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5,805 results for “Data model”
Model Data and Diagnostics used for the Age of Air Diagnostic Paper
<p>Model data and derived diagnostics used in the Age of Air paper, from Unified Model output. © Crown Copyright, Met Office</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis
<p>Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis.</p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
Data and Models of Thermal Density Currents Research in Daheiting Reservoir
<p><span>This database includes the field measurement results from cruise surveys, profile observations, and benthic observations in the Daheiting Reservoir, along with the retrospective model and the average-year model. These data and models are used to study the oxygenation benefits of thermal density currents and their regulation measures.</span></p>
Recorded temperature data of a reinforced concrete, 1.2-m long, beam-slab model and associated meteorological data.
<div><strong>The data contain internal and surface temperature measurements of a 1.2 m-long, reinforced concrete girder-slab element exposed to the environmental conditions of the city of Bucaramanga, Colombia (Coordinates: 7°08'35", 73°07'18"). In addition, meteorological variables of the environment near the element were recorded. The data was measured from July 7, 2023, to November 6, 2023.</strong></div> <div><strong>The data were obtained by:</strong></div> <ul> <li> <div><strong>Internal Temperature (file name: InternalTemperature): 40 thermocouples were embedded in the element (Fig. 1) to collect data every 30 minutes from 6:30 AM on July 10, 2023 to 11:30 PM on November 6, 2023. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li> <div><strong>Surface Temperature (file name: SurfaceTemperatureCameraEast and SurfaceTemperatureCameraWest): A FLIR E6 XT thermal camera was used to record Surface temperature of 10 points on the EAST (Fig. 2) and WEST (Fig. 3) faces of the element from 6:30 AM on June 29, 2023 to 4 PM on October 31, 2023. Twenty daily measurements were recorded every 30 minutes. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li> <div><strong>Surface Temperature (file name: SurfaceTemperatureDroneEast): A DJI MAVIC 2 ENTERPRISE ADVANCED (EU) drone equipped with a thermal imaging camera measured the surface temperature of 10 points on the EAST side of the element (Fig. 4) every 30 minutes, from 6:30 AM on September 9 to 4 PM on October 31. Temperatures are in degrees Celsius (°C).</strong></div> </li> <li><strong>Meteorological variables (file name: WeatherStationData): A DAVIS VANTAGE PRO-2 WLRS weather station recorded data every 30 minutes from 12 AM on July 10, 2023, to 12:30 PM on November 6, 2023. The data included the average, maximum, and minimum ambient temperature (°C); relative humidity (%); speed (m/s) and wind direction (cardinal direction); atmospheric pressure (mb); precipitation (mm) and average and maximum solar radiation (W/m<sup>2</sup>).</strong></li> </ul>
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Data from Deng et al. (2025) "Secular Change of Preservation of Lunar Sinuous Rilles: Constraints from Lava Flow Numerical Modeling"
<p>These are the updated supplemental materials of our article, adding more groups of simulation and a sensitivity analysis of two parameters, cell width and plane slope.</p>
Heron Island Satellite Imagery Classified Benthic Data and Halo Analyses and Models
<p>This dataset includes satellite imagery data from Heron Island, Australia downloaded from Google Earth Pro in 2023, with image from Maxar Technologies dated 2016 clipped to the shallow lagoon layer from the Allen Coral Atlas shape file classified into benthic categories: corals, algae and sand using a combination of unsupervised machine learning spectral classification and manual training and assignment of classes. This dataset also includes scoring of selected coral patch reefs for isolated halos across time using historical aerial imagery.</p> <p>We also include 2 notebooks with code used to generate figures and run analyses for data, geometric, and consumer-resource models for coral halo patterns supporting the work entitled, "Consumer-resource interactions reflected in coral halo patterns" by the authors listed. A knitted html for R Markdown file is also included.</p>
Data set of axle loads and distances for operating passenger and freight trains for development of load models
<p>Train data for moving load models of operating trains (5,007 passenger trains and 139,182 freight trains).</p> <ul> <li>axle distances [m] and axle loads [kN] for all trains included in zip-files as txt-files</li> <li>information on line categories (DIN EN 15528), loadcases and maximum speeds for passenger trains (and assignment to passenger train numbers in previous version of publication (used for development of load model)) in xlsx-file</li> </ul>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</p>
Digital Twin or Digital Model: An Analysis of Definitions along the Product Lifecycle - Research data
<p>This research data contains the statements of the authors Grieves, Stark and Tao with regard to selected characteristics of Digital Twins. According to these statements different case studies along the product life cycle are classified as Digital Twin or Digital Model.</p> <p>Version 2 added a change in characteristic 2.</p>
Data repository for study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate"
<p>Data for the study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate".</p> <p><strong>hindcasting_analysis</strong></p> <ul> <li>figures of the hindcasting exercise in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>baseline scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure and export restriction scenario -- <em>agrimate_baseline=2007-2009_export_restrictions=2007-2011_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> </ul> </li> </ul> </li> </ul> <p><strong>multibreadbasket_analysis</strong></p> <ul> <li>figures of the multibreadbasket analysis in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>simulations under historical climatic conditions with <number> as an identifier -- <em>agrimate_his-<number>.nc</em></li> <li>simulations under +2°C projection with <number> as an identifier -- <em>agrimate_2p0-<number>.nc</em></li> </ul> </li> </ul> </li> <li>processed_data <ul> <li>processed output data to easier/faster plot</li> </ul> </li> </ul> <p><strong>sensitivity_analysis</strong></p> <ul> <li>raw data and graphics as in <strong>main_output</strong> for different model parameters as given in Table F.1</li> </ul> <p> </p> <p> </p>
Shuttle Radar Topography Mission digital elevation models, data points, radiocarbon dates, and geochemical data for the Rub' al Khali Desert
Open the record for dataset details and reuse information.
Code, Data, and Technical Note for SCREAM Beijing flood Convection-Permitting Regionally Refined Model 1.0 version
<p><a href="https://zenodo.org/api/records/15126670/draft/files/BeijingRRM-v0.1-SCREAM_push.tar.gz/content" target="_blank" rel="noopener noreferrer">BeijingRRM-v0.1-SCREAM_push.tar.gz</a> :</p> <p>The code used to generate all simulations for the paper entitled "Through the lens of a kilometer-scale climate model: 2023 Jing-Jin-Ji flood under climate change" submitted to Geophysical Research Letters. The SCREAM Beijing RRM source code is also available on GitHub at https://github.com/E3SM-Project/scream/tree/jzhang/RRM_tmp (last access: 28 Aug 2024) and a maint branch (BeijingRRM-v0.1; https://github.com/jsbamboo/scream/releases/tag/BeijingRRM-v0.1, last access: 28 Aug 2024). </p> <p><a href="https://zenodo.org/api/records/15126670/draft/files/files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz/content" target="_blank" rel="noopener noreferrer">files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz</a> : </p> <p>The runscripts, mapping files, masks used for analysis and figures in the paper. The simulation outputs and processed data are too large (3.3T) to upload to zenodo, and are available on the NERSC portal: https://portal.nersc.gov/archive/home/z/zhang73/www/files_scream-BeijingRRM-v1.0_storylines</p> <p><a href="https://zenodo.org/uploads/15126670" target="_blank" rel="noopener noreferrer">BeijingFlood_Doc.pdf</a> :</p> <p>The technical note documenting our practice in generating the SCREAM Beijing flood RRM configurations. Source page: https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/4056318237/SCREAM+Beijing+Flood+RRM+Technical+Note</p>
Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.
<p><span><span><span>The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example:</span></span></span></p> <ul> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854).</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file </span></span></span><span><span><span>containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. </span></span></span><span><span><span> The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’</span></span></span><span><span><span>s ensemble mean</span></span></span><span><span><span>: one that bias-corrects the </span></span></span><em><span><span><span>x</span></span></span></em><span><span><span>-coordinate of the TC-center, another that bias-corrects the </span></span></span><em><span><span><span>y</span></span></span></em><span><span><span>-coordinate.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/</span></span></span><span><span><span>uncertainty_calibration</span></span></span><span><span><span>/</span></span></span><span><span><span>uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the </span></span></span><span><span><span><em>x</em></span></span></span><span><span><span>-coordinate, one for spread in the </span></span></span><span><span><span><em>y</em></span></span></span><span><span><span>-coordinate.</span></span></span></p> </li> </ul> <p><span> </span></p> <p><span><span><span>As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid rows) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid columns) x </span></span></span><span><span><span>9</span></span></span><span><span><span> (lag times) x 3 (wavelengths). Lag times should be in the following order: </span></span></span><span><span><span>240, 210, </span></span></span><span><span><span>180, 150, 120, 90, 60, 30, 0 min ago. Wavelengths should be in the order indicated by the subdirectory name. The numpy array itself should contain </span></span></span><em><span><span><span>normalized</span></span></span></em><span><span><span> brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a </span></span></span><em><span><span><span>plate carrée</span></span></span></em><span><span><span> grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper, </span></span></span><em><span><span><span>i.e.,</span></span></span></em><span><span><span> those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the </span></span></span><span><span><span>second-</span></span></span><span><span><span>most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper. See details below.</span></span></span></p> <p> </p> <p><span><span><span>Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-score units).</span></span></span></p>
Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
<p>This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD". </p>
Application Case 3: Data sets consisting of 3D scans, 3D model and the derived metadata, from two different software programs
<p>In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.</p> <p>The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.</p> <p>The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. <em>et al.</em> Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. <em>Herit Sci</em> <strong>9</strong>, 91 (2021). <a href="https://doi.org/10.1186/s40494-021-00561-w">https://doi.org/10.1186/s40494-021-00561-w</a></p> <p>Python scripts for exporting metadata can be found here: <a href="https://github.com/i3mainz/3dcap-md-gen/tree/0.1.3">GitHub - i3mainz/3dcap-md-gen</a></p>
Supplementary data: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer's disease
<p>Supplementary data for: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer’s disease. </p> <p>Supplementary_Table1_QC: Excel sheet containing QC metrics for the RNA-seq data and the other containing QC metrics for the ATAC-seq data. </p> <p>Supplementary_Table2_DEGs: Excel sheet containing DeSeq2 differential expression analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table3_MAGMA_geneset_analysis_res: CSV file containing MAGMA gene set analysis results using the differentially expressed genes (FDR < 0.05) for the comparisons outlined in Supplementary_Table2_DEGs and three independent AD GWAS. </p> <p>Supplementary_Table4_DARs: Excel sheet containing DeSeq2 differential accessibility analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table5_sLDSC_res.csv: CSV file containing s-LDSC results using the consensus set of ATAC-seq peaks with three brain disorder GWAS (Alzheimer's disease, autism spectrum disorder, and amyotrophic lateral sclerosis). </p> <p>Supplementary_Table6_WGCNA_clusterProfiler_pathway_enrichment.csv: CSV file containing pathway enrichment results using two WGCNA-identified modules that were significantly upregulated in APOE2-expressing microglia. </p> <p>Supplementary_Table7_homer_motifEnrichment_res.xlsx: Excel sheet containing Homer motif enrichment analysis results using top 100 peaks with increased and decreased chromatin accessibility for APOE2 vs APOE3, APOE4 vs APOE3, and APOE4 vs APOE2.</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.