles are defined by differing gene expression from sucrose-seeking ensembles using a within-subject dual self-administration and seeking mouse model
GEO Series GSE247029. Mus musculus. 60 samples. Type: Expression profiling by high throughput sequencing.
Supporting data case study, ensemble climate-impact modelling
<p>Data supporting the case study in 'Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions', publication under review.</p>
Causal Modeling Using Network Ensemble Simulations Predicts Novel Lipid Metabolism Genes
GEO Series GSE15226. Mus musculus. 120 samples. Type: Expression profiling by array.
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.