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2,113 results for “High resolution”

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zenodo36/100

Surface drifters and high resolution global simulations mapping of internal tide surface energy

File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the  Global Drifter Program dataset.  File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg . File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg . File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg . File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg . File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg . For all files semidiurnal signal is obatined from band-pass filtering.

opencc-by-4.0Feb 2024View details →

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High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022

Description: This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics. Data Description: <ul> <li>Beach Transects: Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li>Temporal Coverage: The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>

opencc-by-4.0Apr 2024View details →

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Presentation in HEARING: High-resolution structural and functional EAR imaging 2023: Three-dimensional vibration of the human tympanic membrane using a scanning laser doppler vibrometer

This is for Bastian Baselt's poster presentation in HEARING: High-resolution structural and functional EAR imaging, Ascona, Switzerland in 2023, and the related data.

opencc-by-4.0Apr 2024View details →

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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.

opencc-by-4.0Feb 2020View details →

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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.

opencc-by-4.0Feb 2020View details →

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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.

opencc-by-4.0Mar 2014View details →

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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

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 104.

opencc-by-4.0Nov 2024View details →

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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> If you use this dataset in your research/work, please cite the following paper: Kawano, Ayako, et al. "Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality." Science Advances 11.4 (2025): eadq1071. <a href="https://doi.org/10.1126/sciadv.adq1071">DOI: 10.1126/sciadv.adq1071</a> Thank you for acknowledging our work! </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>Full model:</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>AOD model: </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 (R2 of 0.85 and RMSE of 18.48 μg/m3). 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>

opencc-by-4.0Mar 2024View details →

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An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning

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.  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. **Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.

opencc-by-4.0Sep 2024View details →

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Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia

<h2>Cite this article</h2> 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>   <h2>Data Description</h2> The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data <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: -1 is single rice cropping area;  -2 is double rice cropping area and -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>

opencc-by-4.0Feb 2024View details →

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FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

<h2>Overview</h2> 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.  Resolution: 30x30 arcsec Projection: EPSG 4326 Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°N Extent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°N Periods for historical data: 1961-1990 and 1991-2020 Periods for scenario data: 2036-2065 and 2071-2100 Format: GeoTIFF List of climatologies (climate indicators)   <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y) or monthly (M) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> 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 </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> 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 </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> 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 </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation </td> <td>   </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> </tbody> </table> <h2>Data sources</h2> 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.  <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>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</li> </ul> Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2): <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>

opencc-by-4.0Feb 2024View details →

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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.

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: <ul> <li> 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). </li> <li> 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. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. 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’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/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 x-coordinate, one for spread in the y-coordinate. </li> </ul>   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 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 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 normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., 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 second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   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().   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().   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 z-score units).

opencc-by-4.0Sep 2024View details →

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Surface surface wind speed and its different grades over China during 1961-2020 based on a high-resolution observation dataset CN05.1

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. References: Jia Wu, Xue-Jie Gao. A grided daily observation dataset over China region and comparison with the other datasets. Chinese J. Geophys. (in Chinese), 2013, 56(4): 1102-1111, doi: 10.6038/cjg20130406.

opencc-by-4.0Oct 2021View details →

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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

This data source contains scripts and data associated with the JGR Earth Surface publication “Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina” 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.

opencc-by-4.0Nov 2021View details →

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Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy

This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in: Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021 Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version) 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).

opencc-by-4.0Nov 2021View details →

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High-resolution and full coverage AOD downscaling based on the bagging model over the arid and semi-arid areas, NW China

High-resolution and full coverage 250 m monthly AOD product over the arid and semi-arid areas, NW China.  the scale factor is 1000.

opencc-by-4.0Nov 2021View details →

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Ultra-high-resolution diffusion MRI atlas of CD1 embryonic mouse brains at E10.5-E15.5.

This dataset is assciated with the paper "A Spatiotemporal Continuum of Embryonic Mouse Brain Development Built on Diffusion MR Microscopy for Probing Dynamic Gene-Neuroanatomy" on PNAS. For each stage, average FA (fractional anisotropy) and DEC (directionally encoded colormap) images (n=5) are provided.

opencc-by-4.0Dec 2021View details →

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Data for : "Three months of combined high resolution rainfall and wind data collected on a wind farm"

The data set corresponds the data presented in the data paper : “Three months of combined high resolution rainfall and wind data collected on a wind farm “ Earth System Science Data” (https://www.earth-system-science-data.net/). More details can be found in the Read_me_v1.txt file and in the paper.

opencc-by-4.0Dec 2021View details →

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High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent

This dataset contains global continental-scale carbon dioxide inversion results for four periods in 2015 with a spatial resolution of 0.01°.

opencc-by-4.0Jan 2022View details →

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Data for Klaes et al. High-resolution stalagmite stratigraphy supports the Late Holocene tephrochronology of southernmost Patagonia. Comms. Earth Environ. (2022)..

This data set comprises the LA-ICP-MS, ICP-MS, EPMA and NanoSIMS measurements as well as SEM imagery presented in the research paper 'High-resolution stalagmite stratigraphy supports the Late Holocene tephrochronology of southernmost Patagonia' by Klaes et al. The Th-U data of the revised chronology of stalagmite MA1 with the calculated correction values according to Budsky et al. (2016) are also included. In addition, stable isotope data from Schimpf et al. (2011) with the revised version of the age-depth model are given. For detailed information on the analyses (e.g., instruments used), the user is kindly referred to the methods section of the article.

opencc-by-4.0Jan 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record