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608 results for “ensembles”
Ensembles for: Computing, Analyzing, and Comparing the Radius of Gyration and Hydrodynamic Radius in Conformational Ensembles of Intrinsically Disordered Proteins
<p>Flexible Meccano and Campari conformational ensembles used in the publication "<em>Computing, Analyzing, and Comparing the Radius of Gyration and Hydrodynamic Radius in Conformational Ensembles of Intrinsically Disordered Proteins</em>" by Ahmed M.C., Crehuet R., Lindorff-Larsen K.. Paper available at: https://doi.org/10.1007/978-1-0716-0524-0_21</p>
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>
Ensemble filtering experiments with HYSPLIT
<p>This dataset contains ensembles of HYSPLIT dispersion model concentration outputs for 14 different case studies. The ensemble members have been filtered using VOLCAT satellite retrievals obtained for the 14 case studies (<a href="http://doi.org/10.5281/zenodo.3579613">http://doi.org/10.5281/zenodo.3579613</a>). </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>
Genoa Flooding | 9 Oct 2014 | Meso-NH, MOLOCH Ensemble Reforecasts
<p>Genoa Flooding | 9 October 2014</p> <p>Ensemble-based reforecasts using the Meso-NH and MOLOCH models</p> <p>Variable: accumulated precipitation (mm/1-hour)</p> <p>Starting dates: 2014/10/07 00 UTC, 2014/10/07 12 UTC, 2014/10/08 00 UTC, 2014/10/08 12 UTC</p> <p>Ending date: 2014/10/10 00 UTC</p> <p>Acknowledgment is made for the use of ECMWF’s computing and archive facilities within the framework of the SPITCAPE Special Projects during the years 2016-2018 and 2019-2021.</p>
customized gtf file from Ensembl version 99 mm10
<p>The gtf from Ensembl version 99 (mm10) 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.</p>
The GRIMs ensemble dataset for "A critical role of the North Pacific bomb cyclones in the onset of the 2021 sudden stratospheric warming"
<p>The numerical model, the Global/Regional Integrated Model system (GRIMs) ensemble results which are initialized at 0000 UTC December 26, 2020, with JRA-55 reanalysis. CTL and PVinv_ANOM in the file name mean the control and sensitivity (North Pacific cyclone-removed initial condition) experiments, respectively.</p>
Ensemble flood map spatial verification
<p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations, data and code.</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for ensemble flood map spatial spread-skill evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>Helen Hooker. (2022). Ensemble flood map spatial verification (v1.0) [Data set]. Zenodo. https://10.5281/zenodo.6603101</p> <p>Related publications:</p> <p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations; 2023; NHESS; Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images
<p>Data for the Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images paper</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>
LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour.
<p>LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour. </p>
Improving Histopathological Image Classification with Patch-based Embeddings and Ensemble Learning: A Study on Enteroscope Biopsy Images
<p>This dataset contains the application codes developed for Enteroscope Biopsy Histopathological images and the images divided into patches in the dataset.</p>
RCubed WP3 empirical downscaling results, common ensemble
<p>NetCDF4 files containing the seasonal output of empirically downscaled GCM data under the RCP8.5 trajectory, has been produced in the Norwegian Research Council Project RCubed, work package 3, using a hybrid dynamical-statistical downscling approach. The data covers most of South Norway, and is available in a lat-lon projection with a resolution of 0.1° in longitude and 0.05° in latitude. The data is produced using the R software package <a href="https://github.com/metno/esd/wiki">esd</a>. The downscaled variables are seasonal mean 2-meter temperature (T2), seasonal wet-day mean (mu - the precipitation intensity on rainy (>1 mm) days), and seasonal wet-day frequency (fw - the frequency of days where precipitation is 1 mm or more). The data here is the common ensemble, that is GCM model runs which were used for downscaling T2, mu, and fw. From mu and fw seasonal mean precipitation (excluding days where precipitation is less than 1 mm) is derived. The data is also available from MET Norway's thredds server with <em>OPeNDAP</em> <a href="http://thredds.met.no/thredds/catalog/metusers/helenebe/catalog.html">http://thredds.met.no/thredds/catalog/metusers/helenebe/catalog.html</a></p> <p>The data is downscaled using ouput from a dynamical downscaling (see Pontoppidan, 2018) as reference data. This means that the data may contain biases during historical time. The precipitation fields inter-annual variation is not fully captured at the fine grid scale for which the data is produced, so we recommend applying some upscaling/areal averaging if the inter-annual correlation is of importance. The long-term trend is likely captured for the fields at the resolution of the data. An article describing the work behind the data is underway, and will be added here once published.</p> <p>Pontoppidan, M., Kolstad, E. W., Sobolowski, S., & King, M. P. ( 2018). Improving the reliability and added value of dynamical downscaling via correction of large‐scale errors: A Norwegian perspective. <em>Journal of Geophysical Research: Atmospheres</em>, 123, 11,875– 11,888. <a href="https://doi.org/10.1029/2018JD028372">https://doi.org/10.1029/2018JD028372</a></p> <p> </p> <p>The GCM runs downscaled:</p> <p>"1" "ACCESS13_r1i1p1"<br> "2" "bcccsm11_r1i1p1"<br> "3" "CanESM2_r1i1p1"<br> "4" "CanESM2_r2i1p1"<br> "5" "CanESM2_r3i1p1"<br> "6" "CanESM2_r4i1p1"<br> "7" "CanESM2_r5i1p1"<br> "8" "CCSM4_r1i1p1"<br> "9" "CCSM4_r2i1p1"<br> "10" "CCSM4_r3i1p1"<br> "11" "CCSM4_r4i1p1"<br> "12" "CCSM4_r5i1p1"<br> "13" "CCSM4_r6i1p1"<br> "14" "CESM1BGC_r1i1p1"<br> "15" "CESM1CAM5_r1i1p1"<br> "16" "CNRMCM5_r10i1p1"<br> "17" "CNRMCM5_r1i1p1"<br> "18" "CNRMCM5_r2i1p1"<br> "19" "CNRMCM5_r4i1p1"<br> "20" "CNRMCM5_r6i1p1"<br> "21" "CSIROMk360_r10i1p1"<br> "22" "CSIROMk360_r1i1p1"<br> "23" "CSIROMk360_r3i1p1"<br> "24" "CSIROMk360_r5i1p1"<br> "25" "CSIROMk360_r6i1p1"<br> "26" "CSIROMk360_r8i1p1"<br> "27" "GFDLCM3_r1i1p1"<br> "28" "GFDLESM2M_r1i1p1"<br> "29" "GISSE2H_r1i1p1"<br> "30" "GISSE2H_r1i1p2"<br> "31" "GISSE2H_r1i1p3"<br> "32" "GISSE2R_r1i1p1"<br> "33" "GISSE2R_r1i1p2"<br> "34" "GISSE2R_r1i1p3"<br> "35" "HadGEM2CC_r1i1p1"<br> "36" "HadGEM2ES_r1i1p1"<br> "37" "HadGEM2ES_r3i1p1"<br> "38" "HadGEM2ES_r4i1p1"<br> "39" "inmcm4_r1i1p1"<br> "40" "IPSLCM5ALR_r1i1p1"<br> "41" "IPSLCM5ALR_r2i1p1"<br> "42" "IPSLCM5ALR_r3i1p1"<br> "43" "IPSLCM5ALR_r4i1p1"<br> "44" "IPSLCM5AMR_r1i1p1"<br> "45" "IPSLCM5BLR_r1i1p1"<br> "46" "MIROC5_r1i1p1"<br> "47" "MIROC5_r2i1p1"<br> "48" "MIROC5_r3i1p1"<br> "49" "MIROCESM_r1i1p1"<br> "50" "MIROCESMCHEM_r1i1p1"<br> "51" "MRICGCM3_r1i1p1"<br> "52" "NorESM1M_r1i1p1"<br> "53" "NorESM1ME_r1i1p1"</p>
Data for "Improving sampling of crystallographic disorder in Ensemble Refinement"
<p>Program outputs and data for "Improving sampling of crystallographic disorder in Ensemble Refinement"</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>
Raw data set for "Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning"
<p>Phase field generated Raw data set for "Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning"</p>
Dataset for paper "Ensemble transfer learning approach for gastric cancer prediction in electronic health records"
<p>This is datasets and codes for research on "Ensemble transfer learning approach for gastric cancer prediction in electronic health records"</p>
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.