Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,155
datasets available to search
ShareScore release 0.9.0
Dataset results
5,155 results for “resolution”
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Figure 16 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 16. Succession of ammonoid genera in the Aras Valley section.
Figure 14 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 14. Mass occurrence of ostracod specimens in sample AV171 (+1.71 m). Scale bar units = 1 mm.
Figure 2 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 2. The Permian–Triassic boundary sections in the Ali Bashi Mountains, NW Iran.
All-sky daily max ambient air temperature datasets at 1-km resolution from 2003-2012 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmax from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>).</li> </ul>
Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"
<p>Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".</p>
Thermal coupling mode in mantle-outer core convection predicted from an ultra-high-resolution numerical simulation of two-layer convection with a large viscosity contrast
<p>Movie of temperature and velocity fields in the highly viscous layer (HVL) and the low-viscosity layer (LVL) (left panels) and the close-up views focusing on the interior of the LVL (right panels). The viscosity contrast between the HVL and LVL is 10<sup>4</sup>.</p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Canopy Height Models of Giant Mountains National Park (2012 and 2022) at 10 m resolution
<p>This repository was created to provide datasets related with an article: "Harmonised airborne laser scanning products can address the limitations of large-scale spaceborne vegetation mapping". The original airborne laser scanning point clouds used for the generation of the canopy height models were kindly provided by Giant Mountains National Park.</p>
High-Quality Daily PM2.5 Datasets for India at 10 km Resolution (Version 2)
<div> <div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>If you use this dataset in your research/work, please cite the following paper:</p> <p><strong>Kawano, Ayako, et al. "Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality." <em>Science Advances</em> 11.4 (2025): eadq1071. <a href="https://doi.org/10.1126/sciadv.adq1071">DOI: 10.1126/sciadv.adq1071</a></strong></p> <p>Thank you for acknowledging our work!</p> </div> </div> </div> </div> </div> </div> </div> <div>----------------------------------------------</div> <div> </div> <div>Open-source daily fine particulate matter (PM2.5) datasets at a 10 km resolution for India from 2005 to 2023, using a region-specific two-stage machine learning model carefully validated on held-out monitor data that it was not trained on. Our model demonstrates robust out-of-sample performance, substantially outperforming existing publicly-available monthly PM2.5 datasets.</div> <div> </div> <div>To take advantage of both the longer available time series of Aerosol Optical Depth (AOD) data and information from newer sensors such as TROPOspheric Monitoring Instrument (TROPOMI), we developed two separate machine learning models - the "Full model" and the "AOD model".</div> <div> </div> <div><strong>Full model:</strong></div> <div> <ul> <li>Predictive performance (spatial cross-validation): R2 value of 0.67, RMSE of 27.79 μg/m3</li> <li>Input features: Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and TROPOMI satellite inputs along with other remote sensing data</li> <li>Daily PM2.5 predictions for: July 10, 2018 - September 30, 2023</li> </ul> </div> <div><strong>AOD model:</strong> </div> <div> <div> <ul> <li>Predictive performance (spatial cross-validation): R2 value of 0.64, RMSE of 32.08 μg/m3</li> <li>Input features: all inputs except TROPOMI used for the Full model</li> <li>Daily PM2.5 predictions for: January 1, 2005 - September 30, 2023</li> </ul> </div> </div> <div> </div> <div>Please note that we employed spatial cross-validation (CV) rather than more conventional random CV to be responsible for predicting daily PM2.5 concentrations for locations without air quality monitors across India. When the above Full model was evaluated using 10-fold random CV, it showed notably higher performance (<strong>R2 of 0.85 and RMSE of 18.48 μg/m3</strong>). This highlights the potential of random CV to overstate model performance on critical real-world applications.</div> <div> </div> <div>Code and source data needed to replicate the results have been also deposited. </div>
An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning
<p>This dataset contains two improved surface pCO2 products, along with surface pCO2 from the INCOIS-BIO-ROMS model (pCO2_model) and other input variables. It is a long-term, high-resolution dataset developed for the Indian Ocean region (30°E - 120°E, 30°S - 30°N), covering the period from 1980 to 2019. The dataset features a monthly temporal resolution and a spatial resolution of 1/12 degree.</p> <p> The file includes INCOIS-BIO-ROMS model outputs (sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), nitrate (NO3), dissolved inorganic carbon (DIC), and chlorophyll-a (CHL)). These variables are used as inputs for machine learning models to improve the pCO2_model. The machine learning model predicts the surface pCO2 deviants (pCO2_obs - pCO2_model). The file also provides spatiotemporally varying uncertainties associated with the predicted pCO2 deviants.</p> <p><strong>**Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.</strong></p>
Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia
<h2>Cite this article</h2> <p>Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). <a href="https://www.nature.com/articles/s41597-025-05722-1">https://doi.org/10.1038/s41597-025-05722-1</a></p> <p> </p> <h2>Data Description</h2> <p>The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data</p> <ul> <li>The data file is in “.tif" format</li> <li>Spatial extent: Southeast Asia</li> <li>Pixel size: 10 m</li> <li>Projection information: EPSG: 4326</li> <li>CropType: Paddy rice.</li> <li>Year: Values from 2021</li> <li>Raster class:<br>-1 is single rice cropping area; <br>-2 is double rice cropping area and<br>-3 is triple rice cropping area</li> <li>The data also can be viewed on the GEE App (<a href="https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10">https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10</a>) and the Climate TRACE platform (<a href="https://climatetrace.org/">https://climatetrace.org/</a>) </li> <li>Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)</li> </ul> <p> </p> <p> </p>
Data from: Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy
<p>This repository contains the raw data of the experimental ISM dataset used to make the figures and supplementary figures for the paper entitled <em>Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy.<br></em></p>
FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution
<h2>Overview</h2> <p>This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. </p> <p><strong>Resolution</strong>: 30x30 arcsec<br><strong>Projection</strong>: EPSG 4326<br><strong>Extent for historical data</strong>: 10.67°W – 47.67°E, 33.68°N – 71.33°N<br><strong>Extent for scenario data</strong>: 10.67°W – <em>39.33°E</em>, 33.68°N – 71.33°N<br><strong>Periods for historical data</strong>: 1961-1990 and 1991-2020<br><strong>Periods for scenario data</strong>: 2036-2065 and 2071-2100<br><strong>Format:</strong> GeoTIFF</p> <p><strong>List of climatologies (climate indicators) </strong> </p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y) or monthly (M)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation</p> </td> <td> <p> </p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> </tbody> </table> <h2>Data sources</h2> <p>The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. </p> <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li><em>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</em></li> </ul> <p><strong>Climate models from EURO-CORDEX </strong>(doi.org/10.1007/s10113-013-0499-2<strong>)</strong>:</p> <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>
Dataset for Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR
<p>This dataset folder contains the DIV2K public dataset, which is utilized for model training and comprises 900 high-quality, high-resolution images along with their corresponding low-resolution versions. Additionally, all pre-trained models used in the experiment and their associated test results are publicly available.</p> <p>The main directory is organized into two subfolders: one labeled "dataset," which houses the DIV2K dataset, and another named "Model_results," which contains the pre-trained models and their corresponding test outcomes. The Dataset folder includes the original DIV2K dataset (referred to as "DIV2K") as well as a channel-expanded dataset processed by the RCNN model (designated as "DIV2K-RCNN"). Within the Model_results folder, the Model_trained subfolder contains all pre-trained models employed during the experiment, while the Test_results subfolder holds the test results for each model.</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>
Surface surface wind speed and its different grades over China during 1961-2020 based on a high-resolution observation dataset CN05.1
<p>The daily 10-m wind speed observation dataset CN05.1, which covers the period from 1961 to 2020, with horizontal resolution of 0.25° × 0.25° (latitude × longitude). This dataset was developed by Jia Wu from National Climate Center, China Meteorological Administration.</p> <p>References: Jia Wu, Xue-Jie Gao. A grided daily observation dataset over China region and comparison with the other datasets. <em>Chinese J. Geophys. </em>(in Chinese), 2013, 56(4): 1102-1111, doi: 10.6038/cjg20130406.</p>
Resolution, validation and divergence of heterozygous haplotypes from pooled long read sequencing of the diamondback moth (Lepidoptera: Plutellidae)
<p>This data sets includes the full set of intermediate genome assemblies produced during our analyses. We make these available for researchers who may be interested in the variation between assembly results and variation within the study organism (<em>Plutella xylostella</em>) prior to removal during subsequent genome processing.</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
<p>This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in:</p> <p>Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021<br> Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version)</p> <p>Files include the retrieval emulator parameters, atmospheric profiles used in the emulator development, column-mean water vapour and cloud water both for the total column water vapour (TCWV) and "effective" TCWV, which accounts for the water vapour integrated along the direct solar path at a range of solar zenith angles, see Richardson 2021b, Eq. (7).</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.